CN110909904A - Terminal user load prediction system based on wireless interconnection and data mining technology - Google Patents
Terminal user load prediction system based on wireless interconnection and data mining technology Download PDFInfo
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Abstract
A terminal user load prediction system based on wireless interconnection and data mining technology comprises the following components: the system comprises a data warehouse (1), a data synchronization module (2) and a load prediction and room temperature control module (3); the latter two are respectively connected with a data warehouse (1); the load prediction and room temperature control module (3) is connected with a data mining unit (1.4); the load prediction and room temperature control module (3) converts the load prediction data generated by the data mining unit (1.4) into a production scheduling system business module, a management module, an analysis module and other production management curves which can be directly applied. The invention achieves the aim of maximizing energy conservation on the premise of ensuring the comfort of users by a heating system.
Description
The technical field is as follows:
the invention relates to scientific technologies such as heating ventilation, sensors, wireless communication, automation and computers, and particularly provides a terminal user load prediction system based on wireless interconnection and data mining technologies.
Background art:
in the prior art, there are many indoor temperature acquisition technologies including telephone line temperature measurement, DTU/GPRS temperature measurement, and wireless temperature measurement, but the acquired indoor temperature of the user is only displayed in the form of report, bar graph, and curve, and is not directly applied to load prediction and production guidance.
Therefore, it is desirable to obtain an end-user load prediction system based on wireless interconnection and data mining techniques.
The invention content is as follows:
the invention aims to provide a terminal user load prediction system based on wireless interconnection and data mining technology with excellent technical effect.
The invention discloses a terminal user load prediction system based on wireless interconnection and data mining technology, which comprises the following components: the system comprises a data warehouse 1, a data synchronization module 2 and a load prediction and room temperature control module 3; wherein: the data synchronization module 2 and the load prediction and room temperature control module 3 are respectively connected with the data warehouse 1;
the data warehouse 1 is specifically composed of the following parts: the system comprises a data conversion unit 1.1, a dimension modeling unit 1.2, an online analysis unit 1.3 and a data mining unit 1.4; wherein:
the data conversion unit 1.1 is connected with the data synchronization module 2, and the dimension modeling unit 1.2 is connected with the data conversion unit 1.1; the online analysis unit 1.3 is connected with the dimension modeling unit 1.2 and the data synchronization module 2; the data mining unit 1.4 is connected with the dimension modeling unit 1.2, the online analysis unit 1.3 and the data synchronization module 2;
the data conversion unit 1.1 synchronizes the outdoor daily average temperature t obtained by the module 2wAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；Q’＝[Q1,Q2,……,Qi,……,QN]；
the dimension modeling unit 1.2 generates (T) from the two one-dimensional arrays generated by the data conversion unit 1.1wi，Q’i) Two-dimensional coordinate sequences, i.e. scatter distribution maps, using least-squares to assemble raw data points for heat Q and outdoor temperature TwFitting to a first order curve relationship:
Q’＝kTW+b
the online analysis unit 1.3 is used for obtaining a first order curve relation of the dimensionality modeling unit 1.2: q' ═ kTW+ b and data synchronization module 2 samplingCorrecting the collected indoor temperature data, and obtaining a corrected load curve aiming at the house maintenance structure of the sampling community, namely:
the data mining unit 1.4 obtains a load prediction formula Q according to the dimension modeling unit 1.2 and the online analysis unit 1.3P＝ATW+ B, future 24-hour meteorological data captured by the data synchronization module 2, predicting future 24-hour thermal load, and making a corresponding room temperature control strategy for production and operation managers to use;
the data synchronization module 2 is specifically composed of the following parts: the system comprises a wireless room temperature acquisition module 2.1, an air temperature acquisition module 2.2, a wind power acquisition module 2.3, a humidity acquisition module 2.4, a heat consumption data query module 2.5 and an auxiliary unit 2.6; wherein: the wireless room temperature acquisition module 2.1 acquires the indoor temperature of a typical user through a room temperature acquisition device which is installed in a user room and is based on a wireless interconnection technology, uploads the indoor temperature to a database, and is used as a correction parameter of the online analysis unit 1.3 in the next heating season and the data of the implementation control target value in the heating season for intelligent analysis;
the air temperature acquisition module 2.2, the wind power acquisition module 2.3 and the humidity acquisition module 2.4 capture meteorological forecast data of a meteorological website for 24 hours in the future as state input parameters of the data mining unit 1.4 and simultaneously serve as input parameters of the dimension modeling unit 1.2 in the next heating season;
the heat consumption data query module 2.5 queries the synchronous data corresponding to the outdoor temperature and the indoor temperature in the database through the SQL data search engine as the input parameters of the dimension modeling unit 1.2.
The load forecasting and room temperature control module 3 is connected with a data mining unit 1.4; the load forecasting and room temperature control module 3 converts the load forecasting data generated by the data mining unit 1.4 into various production management curves which can be directly applied to a production scheduling system service module, a management module, an analysis module and the like, such as a boiler DCS, a heat supply network supervisory control and data acquisition (SCADA) and a factory Management Information System (MIS), such as a 24-hour load forecasting curve of a boiler and each heating power station, a supply and return water temperature climate compensation curve and the like.
The data warehouse 1 comprises a data conversion unit 1.1, a dimension modeling unit 1.2, an online analysis unit 1.3 and a data mining unit 14, and the data conversion unit, the dimension modeling unit 1.2, the online analysis unit 1.3 and the data mining unit 14 generate load prediction and production guidance data of meteorological data captured by the corresponding synchronization module 2 in the future 24 hours according to historical data (mainly the last heating season) acquired by each component in the data synchronization module 2 and a data modeling and mining technology specific to the data warehouse 1.
The data synchronization module 2 captures meteorological data matched with a meteorological website and an indoor temperature acquisition time domain by using a website data capture method, and performs subsequent processing; the concrete requirements are as follows:
the website data capturing method specifically comprises the steps of capturing meteorological data of a meteorological website matched with an indoor temperature acquisition time domain by using EXCEL imported webpage data and a VBA programming technology, storing the meteorological data into an ACCESS database to serve as attribute parameters for historical data mining of an indoor temperature load meteorological model, and capturing weather forecast data of the meteorological website in the future 24 hours to serve as state parameters of the load meteorological model when calculation control is output (see figure 2).
One or the combination of a wireless room temperature acquisition module 2.1, an air temperature acquisition module 2.2, a wind power acquisition module 2.3, a humidity acquisition module 2.4 and an energy consumption data query module 2.5 in the data synchronization module 2 adopts the following wireless data acquisition and network transmission technology for processing, and the specific requirements are as follows:
in engineering practice, indoor temperature collection points are selected according to 5% of the number of users of a certain building, and the collection points cover typical users such as cold mountains, top floors, middle floors and the like; the wireless sensor is directly installed in a user room, a data receiver is installed in a pipeline well near a corresponding acquisition point, a concentrator is arranged in each unit, and the data is uploaded to a data center or a cloud platform server through GPRS;
the wireless sensor comprises a wireless room temperature acquisition module 2.1, the wireless room temperature acquisition module 2.1 is a basic unit of the network, and the stable operation of the wireless room temperature acquisition module is a basic guarantee for the reliability of the whole network; the wireless room temperature acquisition module 2.1 consists of a sensor module 2.1a, a processor module 2.1b, a wireless communication module 2.1c and an energy supply module 2.1 d; as shown in fig. 3, wherein: the sensor module 2.1a, the processor module 2.1b and the wireless communication module 2.1c are respectively connected with the energy supply module 2.1 d; the sensor module 2.1b and the wireless communication module 2.1c are respectively connected with the processor module 2.1 b; specifically, the method comprises the following steps: the sensor module 2.1a is in turn made up of a sensor unit 2.1a1 and an AC/DC unit 2.1a2 connected to each other; the processor module 2.1b is composed of two parts, namely a processor 2.1b1 and a memory 2.1b 2; the wireless communication module 2.1c is composed of a network 2.1c1, a MAC2.1c2 and a transceiver 2.1c 3;
the technical parameter requirements of the wireless temperature sensor are as follows: measuring the temperature: -40 ℃ to +150 ℃, transmission distance (around the wall): 50m, measurement accuracy: +/-0.5 ℃, temperature measurement resolution: 0.1 ℃, frequency range: 433MHz (application-free), the whole machine sleep current is less than 2 muA, the maximum transmitting power: 10dbm, working voltage: 2.7 to 3.6V, external dimension: 40 × 30 × 25mm, design life: 10 years;
the sensor module 2.1a is responsible for collecting information and converting data in a detection area, the processor module 2.1b is responsible for controlling the operation of the whole sensor node, storing and processing data collected by the processor module and data sent by other nodes, the wireless communication module 2.1c is responsible for carrying out wireless communication with other sensor nodes, exchanging control information and receiving and sending collected data, and the energy supply module provides energy required by the operation of the nodes for the sensor nodes.
