CN115560430B - Cloud computing-based air conditioner model optimization method and system - Google Patents
Cloud computing-based air conditioner model optimization method and system Download PDFInfo
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- 238000005457 optimization Methods 0.000 title claims abstract description 23
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- 238000004378 air conditioning Methods 0.000 claims abstract description 44
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- 238000001816 cooling Methods 0.000 claims description 63
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/32—Responding to malfunctions or emergencies
- F24F11/38—Failure diagnosis
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
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- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
- F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
- F24F11/85—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using variable-flow pumps
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract
The scheme includes that a load prediction model, a device dynamic model and an optimization control related model are designed, an edge computing node is adopted to obtain the load prediction model and the device dynamic model from a cloud computing server to conduct real-time computation and control the operation of an air conditioning system, system operation data are reported to the cloud computing server, the cloud computing server iterates optimization model parameters through machine learning, and the optimized model is issued to the edge side for subsequent computation. The scheme can well establish a dynamically optimized air conditioning system model, and optimize the overall energy efficiency of the system under the condition of ensuring the least adverse end pressure; the scheme can also realize the timely update of the model and the self-evolution of the edge side model, thereby realizing long-term and good energy-saving control.
Description
Technical Field
The invention relates to the technical field of cloud-edge combined data processing and model optimization, in particular to an air conditioner model optimization method based on cloud edge calculation and a corresponding system thereof.
Background
The large public building adopts a cold water central air conditioner as a main part, and the aim of the cold water control system of the central air conditioner is to optimize the whole energy consumption on the premise of meeting the demand of the terminal load. When the end load changes, the running parameters such as the number of the refrigerating host, the refrigerating pump, the cooling pump and the cooling tower work table, water flow, temperature difference, working frequency and the like need to be adjusted in time, so that the cooling capacity reaches the balance of supply and demand.
In the prior art, because there is no accurate air conditioning model and a corresponding model dynamic optimization scheme, the existing control of the air conditioning system basically adopts fixed on-off time to control the central air conditioning system, so that the following problems exist in the prior art:
setting fixed startup and shutdown time, and setting reasonable startup time without considering the precooling amount to be processed when the external environment changes greatly; the energy efficiency curve is greatly deviated due to the reasons of installation, maintenance, long-term operation and the like and the equipment performance is declined, and the system cannot be accurately controlled; the group control system of the refrigerating machine room is generally implemented based on DDC or PLC, and is biased to the realization of a communication function and simple automatic control; because the local computing power is weaker, the building of an air conditioner model, the optimization of the model and the intelligent control algorithm of the model are not more involved, and complex big data analysis and artificial intelligent algorithms are difficult to expand and deploy, so that the model optimization and the system control of an air conditioner system are facilitated.
Disclosure of Invention
In view of the problems existing in the prior art, the invention provides an air conditioner model optimization method and system based on cloud computing.
In order to achieve the above purpose, the present invention specifically provides the following technical solutions:
in one aspect, the present invention provides an air conditioning model optimization system based on cloud computing, the system comprising:
an edge computing node subsystem and a cloud computing server connected with the edge computing node subsystem; the cloud computing server establishes a load prediction model and a device dynamic model;
the edge computing node subsystem acquires an updated load prediction model and an updated equipment dynamic model from the cloud computing server so as to optimize the control of the air conditioning system in real time;
the edge compute node subsystem includes:
the data acquisition module is used for acquiring the internet of things data of the air conditioning system in real time;
the meteorological data module is used for acquiring the atmospheric forecast data of the pertaining earth;
the fault diagnosis module is used for acquiring fault codes of the chiller, the cooling tower fan, the chilled water pump and the cooling water pump and generating an alarm signal when the alarm condition is met;
the model updating module is used for downloading the updated load prediction model and the equipment dynamic model from the cloud computing server so as to update the corresponding model at the edge side;
the operation decision module is used for carrying out load prediction based on the load prediction model so as to establish a starting-up time model; constructing a model of adding and subtracting machine time by establishing a cold machine load rate energy efficiency curve; based on the equipment dynamic model, establishing an equipment optimal operation combination model;
based on the equipment optimal operation combination model, the fan frequency of the cooling tower, the frequency of the chilled water pump and the frequency of the cooling water pump can be adjusted under the condition of meeting the least favorable loop pressure.
Preferably, the edge computing node subsystem further comprises a data storage module (edge node) which can be used for storing real-time data, control logs, alarm logs and other data information of the air conditioning system, and preferably, the data storage module (edge node) adopts an ordered database for storage. The stored data can be used as standby data in model optimization.