The data mining unit 1.4 also meets the following requirements: fig. 4 is a distribution diagram of the average outdoor temperature and the heat load in the heating season of a certain heating company, and as can be seen from fig. 4, the distribution range of the data points is large and dispersed, which indicates that the data points include data points of unreasonable heat supply, i.e., excessive heat supply and insufficient heat supply, and therefore the data are screened; according to the method, under the condition that the building is fixed and the indoor temperature is constant, the magnitude of the heat load is only related to the outdoor temperature and has a linear relation;
wherein Q is the building thermal load, W; q. q.svIs an index of heat accumulation of a building, W/m3·℃；qFIs an area heat index of a building, W/m2)(ii) a V is the volume of the enclosure structure, m3(ii) a F is the area of the enclosure structure, m2(ii) a H is the height of the building, m; t is tnWinter indoor temperature, deg.C; t is twIs the outdoor temperature in winter, DEG C.
The data screening steps are as follows:
step 1) sorting out a data set of historical heat supply operation data;
assuming that N heat supply working condition data points are collected in total, the average outdoor daily temperature twAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；
Q′＝[Q1，Q2,……,Qi,……,QN]
wherein, i is 1,2,3, … … N.i is ordinal in chronological order, here, the number of days for starting heating;
step 2) fitting the relation between the heat supply load and the outdoor temperature by using a primary curve;
fitting the heat supply Q and the outdoor temperature tw of the original data point set into a primary curve relationship by using a least square method:
Q'＝ktw+b (1-3)
wherein Q' is a fitted curve heat supply load, MWH or GJ, calculated amount; t is twCalculating the average outdoor daily temperature in winter at DEG C, and inputting the calculated value; k is a first-order coefficient of a curve obtained by fitting, and the calculated amount is calculated; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is calculated;
the relationship between the fitted curve and the raw data is shown in fig. 5;
using fitted curve equation (1-3) and outdoor temperature twThe heat supply amount on the fitting curve can be solved by the data point set of (1):
Qi’＝ktw,i+b (1-4)
wherein: 1,2,3 … … N; thus obtaining the product outdoorsA set of heat supply data points on a fitted curve corresponding to daily average temperature points: q ' ([ Q ') '1，Q′2，…，Q′i，…，Q′N]
Step 3) calculating the deviation between the actual heat supply load and the corresponding point on the fitting curve, and taking the absolute value of the deviation;
first the deviation between the actual heating load and the load on the fitted curve is calculated, ei＝Qi-Q′i
In the formula: qiTesting the MWH or GJ for the measured data; q'iCalculating the amount MWH or GJ for the heat supply on the fitted curve;
a set of deviations is obtained:
e＝e1,e2,…,ei,…,eN
wherein: when i is 1,2,3, … … N, the actual value and the absolute value of the deviation are shown in fig. 6 and fig. 7;
step 4) sorting the deviation absolute values of all the working condition points, and removing 10% of the original data points according to the sequence of the deviation absolute values from large to small to obtain a new data point set, as shown in FIG. 8;
and 5) repeating the processes from the step 1) to the step 4) twice, wherein the number of the obtained final data points is 70% of the number of the original data points.
In the system for predicting the load of the terminal user based on the wireless interconnection and data mining technology, the data warehouse 1 further meets the following requirements: fitting a primary curve of the heat supply quantity and the outdoor temperature by using the data point set subjected to screening and data mining as a load forecasting curve; see fig. 13;
the resulting fitted linear equation through the screened data is:
QP＝Atw+B (1-5)
wherein Q ispCalculating the amount for predicting the load value, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; and B is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained.
The load forecast curve obtained after data screening is more reasonable than the original curve obtained by direct fitting; in order to make the load forecasting curve adapt to the change of the actual heat supply condition, the load forecasting curve is corrected:
correcting a load forecasting curve according to a heat supply area: at present, a large number of new buildings are put into use every year in China, so the actual heat supply area of a heating power company changes every year. The load forecast curve mined in the previous year is used for directly guiding the heat supply operation in the current year, and the load forecast curve is unreasonable and needs to be corrected according to the actual heat supply area; the corrected load prediction curve is shown in the formula (1-7).
Wherein Q ispmxCalculating the amount of predicted load value after area correction, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained; f0Total area of heat supply for the last heating season, m2Calculating a value from the input; f1Total area of heat supply for the current heating season, m2Calculating a value from the input;
2) correcting a load forecasting curve according to the room temperature: the room temperature fed back by the actual user is greatly different from the set room temperature, which indicates that the actual heat supply amount deviates from the heat supply amount; then correcting according to the formula (1-8);
wherein Q isPtxCalculating a value for the predicted heat supply after temperature correction, MWH or GJ; qpmxCalculating a heat supply value after area correction in a statistic time period, namely an MWH or GJ value; t is twpThe average outdoor temperature in winter in the statistical period, DEG C, and the measured value; t is tnsCalculating the indoor temperature set value, DEG C; t is tnpThe average temperature value in the statistical period, DEG C, measured value; t is tn,iIndoor temperature value, DEG C, measured value at the ith moment;
the indoor average temperature at the first N moments is measured at the temperature of DEG C;
when the heat supply amount is initially predicted, the statistical period is a time period corresponding to the existing data, and is generally the data of the previous year.
The terminal user load prediction system based on the wireless interconnection and data mining technology is also provided with a centralized heating intelligent remote valve temperature control device; it comprises the following components: heat gauge 11, intelligent remote valve 12, collector 13, control by temperature change panel 14, measurement control by temperature change integration management platform 15, wherein: the heat meter 11, the intelligent remote valve 12 and the temperature control panel 14 are connected with the collector 13 through an MBUS communication bus, the collector 13 is connected with the integrated metering and temperature control management platform 15 through a wired or wireless connection structure, and the intelligent remote valve 12 is connected with the temperature control panel 14 through wireless communication;
the energy-type remote valve 12 comprises the following two parts which are connected with each other: an electric actuator 12.1 and a valve body 12.2; an electric actuator 12.1 in the intelligent remote valve 12 is connected with a room temperature controller through a wireless connection module; the following requirements are also specifically met:
① the electric actuator 12.1 in the intelligent remote valve 12 is connected with the temperature control panel 14 through wireless communication;
② the electric actuator 12.1 of the intelligent remote valve 12 is connected with the temperature control panel 14 and the valve body 12.2, so as to receive the room temperature information and the set temperature information and control the switch of the valve body 12.2 of the intelligent remote valve 12;
③ recording the on-off time of the intelligent remote valve 12, timing with the temperature control panel 14;
④ the electric actuator 12.1 of the intelligent remote valve 12 communicates with the temperature control panel 14 once every certain period, and the real-time communication is carried out after the set temperature is adjusted to execute the temperature setting instruction;
the intelligent remote valve 12 is connected with the collector 13 through a remote meter reading system M-Bus;
the measurement and temperature control integrated management platform 15 and the collector 13 are connected into a whole through a general packet radio service system GPRS; the intelligent remote valve 12 is composed as follows: electric actuator 12.1, V type ball valve 12.2, PT1000 temperature sensor 12.3 wherein: the electric actuator 12.1 is connected with a control command driving V-shaped ball valve 12.2 through a temperature control panel 14 or a collector 13 respectively; the PT1000 temperature sensor 12.3 is respectively connected with a V-shaped ball valve 12.2 and an electric actuator 12.1; the actuator 12.1 is a component adopting a main gear lengthening shaft; the V-shaped ball valve 12.2 is a structural member adopting a V-shaped valve clack.
Electric actuator 12.1 and valve body 12.2 constitute electric valve jointly, and electric valve's concrete structural component is: the device comprises an upper shell 5.1, a lower shell 5.2, a circuit board 5.3, a sealing ring 5.4, a battery 5.5, a battery cover 5.5a, a motor 5.6 and a V-shaped ball valve 12.2; wherein: a circuit board 5.3, a battery 5.5, a battery cover 5.5a and a motor 5.6 are arranged in a cavity formed by buckling the upper shell 5.1 and the lower shell 5.2; the circuit board 5.3 contacts the upper shell 5.1 or/and the lower shell 5.2 through a sealing ring 5.4; the battery 5.5 is fixed on the battery cover 5.5 a;
the upper shell 5.1 and the lower shell 5.2, the circuit board 5.3, the battery cover 5.5a and the motor 5.6 are fixedly connected with the upper shell 5.1 or/and the lower shell 5.2 through a threaded connecting piece 5.8; the part of the V-shaped ball valve 12.2 extends into the cavity between the upper shell 5.1 and the lower shell 5.2 and is connected with the motor 5.6; the circuit board 5.3 is respectively connected with a battery 5.5 and a motor 5.6; the outer side of the upper shell 5.1, which is far away from the V-shaped ball valve 12.2, is also provided with a surface paste 5.7; a sealing ring 5.4 with corresponding shape and size is also arranged between the V-shaped ball valve 12.2 and the battery 5.5 or/and the battery cover 5.5 a.
The V-ball valve 12.2 is constructed as follows: the valve comprises a valve body 12.2a, a valve rod 12.2b, a V-shaped ball valve core 12.2c, a valve cap 12.2d, a sealing ring 12.2e and a tetrafluoro gasket 12.2f for anticorrosion sealing; wherein: the valve body 12.2a is a tubular structural part, and a T-shaped pipe joint perpendicular to the axis of the main body part is also arranged on the valve body; the valve stem 12.2b is arranged in a branch line of the "T" -shaped pipe joint; the V-shaped ball valve core 12.2c is arranged in the inner cavity of the valve body 12.2a, and the V-shaped ball valve core 12.2c and the valve body 12.2a are mutually embedded or fixedly connected; a bonnet 12.2d is fixedly disposed at one tubular end of the body portion of the valve body 12.2 a; the valve rod 12.2b is also sleeved with a sealing ring 12.2e which is matched with a branch pipeline of a T-shaped pipe joint in the valve body 12.2 a; the V-shaped ball valve core 12.2c is arranged at the front side and the rear side of a main pipeline in the valve body 12.2a and is respectively provided with a polytetrafluoroethylene gasket 12.2f for corrosion prevention and sealing.
When the terminal user load prediction system based on the wireless interconnection and data mining technology uses a centralized heating intelligent remote valve temperature control device, an adjusting algorithm meeting the following requirements is applied (see fig. 16):
one is proportional flow regulation, and the specific steps are as follows:
the selection steps of the adjusting branch line are as follows in sequence:
1) all balance valves of the system account are fully opened, and all other valves on the water supply pipeline are also fully opened, so that the system operates under the working condition of over-flow;
2) the flow, namely the flow of each branch line, is directly checked by utilizing a balance valve calculation chart; or the flow value can be directly checked by utilizing a balance valve calculation chart according to the pressure difference between the front part and the rear part of the balance valve;
3) calculating the ratio x of the branch flowsi，
In the formula, i is the serial number of each branch line; n is the number of branches; giFor measured actual flow of the branch, m3/h；Gi' is the branch ideal flow, m3/h；
4) Selecting the maximum value x of the flow ratiomaxThe corresponding branch is the regulation branch. Arranging according to the magnitude sequence of the branch flow ratio, namely the sequential adjustment sequence of the branches; in general, the heat source proximal leg flow ratio is large and therefore tends to be adjusted from the proximal leg first.
The second is the regulation of branch lines, and the specific steps are as follows:
1) calculating the flow ratio of the branch line heat insulation users, and selecting the minimum value x of the flow ratiominCorresponding heatThe user is a reference user; if branch A is the regulation branch, then xmax＝GA/GA'; in line A, if the flow rate ratio of the user 3 is minimum, xmin＝GA3/GA3', user 3 is the reference user;
2) starting from the end user 1 of the regulating branch line A, regulating the balance valve F-A1 by using an intelligent instrument matched with the balance valve to regulate the flow ratio x of the user 1A1Adjusted to about 95% of the flow ratio of the reference user 3, i.e. xA1＝0.95(GA3/GA3′)；
3) The balance valve F-A2 is adjusted to make the flow ratio x of the user 2A2Adjusted to be equal to the flow ratio of user 1, i.e. xA2＝xA1(ii) a It should be noted that the original flow ratio of user 1 will increase slightly due to the adjustment of user 2;
4) continuously taking the flow ratio of the user 1 as a reference value, and adjusting the users 3 and 4 at one time according to the same method of the step 3); the flow of user 1 will increase slightly for each adjustment of one user, which is normal;
5) according to the sequence of the branch flow ratio, adopting the same method to adjust other branches in turn; the reference flow ratio is the minimum value in each branch line;
and the third step is the adjustment between branch lines, and the specific steps are as follows:
1) measuring the flow ratio, x, of each branchA，xB，xC，xDTaking the minimum value as a reference ratio;
2) starting with the last branch, i.e. adjusting the balancing valves F-D, so that the flow ratio of branch D is adjusted to 95% of the reference value of the branch, if the reference branch is branch C, then x should be adjustedD＝0.95xC-0.95xmin；
3) In the same way, the balance valves F-C, F-B, F-A are sequentially adjusted to ensure that the flow ratio of each branch line is equal to the flow ratio of the tail-most branch line D; during the adjustment process, the flow ratio of the tail branch line D is slightly increased;
4) if the branch lines belong to different areas in the same heating system, the branch lines in the same section are adjusted first, and the adjustment between the sections is performed, and the adjustment method is the same as the above.
Fourthly, the whole network adjustment comprises the following specific steps:
adjusting a total balance valve F of the heating system, wherein the total balance valve F is arranged on a water supply pipeline or a water return pipeline, so that the flow ratio of a branch line D at the tail end is equal to 1.0, and the regulation is carried out according to a consistent equal ratio maladjustment principle; after the adjustment, the flow of each branch line and each heat user of the heating system is always operated on the value of ideal flow or designed flow, and the whole network adjustment is finished.
The proportion regulation method has simple principle and good effect. However, the adjusting method is still complicated, firstly two sets of intelligent instruments are needed to be used, two sets of testers are equipped, and information contact is carried out through a telephone or a telephone reporter; secondly, the repeated measurement times of the balance valve are too many, and the adjustment process is time-consuming and labor-consuming. But generally speaking, the method makes the application of initial adjustment in practical engineering possible due to the support of the balance valve and the intelligent instrument.
Compared with the prior art, the terminal user heat supply temperature control system based on the wireless interconnection and data mining technology establishes a mathematical model of the whole heating season of the user side by acquiring the indoor temperature of the user and the meteorological data related to the indoor temperature and the meteorological data by using a big data platform and the data mining and load forecasting technology, provides scientific basis for the load distribution of a heat source, a heat supply network and heat users, and further realizes the technical purpose of maximizing energy conservation on the premise that the comfort of the user is ensured by the heat supply system.
Description of the drawings:
FIG. 1 is a schematic diagram of the constituent principle of an end user load prediction system based on wireless interconnection and data mining technology;
FIG. 2 is a schematic diagram of a meteorological website data capture technique;
FIG. 3 is a schematic diagram of the principle of the wireless temperature sensor;
FIG. 4 is a graph of the average outdoor temperature and heat load distribution over the heating season for a heating company;
FIG. 5 is a graph of raw data and fit;
FIG. 6 is a deviation profile;
FIG. 7 is a graph of absolute deviation distribution;
FIG. 8 is a diagram illustrating the sorted absolute values of the deviations;
FIG. 9 is a step-by-step screening of heat supply data points (raw data);
FIG. 10 shows heat supply data points that were progressively screened (first screening results);
FIG. 11 shows heat supply data points that were progressively screened (second screening);
FIG. 12 shows heat data points that were progressively screened (third screening);
FIG. 13 shows a graph of load forecast curves obtained by screening data points and fitting.
Fig. 14 is an exploded view of the electric valve formed by the electric actuator 12.1 and the valve body 12.2;
FIG. 15 is a schematic view of the assembly of the V-ball valve;
FIG. 16 is a schematic diagram of a system of a proportional control method;
FIG. 17 is a schematic diagram of the principle of the system of the intelligent remote valve temperature control device for central heating;
FIG. 18 is a comparison graph of flow characteristic curves of a V-shaped thermostatic valve device and a common ball valve;
FIG. 19 is one of comparison graphs of control characteristics of the V-shaped thermo-valve device and a conventional on-off valve;
FIG. 20 is a second comparison graph of the control characteristic curves of the V-shaped thermo-valve device and the ordinary on-off valve.