Preferably, the edge computing node subsystem further comprises a data encryption module for encrypting the communication uplink data, decrypting the downlink data and guaranteeing the data communication safety.
Preferably, the edge computing node subsystem further comprises a data synchronization module (edge node) for synchronizing air conditioning system operation data to the cloud server, and preferably, the data synchronization module (edge node) supports breakpoint continuous transmission.
Preferably, the cloud computing server acquires edge side data provided by the edge computing node subsystem to update a load prediction model and an equipment dynamic model.
Preferably, the cloud computing server includes:
the edge side management module is used for verifying the validity of the edge side;
the off-line calculation module is used for calculating a calculation task which requires a result in non-real time by adopting distributed map reduction;
the online computing module is used for computing tasks requiring real-time results to be obtained by distributed stream computing;
the algorithm engine module is used for carrying out iterative updating on the load prediction model and the equipment dynamic model;
and the index evaluation module is used for evaluating the running condition of the air conditioner model based on the index evaluation system.
Preferably, the cloud computing server further comprises a data synchronization module (cloud) for receiving edge side data, and preferably, the data synchronization module (cloud) supports breakpoint continuous transmission.
Preferably, the cloud computing server further comprises a data storage module (cloud) for storing the received edge side data, and preferably, the data storage module (cloud) adopts a distributed database for storage so as to support subsequent big data application.
Preferably, the load prediction model is:
wherein:
for building load at time t->
Is the outdoor dry bulb temperature at the moment t +.>
Outdoor relative humidity at time t->
Is the indoor temperature->
As constants, a1, a2, a3, a4, a5, a6, a7, a8 are coefficients corresponding to the respective items.
Preferably, the equipment dynamic model comprises a water chilling unit energy consumption model, a water pump energy consumption model, a cooling tower energy consumption model and a water chilling unit load rate energy efficiency model.
Preferably, the water chiller energy consumption model is:
wherein:
the energy consumption of the water chilling unit under the working condition is +.>
The water outlet temperature of chilled water of a water chilling unit is +.>
For the temperature of the cooling tower water outlet->
For correction value->
，/>
，/>
，/>
，/>
，/>
，/>
Is the coefficients.
Preferably, the water pump energy consumption model is:
wherein:
is the energy consumption of the water pump>
For the frequency of the water pump->
Is constant (I)>
，/>
，/>
Is the coefficients.
Preferably, the cooling tower energy consumption model is:
wherein,,
for the energy consumption of the cooling tower->
For cooling tower fan frequency, < >>
Is the outdoor dry bulb temperature +.>
For outdoor relative humidity, < >>
For the water inlet temperature of the cooling tower, < > is->
For the temperature of the cooling tower water outlet->
Is constant (I)>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
Is the coefficients.
Preferably, the water chilling unit load rate energy efficiency model is as follows:
wherein:
for the energy efficiency ratio of the water chilling unit, PLR is the load rate of the water chilling unit, and the ratio is +>
Is constant (I)>
，/>
Is the coefficients.
Preferably, in the load prediction model and the equipment dynamic model, the internet of things data and/or the equipment operation steady-state parameter data at the edge side are obtained, and regression is performed on the load prediction model or the equipment dynamic model so as to determine various parameters of the load prediction model or the equipment dynamic model.
Preferably, the internet of things data comprises chilled water supply and return temperature, cooling water supply and return temperature, a chiller load rate, chilled water supply and return temperature set values, water pump frequency, fan frequency, pressure, flow, outdoor temperature and humidity, indoor temperature and humidity, electricity consumption, equipment start and stop, valve opening, chilled water outlet temperature of a chiller unit and the like.
Preferably, in the operation decision module, the optimal operation combination model of the device is:
wherein,,
for air conditioning system energy efficiency->
For refrigerating capacity>
For cold energy, the person is provided with->
In order to achieve a cooling tower energy consumption,
for the energy consumption of the chilled water pump->
And the energy consumption of the cooling water pump is reduced.
Preferably, in order to make
Optimally, should make +.>
The smallest, i.e. the EER is as large as possible.
Preferably, in the operation decision module, the start-up time model is:
wherein,,
for the start-up time->
For business hours, +.>
For building load at time t->
Rated refrigerating capacity for chiller>
The experience time required for starting the cooling tower, the cooling water pump and the chilled water pump.
Preferably, in the operation decision module, the determining mode of the adding and subtracting machine time model is as follows:
and establishing a water chiller load rate energy efficiency curve through a water chiller load rate energy efficiency model, and selecting the load rate corresponding to the maximum point of the change of the rising and falling slopes of the energy efficiency in the curve as the opportunity of adding and subtracting. When the load of the air conditioning system rises, the load rate of the cold machine is larger than the load rate (stable for a certain time) corresponding to the maximum point of the energy efficiency falling slope in the curve and is used as the machine adding time, and when the load of the air conditioning system falls, the load rate of the cold machine is smaller than the load rate (stable for a certain time) corresponding to the maximum point of the energy efficiency rising slope in the curve and is used as the machine subtracting time. Here, the stabilization time is a preset time length, and may be set in advance.
Preferably, in the operation decision module, based on the equipment optimal operation combination model, the optimal control of the chiller can be further realized, and the specific mode is as follows:
starting up, namely when a plurality of water chilling units with different powers exist, predicting the load time by time through a load prediction model analysis to obtain a load peak value
Selecting rated refrigerating capacity +.>
The cold machine matched with the integrated operation time length is selected as the shortest cold machine when a plurality of cold machines are matched:
adding: when the load of the air conditioning system rises, the load of the cold machine is larger than the corresponding load rate when the energy efficiency decline slope in the energy efficiency curve of the load rate of the cold machine is maximum (the cold machine is stabilized for a certain time, namely stabilized for a preset time), and the load peak value is obtained by analyzing the future time-by-time predicted load through a load prediction model
Subtracting the current cold load already running +.>
Selecting rated refrigerating capacity +.>
And selecting the cold machine with the shortest accumulated operation duration when the cold machine is matched with the cold machines with the increased cold quantity:
and (3) reducing: when a plurality of running chillers exist and the load of the air conditioning system is reduced, the chiller load rate is smaller than the load rate corresponding to the maximum point of the energy efficiency rising slope in the chiller load rate energy efficiency curve (the chiller is stabilized for a certain time, namely, stabilized for a preset time period), and the chiller with the longest accumulated running time period is turned off.
Preferably, when a switch between the two machines is required: when a plurality of water chilling units with different powers exist and only one running chiller is currently operated, when the load of the air conditioning system is reduced and the load rate of the chiller is smaller than the load rate corresponding to the energy efficiency rising point in the energy efficiency curve of the load rate of the chiller (the stabilizing is performed for a certain time, namely, the stabilizing is performed for a preset time period), if the running chiller and the related equipment are firstly closed when the rated power of the chiller is smaller than that of the running chiller, the running chiller and the related equipment are then opened.
Preferably, when gap operation is required: the load rate of the cold machine is smaller than 40% (the threshold value can be set according to the control requirement of the system, and the like, and is stable for a certain time, namely a preset time length), and the predicted load is larger than zero, and the cold machine is prevented from surging by adopting a mode of closing for a period of time and then opening for a period of time to run intermittently.
Preferably, when a shutdown is required: the load rate of the cooling machine is less than 40% (the threshold value can be set according to the control requirement of the system, and the like, the cooling machine is stabilized for a certain time, namely, a preset time period is stabilized), and the cooling machine is shut down when the predicted load is zero.
Preferably, in the operation decision module, based on the equipment optimal operation combination model, optimal control of the chilled water pump can be further realized, and the specific mode is as follows:
the running number of the chilled water pumps is in one-to-one correspondence with that of the chillers.
The chilled water system is operated with differential pressure control to actually check and set the upper and lower limits of the most unfavorable loop differential pressure. In the adjustable pressure difference interval, in order to make
Optimally, selecting an operable set according to a COP (ratio of refrigerating capacity to electricity consumption) curve of the chiller, and calculating and selecting energy consumption of the chiller and the chilled water pump>
And adjusting the running frequency of the chilled water pump according to the working condition with the minimum sum.
Preferably, in the operation decision module, based on the equipment optimal operation combination model, the optimal control of the cooling water pump can be further realized, and the specific mode is as follows:
the cooling water system is operated according to temperature difference control, when the frequency of the chilled water pump is changed, the cooling water pump adjusts the frequency to change, so that the cooling water flow is larger than the chilled water and a proper multiplying power ratio is maintained, and the multiplying power ratio can be 1.2 times, for example, namely:
wherein the method comprises the steps of
This cooling water flow>
Is the flow of chilled water.