The specific implementation mode is as follows:
example 1
A terminal user load prediction system based on wireless interconnection and data mining technology comprises the following components: the system comprises a data warehouse 1, a data synchronization module 2 and a load prediction and room temperature control module 3; wherein: the data synchronization module 2 and the load prediction and room temperature control module 3 are respectively connected with the data warehouse 1;
the data warehouse 1 is specifically composed of the following parts: the system comprises a data conversion unit 1.1, a dimension modeling unit 1.2, an online analysis unit 1.3 and a data mining unit 1.4; wherein:
the data conversion unit 1.1 is connected with the data synchronization module 2, and the dimension modeling unit 1.2 is connected with the data conversion unit 1.1; the online analysis unit 1.3 is connected with the dimension modeling unit 1.2 and the data synchronization module 2; the data mining unit 1.4 is connected with the dimension modeling unit 1.2, the online analysis unit 1.3 and the data synchronization module 2;
the data conversion unit 1.1 synchronizes the outdoor daily average temperature t obtained by the module 2wAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；Q’＝[Q1,Q2,……,Qi,……,QN]；
the dimension modeling unit 1.2 generates (T) from the two one-dimensional arrays generated by the data conversion unit 1.1wi，Q’i) Two-dimensional coordinate sequences, i.e. scatter distribution maps, using least-squares to assemble raw data points for heat Q and outdoor temperature TwFitting to a first order curve relationship:
Q’＝kTW+b
the online analysis unit 1.3 is used for obtaining a first order curve relation of the dimensionality modeling unit 1.2: q' ═ kTW+ b and the indoor temperature data collected by the data synchronization module 2 are corrected, and a corrected load curve for the house maintenance structure of the sampling cell is obtained, namely:
the data mining unit 1.4 obtains a load prediction formula Q according to the dimension modeling unit 1.2 and the online analysis unit 1.3P＝ATW+ B, future 24-hour meteorological data captured by the data synchronization module 2, predicting future 24-hour thermal load, and making a corresponding room temperature control strategy for production and operation managers to use;
the data synchronization module 2 is specifically composed of the following parts: the system comprises a wireless room temperature acquisition module 2.1, an air temperature acquisition module 2.2, a wind power acquisition module 2.3, a humidity acquisition module 2.4, a heat consumption data query module 2.5 and an auxiliary unit 2.6; wherein: the wireless room temperature acquisition module 2.1 acquires the indoor temperature of a typical user through a room temperature acquisition device which is installed in a user room and is based on a wireless interconnection technology, uploads the indoor temperature to a database, and is used as a correction parameter of the online analysis unit 1.3 in the next heating season and the data of the implementation control target value in the heating season for intelligent analysis;
the air temperature acquisition module 2.2, the wind power acquisition module 2.3 and the humidity acquisition module 2.4 capture meteorological forecast data of a meteorological website for 24 hours in the future as state input parameters of the data mining unit 1.4 and simultaneously serve as input parameters of the dimension modeling unit 1.2 in the next heating season;
the heat consumption data query module 2.5 queries the synchronous data corresponding to the outdoor temperature and the indoor temperature in the database through the SQL data search engine as the input parameters of the dimension modeling unit 1.2.
The load forecasting and room temperature control module 3 is connected with a data mining unit 1.4; the load forecasting and room temperature control module 3 converts the load forecasting data generated by the data mining unit 1.4 into various production management curves which can be directly applied to a production scheduling system service module, a management module, an analysis module and the like, such as a boiler DCS, a heat supply network supervisory control and data acquisition (SCADA) and a factory Management Information System (MIS), such as a 24-hour load forecasting curve of a boiler and each heating power station, a supply and return water temperature climate compensation curve and the like.
The data warehouse 1 comprises a data conversion unit 1.1, a dimension modeling unit 1.2, an online analysis unit 1.3 and a data mining unit 14, and the data conversion unit, the dimension modeling unit 1.2, the online analysis unit 1.3 and the data mining unit 14 generate load prediction and production guidance data of meteorological data captured by the corresponding synchronization module 2 in the future 24 hours according to historical data (mainly the last heating season) acquired by each component in the data synchronization module 2 and a data modeling and mining technology specific to the data warehouse 1.
The data synchronization module 2 captures meteorological data matched with a meteorological website and an indoor temperature acquisition time domain by using a website data capture method, and performs subsequent processing; the concrete requirements are as follows:
the website data capturing method specifically comprises the steps of capturing meteorological data of a meteorological website matched with an indoor temperature acquisition time domain by using EXCEL imported webpage data and a VBA programming technology, storing the meteorological data into an ACCESS database to serve as attribute parameters for historical data mining of an indoor temperature load meteorological model, capturing weather forecast data of the meteorological website in the future 24 hours as state parameters of the load meteorological model when calculation control is output (see figure 2), and obtaining the following reference codes:
one or the combination of a wireless room temperature acquisition module 2.1, an air temperature acquisition module 2.2, a wind power acquisition module 2.3, a humidity acquisition module 2.4 and an energy consumption data query module 2.5 in the data synchronization module 2 adopts the following wireless data acquisition and network transmission technology for processing, and the specific requirements are as follows:
in engineering practice, indoor temperature collection points are selected according to 5% of the number of users of a certain building, and the collection points cover typical users such as cold mountains, top floors, middle floors and the like; the wireless sensor is directly installed in a user room, a data receiver is installed in a pipeline well near a corresponding acquisition point, a concentrator is arranged in each unit, and the data is uploaded to a data center or a cloud platform server through GPRS;
the wireless sensor comprises a wireless room temperature acquisition module 2.1, the wireless room temperature acquisition module 2.1 is a basic unit of the network, and the stable operation of the wireless room temperature acquisition module is a basic guarantee for the reliability of the whole network; the wireless room temperature acquisition module 2.1 consists of a sensor module 2.1a, a processor module 2.1b, a wireless communication module 2.1c and an energy supply module 2.1 d; as shown in fig. 3, wherein: the sensor module 2.1a, the processor module 2.1b and the wireless communication module 2.1c are respectively connected with the energy supply module 2.1 d; the sensor module 2.1b and the wireless communication module 2.1c are respectively connected with the processor module 2.1 b; specifically, the method comprises the following steps: the sensor module 2.1a is in turn made up of a sensor unit 2.1a1 and an AC/DC unit 2.1a2 connected to each other; the processor module 2.1b is composed of two parts, namely a processor 2.1b1 and a memory 2.1b 2; the wireless communication module 2.1c is composed of a network 2.1c1, a MAC2.1c2 and a transceiver 2.1c 3;
the technical parameter requirements of the wireless temperature sensor are as follows: measuring the temperature: -40 ℃ to +150 ℃, transmission distance (around the wall): 50m, measurement accuracy: +/-0.5 ℃, temperature measurement resolution: 0.1 ℃, frequency range: 433MHz (application-free), the whole machine sleep current is less than 2 muA, the maximum transmitting power: 10dbm, working voltage: 2.7 to 3.6V, external dimension: 40 × 30 × 25mm, design life: 10 years; the technical parameter requirements of the wireless temperature sensor are shown in table 1;
TABLE 1 technical parameters of Wireless temperature sensor
| Model number | SJ100 |
| Measuring temperature | -40℃～+150℃ |
| Transmission distance (around wall) | 50m |
| Measurement accuracy | ±0.5℃ |
| Temperature measurement resolution | 0.1℃ |
| Frequency range | 433MHz (application-free) |
| Sleep current of whole machine | ＜2μA |
| Maximum ofTransmitting power | 10dbm |
| Operating voltage | 2.7～3.6V |
| Overall dimension | 40x30x25mm |
| Design life | For 10 years |
The sensor module 2.1a is responsible for collecting information and converting data in a detection area, the processor module 2.1b is responsible for controlling the operation of the whole sensor node, storing and processing data collected by the processor module and data sent by other nodes, the wireless communication module 2.1c is responsible for carrying out wireless communication with other sensor nodes, exchanging control information and receiving and sending collected data, and the energy supply module provides energy required by the operation of the nodes for the sensor nodes.
The data mining unit 1.4 also meets the following requirements: fig. 4 is a distribution diagram of the average outdoor temperature and the heat load in the heating season of a certain heating company, and as can be seen from fig. 4, the distribution range of the data points is large and dispersed, which indicates that the data points include data points of unreasonable heat supply, i.e., excessive heat supply and insufficient heat supply, and therefore the data are screened; under the condition that the building is fixed and the indoor temperature is constant, the magnitude of the heat load is only related to the outdoor temperature and has a linear relation;
wherein Q is the building thermal load, W; q. q.svIs an index of heat accumulation of a building, W/m3·℃；qFIs an area heat index of a building, W/m2)(ii) a V is the volume of the enclosure structure, m3(ii) a F is the area of the enclosure structure, m2(ii) a H is the height of the building, m; t is tnWinter indoor temperature, deg.C; t is twWinter outdoor temperature, ° c。
The data screening steps are as follows:
step 1) sorting out a data set of historical heat supply operation data;
assuming that N heat supply working condition data points are collected in total, the average outdoor daily temperature twAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；
Q′＝[Q1,Q2,……,Qi,……,QN]
wherein, i is 1,2,3, … … N.i is ordinal in chronological order, here, the number of days for starting heating;
step 2) fitting the relation between the heat supply load and the outdoor temperature by using a primary curve;
fitting the heat supply Q and the outdoor temperature tw of the original data point set into a primary curve relationship by using a least square method:
Q'＝ktw+b(1-3)
wherein Q' is a fitted curve heat supply load, MWH or GJ, calculated amount; t is twCalculating the average outdoor daily temperature in winter at DEG C, and inputting the calculated value; k is a first-order coefficient of a curve obtained by fitting, and the calculated amount is calculated; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is calculated;
the relationship between the fitted curve and the raw data is shown in fig. 5;
using fitted curve equation (1-3) and outdoor temperature twThe heat supply amount on the fitting curve can be solved by the data point set of (1):
Qi’＝ktw,i+b (1-4)
wherein: 1,2,3 … … N; this results in a set of heat supply data points on the fitted curve corresponding to the outdoor daily average temperature points: q ' ([ Q ') '1，Q′2，…，Q′i，…，Q′N]
Step 3) calculating the deviation between the actual heat supply load and the corresponding point on the fitting curve, and taking the absolute value of the deviation;
first the deviation between the actual heating load and the load on the fitted curve is calculated, ei＝Qi-Q′i
In the formula: qiTesting the MWH or GJ for the measured data; q'iCalculating the amount MWH or GJ for the heat supply on the fitted curve;
a set of deviations is obtained:
e＝e1，e2，…，ei，…eN
wherein: when i is 1,2,3, … … N, the actual value and the absolute value of the deviation are shown in fig. 6 and fig. 7;
step 4) sorting the deviation absolute values of all the working condition points, and removing 10% of the original data points according to the sequence of the deviation absolute values from large to small to obtain a new data point set, as shown in FIG. 8;
and 5) repeating the processes from the step 1) to the step 4) twice, wherein the number of the obtained final data points is 70% of the number of the original data points.