Preferably, in the operation decision module, based on the equipment optimal operation combination model, the optimal control of the cooling tower can be further realized, and the specific mode is as follows:
starting a cooling tower, starting all fans under the cooling tower, and adjusting the frequencies to be consistent;
when the fan frequency of the cooling tower is larger than 45Hz (the threshold value can be set, the cooling tower is stable for a period of time, namely, a preset time period is stabilized), and a cooling tower is additionally operated;
when the fan frequency of the cooling tower is less than 30Hz (the threshold value can be set, the cooling tower is stable for a period of time, namely, a preset time period is stabilized), and one cooling tower is reduced to operate;
the cooling water system is operated according to temperature difference control, an operable set is selected according to a COP (ratio of refrigerating capacity to electricity consumption) curve of the cooling machine, and the energy consumption of the cooling machine and the cooling tower is calculated and selected
And (5) adjusting the fan frequency of the cooling tower according to the working condition of the minimum sum.
On the other hand, the invention also provides an air conditioner model optimization method based on cloud computing, which can be applied to the air conditioner model optimization system based on cloud computing, and comprises the following steps:
s1, establishing a load prediction model and a device dynamic model in a cloud computing server, and adopting historical steady-state data to carry out regression to determine each coefficient of each model;
s2, the edge computing node subsystem updates a load prediction model and a device dynamic model from the cloud computing server;
s3, the edge computing node subsystem predicts the load based on the load prediction model, and establishes a startup time model;
s4, the edge computing node subsystem establishes a cold machine load rate energy efficiency curve and establishes a machine adding and subtracting time model;
s5, the edge computing node subsystem establishes an equipment optimal operation combination model based on the equipment dynamic model, and adjusts the fan frequency of the cooling tower, the frequency of the chilled water pump and the frequency of the cooling water pump under the condition of meeting the least favorable loop pressure based on the equipment optimal operation combination model;
and S6, continuously collecting steady-state data uploaded by the edge computing node subsystem by the cloud computing server, and regressively optimizing a load prediction model and a device dynamic model through a machine learning algorithm.
Preferably, in S4, a chiller load rate energy efficiency curve is established, and the chiller load rate energy efficiency curve is obtained through a chiller load rate energy efficiency model.
Preferably, in S5, the specific device dynamic model used is: a water chilling unit energy consumption model, a water pump energy consumption model and a cooling tower energy consumption model.
Compared with the prior art, the technical scheme of the invention has the advantages that the mathematical model is built for the air conditioning system, the real-time calculation is carried out on the edge side, and the optimization model in the running and control process of the air conditioning system is provided through the optimization and updating of the model, so that the starting time of the air conditioning system is optimized, the water outlet temperature of the water chilling unit is reset, the load change is predicted, the opportunity of adding and subtracting machines is optimized, the optimal running combination of energy efficiency is matched, and the fan frequency of the cooling tower, the frequency of the chilled water pump and the frequency of the cooling water pump are adjusted to ensure that the overall energy efficiency of the system is optimal, and the energy-saving control of the large-scale air conditioning system can be well realized. And meanwhile, uploading edge side data to the cloud, iterating each mathematical model parameter through a machine learning algorithm, and transmitting an optimization result to the edge side to realize self-evolution of the system, so that the optimal energy-saving target is realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a block diagram of a system architecture according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the steps of the method of the present invention;
FIG. 3 is a flow chart of the operation of the present invention;
FIG. 4 is an energy efficiency curve analysis chart of an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some, but not all, of the embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It will be appreciated by those of skill in the art that the following specific embodiments or implementations are provided as a series of preferred arrangements of the present invention for further explanation of the specific disclosure, and that the arrangements may be used in conjunction or association with each other, unless it is specifically contemplated that some or some of the specific embodiments or implementations may not be associated or used with other embodiments or implementations. Meanwhile, the following specific examples or embodiments are merely provided as an optimized arrangement, and are not to be construed as limiting the scope of the present invention.
Referring to fig. 1, in an preferred embodiment, the cloud computing-based air conditioning model optimization system provided by the invention may be set as follows:
1. and the edge computing node subsystem acquires the latest versions of the prediction model and the equipment dynamic model from the cloud computing server, updates the edge side algorithm model and performs real-time computation to realize the optimal control of the air conditioning system.
In a preferred implementation of this embodiment, the device dynamic model includes a chiller energy consumption model, a water pump energy consumption model, a cooling tower energy consumption model, a chiller load factor energy efficiency model, and the like.
The edge computing node subsystem may include a data acquisition module, a meteorological data module, a fault diagnosis module, a model update module, an operational decision module, a data storage module (edge node), a data encryption module, a data synchronization module (edge node). Wherein:
the data acquisition module acquires the things data such as chilled water supply and return temperature, cooling water supply and return temperature, chiller load rate, chilled water supply temperature set value, water pump frequency, fan frequency, pressure, flow, outdoor temperature and humidity, indoor temperature and humidity, electricity consumption, chilled water outlet temperature of a chiller unit and the like in real time.