In the system for predicting the load of the terminal user based on the wireless interconnection and data mining technology, the data warehouse 1 further meets the following requirements: fitting a primary curve of the heat supply quantity and the outdoor temperature by using the data point set subjected to screening and data mining as a load forecasting curve; see fig. 13;
the resulting fitted linear equation through the screened data is:
QP＝Atw+B (1-5)
wherein Q ispCalculating the amount for predicting the load value, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; and B is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained.
The load forecast curve obtained after data screening is more reasonable than the original curve obtained by direct fitting; in order to make the load forecasting curve adapt to the change of the actual heat supply condition, the load forecasting curve is corrected:
correcting a load forecasting curve according to a heat supply area: at present, a large number of new buildings are put into use every year in China, so the actual heat supply area of a heating power company changes every year. The load forecast curve mined in the previous year is used for directly guiding the heat supply operation in the current year, and the load forecast curve is unreasonable and needs to be corrected according to the actual heat supply area; the corrected load prediction curve is shown in the formula (1-7).
Wherein Q ispmxCalculating the amount of predicted load value after area correction, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained; f0Total area of heat supply for the last heating season, m2Calculating a value from the input; f1Total area of heat supply for the current heating season, m2Calculating a value from the input;
2) correcting a load forecasting curve according to the room temperature: the room temperature fed back by the actual user is greatly different from the set room temperature, which indicates that the actual heat supply amount deviates from the heat supply amount; then correcting according to the formula (1-8);
wherein Q isPtxCalculating a value for the predicted heat supply after temperature correction, MWH or GJ; qpmxCalculating a heat supply value after area correction in a statistic time period, namely an MWH or GJ value; t is twpThe average outdoor temperature in winter in the statistical period, DEG C, and the measured value; t is tnsCalculating the indoor temperature set value, DEG C; t is tnpThe average temperature value in the statistical period, DEG C, measured value; t is tn,iIndoor temperature value, DEG C, measured value at the ith moment;
the indoor average temperature at the first N moments is measured at the temperature of DEG C;
when the heat supply amount is initially predicted, the statistical period is a time period corresponding to the existing data, and is generally the data of the previous year.
The terminal user load prediction system based on the wireless interconnection and data mining technology is also provided with a centralized heating intelligent remote valve temperature control device; it comprises the following components: heat gauge 11, intelligent remote valve 12, collector 13, control by temperature change panel 14, measurement control by temperature change integration management platform 15, wherein: the heat meter 11, the intelligent remote valve 12 and the temperature control panel 14 are connected with the collector 13 through an MBUS communication bus, the collector 13 is connected with the integrated metering and temperature control management platform 15 through a wired or wireless connection structure, and the intelligent remote valve 12 is connected with the temperature control panel 14 through wireless communication;
the energy-type remote valve 12 comprises the following two parts which are connected with each other: an electric actuator 12.1 and a valve body 12.2; an electric actuator 12.1 in the intelligent remote valve 12 is connected with a room temperature controller through a wireless connection module; the following requirements are also specifically met:
① the electric actuator 12.1 in the intelligent remote valve 12 is connected with the temperature control panel 14 through wireless communication;
② the electric actuator 12.1 of the intelligent remote valve 12 is connected with the temperature control panel 14 and the valve body 12.2, so as to receive the room temperature information and the set temperature information and control the switch of the valve body 12.2 of the intelligent remote valve 12;
③ recording the on-off time of the intelligent remote valve 12, timing with the temperature control panel 14;
④ the electric actuator 12.1 of the intelligent remote valve 12 communicates with the temperature control panel 14 once every certain period, and the real-time communication is carried out after the set temperature is adjusted to execute the temperature setting instruction;
the intelligent remote valve 12 is connected with the collector 13 through a remote meter reading system M-Bus;
the measurement and temperature control integrated management platform 15 and the collector 13 are connected into a whole through a general packet radio service system GPRS; the intelligent remote valve 12 is composed as follows: electric actuator 12.1, V type ball valve 12.2, PT1000 temperature sensor 12.3 wherein: the electric actuator 12.1 is connected with a control command driving V-shaped ball valve 12.2 through a temperature control panel 14 or a collector 13 respectively; the PT1000 temperature sensor 12.3 is respectively connected with a V-shaped ball valve 12.2 and an electric actuator 12.1; the actuator 12.1 is a component adopting a main gear lengthening shaft; the V-shaped ball valve 12.2 is a structural member adopting a V-shaped valve clack.
Electric actuator 12.1 and valve body 12.2 constitute electric valve jointly, and electric valve's concrete structural component is: the device comprises an upper shell 5.1, a lower shell 5.2, a circuit board 5.3, a sealing ring 5.4, a battery 5.5, a battery cover 5.5a, a motor 5.6 and a V-shaped ball valve 12.2; wherein: a circuit board 5.3, a battery 5.5, a battery cover 5.5a and a motor 5.6 are arranged in a cavity formed by buckling the upper shell 5.1 and the lower shell 5.2; the circuit board 5.3 contacts the upper shell 5.1 or/and the lower shell 5.2 through a sealing ring 5.4; the battery 5.5 is fixed on the battery cover 5.5 a;
the upper shell 5.1 and the lower shell 5.2, the circuit board 5.3, the battery cover 5.5a and the motor 5.6 are fixedly connected with the upper shell 5.1 or/and the lower shell 5.2 through a threaded connecting piece 5.8; the part of the V-shaped ball valve 12.2 extends into the cavity between the upper shell 5.1 and the lower shell 5.2 and is connected with the motor 5.6; the circuit board 5.3 is respectively connected with a battery 5.5 and a motor 5.6; the outer side of the upper shell 5.1, which is far away from the V-shaped ball valve 12.2, is also provided with a surface paste 5.7; a sealing ring 5.4 with corresponding shape and size is also arranged between the V-shaped ball valve 12.2 and the battery 5.5 or/and the battery cover 5.5 a.
The V-ball valve 12.2 is constructed as follows: the valve comprises a valve body 12.2a, a valve rod 12.2b, a V-shaped ball valve core 12.2c, a valve cap 12.2d, a sealing ring 12.2e and a tetrafluoro gasket 12.2f for anticorrosion sealing; wherein: the valve body 12.2a is a tubular structural part, and a T-shaped pipe joint perpendicular to the axis of the main body part is also arranged on the valve body; the valve stem 12.2b is arranged in a branch line of the "T" -shaped pipe joint; the V-shaped ball valve core 12.2c is arranged in the inner cavity of the valve body 12.2a, and the V-shaped ball valve core 12.2c and the valve body 12.2a are mutually embedded or fixedly connected; a bonnet 12.2d is fixedly disposed at one tubular end of the body portion of the valve body 12.2 a; the valve rod 12.2b is also sleeved with a sealing ring 12.2e which is matched with a branch pipeline of a T-shaped pipe joint in the valve body 12.2 a; the V-shaped ball valve core 12.2c is arranged at the front side and the rear side of a main pipeline in the valve body 12.2a and is respectively provided with a polytetrafluoroethylene gasket 12.2f for corrosion prevention and sealing.