And the weather data module is used for acquiring weather forecast data of the city on a day-by-day basis, and the temperature and the humidity in the weather forecast data are used for forecasting the air conditioner load.
The fault diagnosis module acquires fault codes of the chiller, the cooling tower fan, the chilled water pump and the cooling water pump, and can be further arranged to generate or send an alarm signal under the condition that the alarm condition is met.
And the model updating module is used for downloading the load prediction model and the latest version algorithm model of the equipment dynamic model from the cloud computing server so as to update the edge side local algorithm model.
And the operation decision module is used for carrying out load prediction based on the load prediction model, and establishing a starting-up time model based on the predicted load so as to determine the optimized starting-up time. The cold machine runs with the maximum load after the start-up, the start-up time can be optimally shortened through the establishment of a start-up time model, and the energy consumption waste caused by maintaining idle time when the operation is performed for a long time after the completion of the start-up by setting fixed start-up time is avoided, so that the aim of saving the energy consumption is achieved; based on the water chilling unit load rate energy efficiency model, the relation between the energy efficiency and the load rate can be determined, so that the predicted load and the actual load of the air conditioning system are compared according to the actual comparison, whether the system needs to be powered on or powered off is determined, a power on/off time model is built, the power on/off time is optimized, and the purpose of saving energy consumption is achieved by improving the energy efficiency of the water chilling unit; based on the water chilling unit energy consumption model, the water pump energy consumption model and the cooling tower energy consumption model, an equipment optimal operation combination model is established, and based on the equipment optimal operation combination model, the cooling tower fan frequency, the chilled water pump frequency and the cooling water pump frequency are adjusted under the condition that the least adverse loop pressure is met, so that corresponding energy efficiency is optimal in actual air conditioning system control, and the purpose of saving energy consumption is achieved.
The data storage module (edge node) stores data information such as real-time data, control logs, alarm logs and the like of the air conditioning system, and preferably, the data storage module (edge node) adopts a time sequence database for storage.
And the data encryption module is used for encrypting the communication uplink data and decrypting the downlink data, so that the data communication safety is ensured.
And the data synchronization module (edge node) synchronizes the operation data and parameters of the air conditioning system to the cloud server, and preferably supports breakpoint continuous transmission.
2. The cloud computing server is used for acquiring edge side data provided by the edge computing node subsystem, generating air conditioning system indexes such as refrigerating capacity, heat dissipation capacity, electricity consumption, heat dissipation capacity-to-refrigerating capacity ratio heat dissipation rate, refrigerating capacity-to-electricity consumption ratio energy efficiency ratio EER (namely air conditioning system energy efficiency), outdoor temperature, indoor temperature, chilled water supply and return water temperature and cooling water supply and return water temperature change curves with time, so as to conveniently analyze and evaluate the effect of the model, and performing iterative optimization of the model in a machine learning mode and the like so as to realize optimization of each model. In addition, the method is also used for building a load prediction model and a device dynamic model.
The cloud computing server comprises an edge side management module, a data synchronization module (cloud), a data storage module (cloud), an offline computing module, an online computing module, an algorithm engine module and an index evaluation module.
The edge side management module is used for verifying the identity of the edge side, and after the identity is verified to be legal, the data of the edge side can be accessed to the cloud server.
And the data synchronization module (cloud) is used for receiving the edge side data, supporting breakpoint continuous transmission and guaranteeing that the data is not repeated.
The data storage module (cloud) stores the received edge side data, and preferably, the data storage module (cloud) uses a distributed database for storage so as to support subsequent big data application.
And the off-line calculation module is used for performing mapreduce calculation on a calculation task requiring a result in non-real time by adopting a distributed task, wherein the calculation task requiring the result in non-real time comprises same-time data processing, steady-state data processing, model regression and the like.
And the online computing module adopts distributed stream computing for computing tasks requiring real-time computing results, such as real-time computing of various index data.
And the algorithm engine module is used for iterating the load prediction model and the equipment dynamic efficiency model through machine learning, updating the parameters of the two models and realizing the updating of the model.