When the terminal user load prediction system based on the wireless interconnection and data mining technology uses a centralized heating intelligent remote valve temperature control device, an adjusting algorithm meeting the following requirements is applied (see fig. 16):
one is proportional flow regulation, and the specific steps are as follows:
the selection steps of the adjusting branch line are as follows in sequence:
1) all balance valves of the system account are fully opened, and all other valves on the water supply pipeline are also fully opened, so that the system operates under the working condition of over-flow;
2) the flow, namely the flow of each branch line, is directly checked by utilizing a balance valve calculation chart; or the flow value can be directly checked by utilizing a balance valve calculation chart according to the pressure difference between the front part and the rear part of the balance valve;
3) calculating the ratio x of the branch flowsi，
In the formula, i is the serial number of each branch line; n is the number of branches; giFor measured actual flow of the branch, m3/h；Gi' is the branch ideal flow, m3/h；
4) Selecting the maximum value x of the flow ratiomaxThe corresponding branch is the regulation branch. Arranging according to the magnitude sequence of the branch flow ratio, namely the sequential adjustment sequence of the branches; in general, the heat source proximal leg flow ratio is large and therefore tends to be adjusted from the proximal leg first.
The second is the regulation of branch lines, and the specific steps are as follows:
1) calculating the flow ratio of the branch line heat insulation users, and selecting the minimum value x of the flow ratiominThe corresponding hot user is a reference user; if branch A is the regulation branch, then xmax＝GA/GA'; in line A, if the flow rate ratio of the user 3 is minimum, xmin＝GA3/GA3', user 3 is the reference user;
2) starting from the end user 1 of the regulating branch line A, regulating the balance valve F-A1 by using an intelligent instrument matched with the balance valve to regulate the flow ratio x of the user 1A1Adjusted to about 95% of the flow ratio of the reference user 3, i.e. xA1＝0.95(GA3/GA3′)；
3) The balance valve F-A2 is adjusted to make the flow ratio x of the user 2A2Adjusted to be equal to the flow ratio of user 1, i.e. xA2＝xA1(ii) a It should be noted that the original flow ratio of user 1 will increase slightly due to the adjustment of user 2;
4) continuously taking the flow ratio of the user 1 as a reference value, and adjusting the users 3 and 4 at one time according to the same method of the step 3); the flow of user 1 will increase slightly for each adjustment of one user, which is normal;
5) according to the sequence of the branch flow ratio, adopting the same method to adjust other branches in turn; the reference flow ratio is the minimum value in each branch line;
and the third step is the adjustment between branch lines, and the specific steps are as follows:
1) measuring the flow ratio, x, of each branchA，xB，xC，xDTaking the minimum value as a reference ratio;
2) starting with the last branch, i.e. adjusting the balancing valves F-D, so that the flow ratio of branch D is adjusted to 95% of the reference value of the branch, if the reference branch is branch C, then x should be adjustedD＝0.95xC-0.95xmin；
3) In the same way, the balance valves F-C, F-B, F-A are sequentially adjusted to ensure that the flow ratio of each branch line is equal to the flow ratio of the tail-most branch line D; during the adjustment process, the flow ratio of the tail branch line D is slightly increased;
4) if the branch lines belong to different areas in the same heating system, the branch lines in the same section are adjusted first, and the adjustment between the sections is performed, and the adjustment method is the same as the above.
Fourthly, the whole network adjustment comprises the following specific steps:
adjusting a total balance valve F of the heating system, wherein the total balance valve F is arranged on a water supply pipeline or a water return pipeline, so that the flow ratio of a branch line D at the tail end is equal to 1.0, and the regulation is carried out according to a consistent equal ratio maladjustment principle; after the adjustment, the flow of each branch line and each heat user of the heating system is always operated on the value of ideal flow or designed flow, and the whole network adjustment is finished.
The proportion regulation method has simple principle and good effect. However, the adjusting method is still complicated, firstly two sets of intelligent instruments are needed to be used, two sets of testers are equipped, and information contact is carried out through a telephone or a telephone reporter; secondly, the repeated measurement times of the balance valve are too many, and the adjustment process is time-consuming and labor-consuming. But generally speaking, the method makes the application of initial adjustment in practical engineering possible due to the support of the balance valve and the intelligent instrument.
Compared with the prior art, the terminal user heating temperature control system based on the wireless interconnection and data mining technology establishes a mathematical model of the whole heating season of the user side by acquiring the indoor temperature of the user and meteorological data related to the indoor temperature of the user and utilizing a big data platform and the data mining and load prediction technology, provides scientific basis for load distribution of a heat source, a heat supply network and the heat user, and further achieves the technical purpose of maximizing energy conservation on the premise that the comfort of the user is guaranteed by the heating system.
Example 2
This embodiment is basically the same as embodiment 1, except that:
by adopting load forecasting based on comprehensive temperature, the heat gained by the sun is not considered in the calculation of the energy consumption of the building at present and is considered as a safety value. This is possible when selecting heating equipment, but when performing operational scheduling, the heating load will be high regardless of the influence of the sun. If the influence of solar radiation is converted into the outdoor temperature, the outdoor temperature is defined as an outdoor comprehensive temperature, and the average value of the outdoor comprehensive temperature is calculated according to the formula (1-11):
in the formula: t is twzypIs the average value of outdoor comprehensive temperature (DEG C); (calculated value)
twypIs the predicted average value of the outdoor air temperature (DEG C); (predicting values based on test data or weather station data)
Delta t-outdoor temperature increase value (DEG C), and is selected according to a city table look-up; (Table lookup)
rho is the solar radiation absorption coefficient and can be used by looking up a table;
α e is the external surface heat exchange coefficient, and is 19.0W/m 2. K.
Claims (10)
1. A terminal user load prediction system based on wireless interconnection and data mining technology is characterized in that: the system is composed as follows: the system comprises a data warehouse (1), a data synchronization module (2) and a load prediction and room temperature control module (3); wherein: the data synchronization module (2) and the load prediction and room temperature control module (3) are respectively connected with the data warehouse (1);
the data warehouse (1) is specifically composed of the following parts: the system comprises a data conversion unit (1.1), a dimension modeling unit (1.2), an online analysis unit (1.3) and a data mining unit (1.4); wherein:
the data conversion unit (1.1) is connected with the data synchronization module (2), and the dimension modeling unit (1.2) is connected with the data conversion unit (1.1); the online analysis unit (1.3) is connected with the dimension modeling unit (1.2) and the data synchronization module (2); the data mining unit (1.4) is connected with the dimension modeling unit (1.2), the online analysis unit (1.3) and the data synchronization module (2);
the data conversion unit (1.1) synchronizes the outdoor daily average temperature t obtained by the module (2)wAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；Q’＝[Q1,Q2,……,Qi,……,QN]；
the dimension modeling unit (1.2) generates (T) from the two one-dimensional arrays generated by the data conversion unit (1.1)wi，Q’i) Two-dimensional coordinate sequences, i.e. scatter distribution maps, using least-squares to assemble raw data points for heat Q and outdoor temperature TwFitting to a first order curve relationship:
Q’＝kTW+b
the online analysis unit (1.3) is used for carrying out the relationship of the first-order curve obtained by the dimension modeling unit (1.2): q' ═ kTW+ b and the indoor temperature data collected by the data synchronization module (2) are corrected, and a corrected load curve aiming at the house maintenance structure of the sampling cell is obtained, namely:
the data mining unit (1.4) obtains a load prediction formula Q according to the dimension modeling unit (1.2) and the online analysis unit (1.3)P＝ATW+ B, future 24-hour meteorological data captured by the data synchronization module (2), predicting future 24-hour heat load, and making a corresponding room temperature control strategy for production and operation managers to use;
the data synchronization module (2) is specifically composed of the following parts: the system comprises a wireless room temperature acquisition module (2.1), an air temperature acquisition module (2.2), a wind power acquisition module (2.3), a humidity acquisition module (2.4), a heat consumption data query module (2.5) and an auxiliary unit (2.6); wherein:
the wireless room temperature acquisition module (2.1) acquires the indoor temperature of a typical user through a room temperature acquisition device which is installed in a user room and is based on a wireless interconnection technology, uploads the indoor temperature to a database, and is used as a correction parameter of the online analysis unit (1.3) of the next heating season and the data of the implementation control target value in the heating season for intelligent analysis;
the temperature acquisition module (2.2), the wind power acquisition module (2.3) and the humidity acquisition module (2.4) capture meteorological forecast data of a meteorological website for 24 hours in the future as state input parameters of the data mining unit (1.4), and meanwhile, the meteorological forecast data are used as input parameters of the next heating season dimension modeling unit (1.2);
the heat consumption data query module (2.5) queries synchronous data corresponding to the outdoor temperature and the indoor temperature in a database through an SQL data search engine, and the synchronous data are used as input parameters of the dimension modeling unit (1.2);
the load prediction and room temperature control module (3) is connected with a data mining unit (1.4); the load prediction and room temperature control module (3) converts the load prediction data generated by the data mining unit (1.4) into a production scheduling system business module, a management module, an analysis module and other production management curves which can be directly applied.