The index evaluation module is used for evaluating the energy efficiency of the air conditioner model by establishing an air conditioner system index evaluation system, and data mainly related to the energy efficiency evaluation comprise system indexes and equipment indexes. The system indexes comprise: refrigerating capacity, heat dissipation capacity, electricity consumption, heat dissipation capacity and refrigerating capacity ratio, EER (energy efficiency of an air conditioning system) and outdoor temperature, indoor temperature, chilled water supply and return water temperature, cooling water supply and return water temperature change curves with time and the like; the equipment indexes comprise: refrigerating capacity, electricity consumption, COP (coefficient of performance) of the ratio of the refrigerating capacity to the electricity consumption, chilled water supply and return water temperature, cooling water supply and return water temperature and the like. The actual running conditions of the air conditioning system and the equipment can be estimated through the running indexes of the system and the equipment so as to further estimate the model effect. The specific evaluation method can be based on the above indexes, and an evaluation comparison method well known in the art is adopted to realize the evaluation, which is not described herein.
Further, as shown in fig. 2, the working method of the system is as follows:
s1, a cloud end (namely a cloud computing server end) establishes a load prediction model and a device dynamic model.
And establishing a load prediction model, a water chiller energy consumption model, a water pump energy consumption model, a cooling tower energy consumption model and a water chiller load rate energy efficiency model at the cloud, carrying out regression by adopting factory performance data of equipment and historical steady-state data, and determining various coefficients in the model.
Specifically, in the present preferred embodiment, the present invention provides preferred models, each model being as follows:
the load prediction model is as follows:
wherein:
for building load at time t->
Is the outdoor dry bulb temperature at the moment t +.>
Outdoor relative humidity at time t->
Is the indoor temperature->
As constants, a1, a2, a3, a4, a5, a6, a7, a8 are coefficients corresponding to the respective items.
The energy consumption model of the water chilling unit is as follows:
wherein:
the energy consumption of the water chilling unit under the working condition is +.>
The water outlet temperature of chilled water of a water chilling unit is +.>
For the temperature of the cooling tower water outlet->
For correction value->
，/>
，/>
，/>
，/>
，/>
，/>
Is the coefficients.
The energy consumption model of the water pump is as follows:
wherein:
is the energy consumption of the water pump>
For the frequency of the water pump->
Is constant (I)>
，/>
，/>
Is the coefficients.
The cooling tower energy consumption model is as follows:
wherein,,
for the energy consumption of the cooling tower->
For cooling tower fan frequency, < >>
Is the outdoor dry bulb temperature +.>
For outdoor relative humidity, < >>
For the water inlet temperature of the cooling tower, < > is->
For the temperature of the cooling tower water outlet->
Is constant (I)>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
，/>
Is the coefficients.
The water chilling unit load rate energy efficiency model is as follows:
wherein:
for the energy efficiency ratio of the water chilling unit, PLR is the load rate of the water chilling unit, and the ratio is +>
Is constant (I)>
，/>
Is the coefficients.
It should be noted that, the foregoing models are preferred models provided by the present invention, and those skilled in the art may also replace the existing models disclosed in the art, and may also be applicable to the solution of the present invention, which is not described herein.
S2, updating a load prediction model and each equipment dynamic model from the cloud by the edge node (namely an edge computing node subsystem end).
And downloading the latest load prediction model, the water chilling unit energy consumption model, the water pump energy consumption model, the cooling tower energy consumption model and the water chilling unit load rate energy efficiency model from the cloud at the edge side.
S3, the edge node predicts the load based on the load prediction model, and establishes a starting-up time model, so that the starting-up time is calculated.
In a preferred embodiment, matching the chiller and start-up time models may be performed as follows:
wherein,,
for the start-up time->
For business hours, +.>
For the building load at time t (i.e. the starting time can be determined by predicting the building load at time t by means of a load prediction model),>
rated refrigerating capacity for chiller>
The experience time required for starting the cooling tower, the cooling water pump and the chilled water pump. Here, preference is given to->
Can be predicted by a load prediction modelAnd predicting the building load at the moment t so as to determine the starting time.
The method for determining the matching cold machine is as follows:
when a plurality of water chilling units with different powers exist, predicting the load time by time through the load prediction model analysis to obtain a load peak value
Selecting rated refrigerating capacity +.>
When the cooling machines are matched with the cooling machines, the cooling machine with the shortest accumulated operation duration is selected, and the requirements of cooling machine matching are as follows:
and S4, the edge computing node subsystem establishes a cold machine load rate energy efficiency curve based on a cold water machine set load rate energy efficiency model, establishes an adding and subtracting machine time model, and takes the load rate corresponding to the maximum point of the slope change of the energy efficiency ascending and descending curve in the model as adding and subtracting machine time.