2. The system for end-user load prediction based on wireless interconnection and data mining technology as claimed in claim 1, wherein: the load forecasting and production guiding data of the meteorological data captured in the future 24 hours by the corresponding synchronization module (2) are generated by the components of the data warehouse (1), namely the data conversion unit (1.1), the dimension modeling unit (1.2), the online analysis unit (1.3) and the data mining unit (14) according to historical data acquired by each component in the data synchronization module (2) and the data modeling and mining technology specific to the data warehouse (1).
3. The system for end-user load prediction based on wireless interconnection and data mining technology as claimed in claim 2, wherein: the data synchronization module (2) captures meteorological data matched with the meteorological website and the indoor temperature acquisition time domain by using a website data capture method, and performs subsequent processing; the concrete requirements are as follows: the website data capturing method specifically comprises the steps of capturing meteorological data of a meteorological website matched with an indoor temperature acquisition time domain by using EXCEL imported webpage data and a VBA programming technology, storing the meteorological data into an ACCESS database to serve as attribute parameters for historical data mining of an indoor temperature load meteorological model, and capturing weather forecast data of the meteorological website in the future 24 hours to serve as state parameters of the load meteorological model when calculation control is output.
4. An end-user load prediction system based on wireless interconnection and data mining technology as claimed in claim 3, characterized in that: one or the combination of a wireless room temperature acquisition module (2.1), an air temperature acquisition module (2.2), a wind power acquisition module (2.3), a humidity acquisition module (2.4) and an energy consumption data query module (2.5) in the data synchronization module (2) adopts the following wireless data acquisition and network transmission technology for processing, and the specific requirements are as follows:
the wireless sensor is directly installed in a user room, a data receiver is installed in a pipeline well near a corresponding acquisition point, a concentrator is arranged in each unit, and the data is uploaded to a data center or a cloud platform server through GPRS;
the wireless sensor comprises a wireless room temperature acquisition module (2.1), and the wireless room temperature acquisition module (2.1) is a basic unit of the network; the wireless room temperature acquisition module (2.1) consists of a sensor module (2.1a), a processor module (2.1b), a wireless communication module (2.1c) and an energy supply module (2.1 d); wherein: the sensor module (2.1a), the processor module (2.1b) and the wireless communication module (2.1c) are respectively connected with the energy supply module (2.1 d); the sensor module (2.1b) and the wireless communication module (2.1c) are respectively connected with the processor module (2.1 b); specifically, the method comprises the following steps: the sensor module (2.1a) is in turn composed of a sensor unit (2.1a1) and an AC/DC unit (2.1a2) which are connected to one another; the processor module (2.1b) is composed of a processor (2.1b1) and a memory (2.1b 2); the wireless communication module (2.1c) is composed of a network (2.1c1), a MAC (2.1c2) and a transceiver (2.1c 3);
the technical parameter requirements of the wireless temperature sensor are as follows: measuring the temperature: -40 ℃ to +150 ℃, transmission distance: 50m, measurement accuracy: +/-0.5 ℃, temperature measurement resolution: 0.1 ℃, frequency range: 433MHz, the whole machine sleep current is less than 2 muA, the maximum transmitting power: 10dbm, working voltage: 2.7 to 3.6V, external dimension: 40 × 30 × 25mm, design life: 10 years;
the sensor module (2.1a) is responsible for collecting information and converting data in a detection area, the processor module (2.1b) is responsible for controlling the operation of the whole sensor node, storing and processing data collected by the processor module and data sent by other nodes, the wireless communication module (2.1c) is responsible for carrying out wireless communication with other sensor nodes, exchanging control information and receiving and sending collected data, and the energy supply module provides energy required by the operation of the nodes for the sensor nodes.
5. An end-user load prediction system based on wireless interconnection and data mining technology as claimed in claim 3, characterized in that: the data mining unit (1.4) also meets the following requirements:
under the condition that the building is fixed and the indoor temperature is constant, the magnitude of the heat load is only related to the outdoor temperature and has a linear relation;
wherein Q is the building thermal load, W; q. q.svIs an index of heat accumulation of a building, W/m3·℃；qFIs an area heat index of a building, W/m2)(ii) a V is the volume of the enclosure structure, m3(ii) a F is the area of the enclosure structure, m2(ii) a H is the height of the building, m; t is tnWinter indoor temperature, deg.C; t is twIs the outdoor temperature in winter, DEG C.
The data screening steps are as follows:
step 1) sorting out a data set of historical heat supply operation data;
assuming that N heat supply working condition data points are collected in total, the average outdoor daily temperature twAnd the heating load Q corresponding to the average outdoor daily temperature is respectively expressed as two one-dimensional arrays:
Tw＝[tw,1tw,2tw,3……tw,i……tw,N]；
Q′＝[Q1,Q2,……,Qi,……,QN]
wherein, i is 1,2,3, … … N.i is ordinal in chronological order, here, the number of days for starting heating;
step 2) fitting the relation between the heat supply load and the outdoor temperature by using a primary curve;
fitting the heat supply Q and the outdoor temperature tw of the original data point set into a primary curve relationship by using a least square method:
Q′＝ktw+b (1-3)
wherein Q' is the fitted curve heating load, MWHOr GJ, calculated amount; t is twCalculating the average outdoor daily temperature in winter at DEG C, and inputting the calculated value; k is a first-order coefficient of a curve obtained by fitting, and the calculated amount is calculated; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is calculated;
using fitted curve equation (1-3) and outdoor temperature twThe heat supply on the fitting curve is solved according to the data point set:
Qi’＝ktw,i+b (1-4)
wherein: 1,2,3 … … N; this results in a set of heat supply data points on the fitted curve corresponding to the outdoor daily average temperature points: q ' ([ Q ') '1，Q′2，…，Q′i’，…，Q′N]
Step 3) calculating the deviation between the actual heat supply load and the corresponding point on the fitting curve, and taking the absolute value of the deviation;
first the deviation between the actual heating load and the load on the fitted curve is calculated, ei＝Qi-Q′i
In the formula: qiTesting the MWH or GJ for the measured data; q'iCalculating the amount MWH or GJ for the heat supply on the fitted curve;
a set of deviations is obtained:
e＝e1，e2，…，ei，…eN
wherein: 1,2,3, … … N;
step 4) sorting the deviation absolute values of all the working condition points, and removing 10% of the original data points according to the sequence of the deviation absolute values from large to small to obtain a new data point set;
and 5) repeating the processes from the step 1) to the step 4) twice, wherein the number of the obtained final data points is 70% of the number of the original data points.
6. The system for end-user load prediction based on wireless interconnection and data mining technology of claim 5, wherein: in the system for predicting the load of the terminal user based on the wireless interconnection and data mining technology, the data warehouse (1) further meets the following requirements:
fitting a primary curve of the heat supply quantity and the outdoor temperature by using the data point set subjected to screening and data mining as a load forecasting curve;
the resulting fitted linear equation through the screened data is:
QP＝Atw+B (1-5)
wherein Q ispCalculating the amount for predicting the load value, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained;
the load forecast curve obtained after data screening is more reasonable than the original curve obtained by direct fitting; in order to make the load forecasting curve adapt to the change of the actual heating condition, the load forecasting curve is corrected:
1) correcting a load forecasting curve according to the heat supply area;
correcting according to the actual heat supply area; the corrected load prediction curve is shown in the formula (1-7).
Wherein: qpmxCalculating the amount of predicted load value after area correction, MWH or GJ; t is twThe winter outdoor daily average temperature, DEG C, the test quantity; a is a first-order coefficient of a curve obtained by fitting, and the calculated amount is obtained; b is a constant term coefficient of the curve obtained by fitting, and the calculated amount is obtained; f0Total area of heat supply for the last heating season, m2Calculating a value from the input; f1Total area of heat supply for the current heating season, m2Calculating a value from the input;
2) correcting load forecast curve according to room temperature
The room temperature fed back by the actual user is greatly different from the set room temperature, which indicates that the actual heat supply amount deviates from the heat supply amount; then correcting according to the formula (1-8);
wherein Q isPtxCalculating a value for the predicted heat supply after temperature correction, MWH or GJ; qpmxCalculating a value for the heat supply value after area correction in the statistical period, namely MWH or GJ; t is twpThe average outdoor temperature in winter in the statistical period, DEG C, and the measured value; t is tnsCalculating the indoor temperature set value, DEG C; t is tnpThe average temperature value in the statistical period, DEG C, measured value; t is tn,iIndoor temperature value, DEG C, measured value at the ith moment;
the indoor average temperature at the first N moments is measured to obtain the temperature;
and when the heat supply amount is initially predicted, the statistical period is a time period corresponding to the existing data.