Adding: when the load of the air conditioning system rises, the load of the cold machine is larger than the corresponding load rate when the energy efficiency decline slope in the energy efficiency curve of the load rate of the cold machine is maximum (the cold machine is stabilized for a certain time, namely stabilized for a preset time), and the load peak value is obtained by analyzing the future time-by-time predicted load through a load prediction model
Subtracting the current cold load already running +.>
Selecting rated refrigerating capacity +.>
And selecting the cold machine with the shortest accumulated operation duration when the cold machine is matched with the cold machines with the increased cold quantity:
and (3) reducing: when a plurality of running chillers exist and the load of the air conditioning system is reduced, the chiller load rate is smaller than the load rate corresponding to the maximum point of the energy efficiency rising slope in the chiller load rate energy efficiency curve (the chiller is stabilized for a certain time, namely, stabilized for a preset time period), and the chiller with the longest accumulated running time period is turned off.
S5, an edge node establishes an equipment optimal operation combination model based on an equipment dynamic model, and adjusts the fan frequency of a cooling tower, the frequency of a chilled water pump and the frequency of a cooling water pump under the condition of meeting the least favorable loop pressure based on the equipment optimal operation combination model so as to optimize the energy efficiency of an air conditioning system, and in a preferred embodiment provided by the invention, as shown in fig. 3, the optimization mode of the optimal operation combination model comprises the following steps:
the optimal mode for optimizing the adding and subtracting time of the cold machine is as follows: as shown in fig. 4, a chiller energy efficiency and load rate curve is established through a chiller energy efficiency load rate model, the maximum slope change point of the energy efficiency rising and falling curve is selected as an adding and subtracting opportunity, when the load of the air conditioning system rises and the chiller load rate is greater than the falling point, the adding and subtracting opportunity is used, at this moment, after a certain period of stability, the adding and subtracting opportunity is used, when the load of the air conditioning system decreases and the chiller load rate is less than the rising point, and likewise, the subtracting and subtracting opportunity is used after a certain period of stability. Wherein, in the water chilling unit load rate energy efficiency model
The value of (2) can be determined by the energy efficiency of the water chiller>
Calculated, wherein->
For refrigerating capacity>
Is energy-saving for cooling machine.
In a preferred embodiment, the cooling tower, cooling water pump, chilled water pump operating frequency can be optimized by system energy efficiency based on the plant optimal operating combination model:
wherein,,
for air conditioning system energy efficiency->
For refrigerating capacity>
For cold energy, the person is provided with->
In order to achieve a cooling tower energy consumption,
for the energy consumption of the chilled water pump->
And the energy consumption of the cooling water pump is reduced.
In order to make
Optimally, should make +.>
Minimum, selecting an operable set according to a chiller COP curve, and calculating and selecting energy consumption of a chiller and a chilled water pump>
And the minimum sum corresponds to a working condition, and the running frequency of the chilled water pump can be adjusted based on the working condition.
When the frequency of the chilled water pump is changed, the cooling water pump adjusts the frequency to change so as to enable the flow of the cooling water
Greater than chilled water flowQuantity->
And maintains a ratio which we can set to, for example, 1.2 times, i.e.:
selecting an operable set according to the COP curve of the chiller, and calculating and selecting the energy consumption of the chiller and the cooling tower
And (5) adjusting the fan frequency of the cooling tower according to the working condition of the minimum sum.
And S6, continuously collecting steady-state data uploaded by the edge nodes by the cloud computing server, and regressively optimizing a load prediction model and a device dynamic model through a machine learning algorithm.
The cloud computing server continuously collects steady-state data uploaded by the edge nodes, and the models can be continuously updated based on historical data through a machine learning algorithm regression load prediction model, a chiller load rate energy efficiency model, a chiller energy consumption model, a water pump energy consumption model and a cooling tower energy consumption model, so that the cloud computing server is suitable for air conditioning systems under different working conditions.
It should be noted that the above steps are only convenient for explaining the working mechanism of the present invention, and have no logical precedence relationship, if no substantial innovation is provided, only the sequence of the execution steps is changed, and the steps should be considered as falling within the protection scope of the present invention.
In yet another embodiment, the inventive aspects may be implemented by means of an apparatus, which may comprise corresponding modules performing each or several of the steps of the various embodiments described above. Thus, each step or several steps of the various embodiments described above may be performed by a respective module, and the electronic device may include one or more of these modules. A module may be one or more hardware modules specifically configured to perform the respective steps, or be implemented by a processor configured to perform the respective steps, or be stored within a computer-readable medium for implementation by a processor, or be implemented by some combination.
The device may be implemented using a bus architecture. The bus architecture may include any number of interconnecting buses and bridges depending on the specific application of the hardware and the overall design constraints. The bus connects together various circuits including one or more processors, memories, and/or hardware modules. The bus may also connect various other circuits such as peripherals, voltage regulators, power management circuits, external antennas, and the like.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiment of the present invention. The processor performs the various methods and processes described above. For example, method embodiments in the present solution may be implemented as a software program tangibly embodied on a machine-readable medium, such as a memory.