7. The system for end-user load prediction based on wireless interconnection and data mining technology as claimed in claim 4, wherein: the terminal user load prediction system based on the wireless interconnection and data mining technology is also provided with a centralized heating intelligent remote valve temperature control device; it comprises the following components: heat gauge table (11), intelligent remote valve (12), collector (13), control by temperature change panel (14), measurement control by temperature change integration management platform (15), wherein: the heat meter (11), the intelligent remote valve (12) and the temperature control panel (14) are connected with the collector (13) through an MBUS communication bus, the collector (13) is connected with the integrated metering and temperature control management platform (15) through a wired or wireless connection structure, and the intelligent remote valve (12) is connected with the temperature control panel (14) through wireless communication;
the energy-type remote valve (12) comprises the following two parts which are connected with each other: an electric actuator (12.1) and a valve body (12.2); an electric actuator (12.1) in the intelligent remote valve (12) is connected with a room temperature controller through a wireless connection module; the following requirements are also specifically met:
① the electric actuator (12.1) in the intelligent remote valve (12) is connected with the temperature control panel (14) through wireless communication;
② the electric actuator (12.1) of the intelligent remote valve (12) is connected with the temperature control panel (14) and the valve body (12.2) so as to receive the room temperature information and the set temperature information and control the switch of the valve body (12.2) of the intelligent remote valve (12);
③ recording the on-off time of the intelligent remote valve 12, timing with the temperature control panel 14;
④ the electric actuator 12.1 of the intelligent remote valve 12 communicates with the temperature control panel 14 once every certain period, and the real-time communication is carried out after the set temperature is adjusted to execute the temperature setting instruction;
the intelligent remote valve (12) is connected with the collector (13) through a remote meter reading system M-Bus;
the measurement and temperature control integrated management platform (15) and the collector (13) are connected into a whole through a general packet radio service system (GPRS);
the intelligent remote valve (12) is composed of the following components: electric actuator (12.1), V type ball valve (12.2), PT1000 temperature sensor (12.3) wherein:
the electric actuator (12.1) is connected with a control command to drive the V-shaped ball valve (12.2) through a temperature control panel (14) or a collector (13) respectively;
a PT1000 temperature sensor (12.3) is respectively connected with a V-shaped ball valve (12.2) and an electric actuator (12.1);
the actuator (12.1) is a component adopting a main gear lengthening shaft;
the V-shaped ball valve (12.2) is a structural member adopting a V-shaped valve clack.
8. The system for end-user load prediction based on wireless connectivity and data mining technology as claimed in claim 7, wherein: electric actuator (12.1) and valve body (12.2) constitute electric valve jointly, and electric valve's concrete structural component is: the device comprises an upper shell (5.1), a lower shell (5.2), a circuit board (5.3), a sealing ring (5.4), a battery (5.5), a battery cover (5.5a), a motor (5.6) and a V-shaped ball valve (12.2); wherein: a circuit board (5.3), a battery (5.5), a battery cover (5.5a) and a motor (5.6) are arranged in a cavity formed by buckling the upper shell (5.1) and the lower shell (5.2); the circuit board (5.3) is contacted with the upper shell (5.1) or/and the lower shell (5.2) through a sealing ring (5.4); the battery (5.5) is fixed on the battery cover (5.5 a);
the upper shell (5.1) and the lower shell (5.2) as well as the circuit board (5.3), the battery cover (5.5a) and the motor (5.6) are fixedly connected with the upper shell (5.1) or/and the lower shell (5.2) through a threaded connecting piece (5.8); the part of the V-shaped ball valve (12.2) extends into the cavity between the upper shell (5.1) and the lower shell (5.2) and is connected with a motor (5.6); the circuit board (5.3) is respectively connected with a battery (5.5) and a motor (5.6); the outer side of the upper shell (5.1) far away from the V-shaped ball valve (12.2) is also provided with a surface paste (5.7); a sealing ring (5.4) with corresponding shape and size is also arranged between the V-shaped ball valve (12.2) and the battery (5.5) or/and the battery cover (5.5 a).
9. An end-user load prediction system based on wireless internet and data mining technology according to claim 4 or 7 or 8, characterized by: the V-shaped ball valve (12.2) is composed as follows: the valve comprises a valve body (12.2a), a valve rod (12.2b), a V-shaped ball valve core (12.2c), a valve cap (12.2d), a sealing ring (12.2e) and a polytetrafluoroethylene gasket (12.2f) for anticorrosion sealing; wherein: the valve body (12.2a) is a tubular structural part, and a T-shaped pipe joint perpendicular to the axis of the main body part is also arranged on the valve body; the valve stem (12.2b) is arranged in a branch line of the T-shaped pipe joint; the V-shaped ball valve core (12.2c) is arranged in the inner cavity of the valve body (12.2a), and the V-shaped ball valve core (12.2c) and the valve body (12.2a) are mutually embedded or fixedly connected; a bonnet (12.2d) fixedly arranged at one pipe end of the body portion of the valve body (12.2 a); the valve rod (12.2b) is also sleeved with a sealing ring (12.2e) which is matched with the branch pipeline of the T-shaped pipe joint in the valve body (12.2 a); the V-shaped ball valve core (12.2c) is arranged on the front side and the rear side of a main pipeline in the valve body (12.2a) and is respectively provided with a polytetrafluoroethylene gasket (12.2f) for corrosion prevention and sealing.
10. The system for end-user load prediction based on wireless connectivity and data mining technology as claimed in claim 9, wherein: when the terminal user load prediction system based on the wireless interconnection and data mining technology uses the centralized heating intelligent remote valve temperature control device, an adjusting algorithm meeting the following requirements is applied:
one is proportional flow regulation, and the specific steps are as follows:
the selection steps of the adjusting branch line are as follows in sequence:
1) all balance valves of the system account are fully opened, and all other valves on the water supply pipeline are also fully opened, so that the system operates under the working condition of over-flow;
2) the flow, namely the flow of each branch line, is directly checked by utilizing a balance valve calculation chart; or the flow value can be directly checked by utilizing a balance valve calculation chart according to the pressure difference between the front part and the rear part of the balance valve;
3) calculating the ratio x of the branch flowsi，
In the formula, i is the serial number of each branch line; n is the number of branches; giFor measured actual flow of the branch, m3/h；Gi' is the branch ideal flow, m3/h；
4) Selecting the maximum value x of the flow ratiomaxThe corresponding branch is the regulation branch. Arranging according to the magnitude sequence of the branch flow ratio, namely the sequential adjustment sequence of the branches;
the second is the regulation of branch lines, and the specific steps are as follows:
1) calculating the flow ratio of the branch line heat insulation users, and selecting the minimum value x of the flow ratiominThe corresponding hot user is a reference user; if branch A is the regulation branch, then xmax＝GA/GA'; in line A, if the flow rate ratio of the subscriber 3 is minimal, xmin＝GA3/GA3', user 3 is the reference user;
2) starting from the end user 1 of the regulating branch line A, regulating the balance valve F-A1 by using an intelligent instrument matched with the balance valve to regulate the flow ratio x of the user 1A1Adjusted to about 95% of the flow ratio of the reference user 3, i.e. xA1＝0.95(GA3/GA3')；
3) The balance valve F-A2 is adjusted to make the flow ratio x of the user 2A2Regulating flow to user 1The ratio being equal, i.e. xA2＝xA1；
4) Continuously taking the flow ratio of the user 1 as a reference value, and adjusting the users 3 and 4 at one time according to the same method of the step 3); the flow of user 1 will increase slightly for each adjustment of one user, which is normal;
5) according to the sequence of the branch flow ratio, adopting the same method to adjust other branches in turn;
and the third step is the adjustment between branch lines, and the specific steps are as follows:
1) measuring the flow ratio, x, of each branchA，xB，xC，xDTaking the minimum value as a reference ratio;
2) starting with the last branch, i.e. adjusting the balancing valves F-D, so that the flow ratio of branch D is adjusted to 95% of the reference value of the branch, if the reference branch is branch C, then x should be adjustedD＝0.95xC-0.95xmin；
3) In the same way, the balance valves F-C, F-B, F-A are sequentially adjusted to ensure that the flow ratio of each branch line is equal to the flow ratio of the tail-most branch line D;
4) if the branch lines belong to different areas in the same heating system, the branch lines in the same section are adjusted first, and the adjustment between the sections is performed, and the adjustment method is the same as the above.
Fourthly, the whole network adjustment comprises the following specific steps:
adjusting a total balance valve F of the heating system, wherein the total balance valve F is arranged on a water supply pipeline or a water return pipeline, so that the flow ratio of a branch line D at the tail end is equal to 1.0, and the regulation is carried out according to a consistent equal ratio maladjustment principle; after the adjustment, the flow of each branch line and each heat user of the heating system is always operated on the value of ideal flow or designed flow, and the whole network adjustment is finished.
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Inventor after: Pan Shiying Inventor after: Chen Fuzhong Inventor after: Xu Jun Inventor after: Ding Xin Inventor after: Yan Long Inventor before: Pan Shiying Inventor before: Chen Fuzhong Inventor before: Xu Jun Inventor before: Ding Xin |
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