Logic and/or steps represented in the flowcharts or otherwise described herein may be embodied in any readable storage medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. Cold water central air conditioning optimizing control system based on cloud limit calculation, characterized in that, the system includes: an edge computing node subsystem and a cloud computing server connected with the edge computing node subsystem;
the edge computing node subsystem acquires an updated load prediction model and an updated equipment dynamic model from the cloud computing server so as to optimize the control of the air conditioning system in real time;
the edge compute node subsystem includes:
the data acquisition module is used for acquiring the internet of things data of the air conditioning system in real time;
the meteorological data module is used for acquiring the atmospheric forecast data of the pertaining earth;
the fault diagnosis module is used for acquiring fault codes of the chiller, the cooling tower fan, the chilled water pump and the cooling water pump and alarming when the alarm condition is met;
the model updating module is used for downloading the updated load prediction model and the equipment dynamic model from the cloud computing server so as to update the corresponding model at the edge side;
the operation decision module is used for carrying out load prediction based on the load prediction model so as to determine the starting time; by establishing a cold machine load rate energy efficiency curve, selecting the load rate corresponding to the maximum point of the change of the rising and falling slopes of the energy efficiency in the curve as the time for adding and subtracting the machine; based on the equipment dynamic model, determining an optimal operation combination of equipment, and adjusting the fan frequency of a cooling tower, the frequency of a chilled water pump and the frequency of a cooling water pump to enable the energy efficiency to be optimal under the condition of meeting the least favorable loop pressure;
the load prediction model in the operation decision module is as follows:
in the operation decision module, the mode of determining the optimal equipment operation combination is as follows:
in the operation decision module, the starting time is determined in the following manner:
2. The system of claim 1, wherein the cloud computing server obtains edge side data provided by the edge computing node subsystem to update a load prediction model, a device dynamic model.
3. The system of claim 1, wherein the cloud computing server comprises:
the edge side management module is used for verifying the validity of the edge side;
the off-line calculation module is used for calculating a calculation task which requires a result in non-real time by adopting distributed map reduction;
the online computing module is used for computing tasks requiring real-time results to be obtained by distributed stream computing;
the algorithm engine module is used for carrying out iterative updating on the load prediction model and the equipment dynamic model;
and the index evaluation module is used for analyzing the running condition of the air conditioning system based on the index evaluation system.
4. The system of claim 1, wherein the plant dynamic model comprises a chiller energy consumption model, a water pump energy consumption model, a cooling tower energy consumption model, a chiller load factor energy efficiency model.
5. The system according to claim 1, wherein in the load prediction model and the equipment dynamic model, the internet of things data and the equipment operation steady-state parameter data on the edge side are obtained, and regression is performed on the load prediction model or the equipment dynamic model to determine each parameter of the load prediction model or the equipment dynamic model.
6. The system of claim 4, wherein in the operational decision module, the adding and subtracting opportunities are determined in the following manner:
establishing a chiller load rate energy efficiency curve through a chiller load rate energy efficiency model, and selecting the load rate corresponding to the maximum point of the change of the rising and falling slopes of the energy efficiency in the curve as the adding and subtracting opportunity: when the load of the air conditioning system rises, the load rate of the cold machine is larger than the load rate corresponding to the maximum point of the energy efficiency falling slope in the curve and is stable for a preset time, and the load rate is used as the machine adding time; when the load of the air conditioning system is reduced and the load rate of the chiller is smaller than the load rate corresponding to the maximum point of the energy efficiency rising slope in the curve and is stable for a preset time, the load rate is used as the time for shutting down the chiller.
7. The cloud computing-based cold water central air conditioner optimization control method is characterized by being applied to the cloud computing-based cold water central air conditioner optimization control system according to any one of claims 1-6, and comprises the following steps:
s1, establishing a load prediction model and a device dynamic model in a cloud computing server, and adopting historical steady-state data to carry out regression to determine each coefficient of each model;
s2, the edge computing node subsystem updates a load prediction model and a device dynamic model from the cloud computing server;
s3, the edge computing node subsystem predicts the load based on the load prediction model, and calculates the starting time;
s4, the edge computing node subsystem establishes a cold machine load rate energy efficiency curve, and selects the load rate corresponding to the maximum point of the slope change of the energy efficiency ascending and descending curve in the curve as the machine adding and subtracting time;
s5, the edge computing node subsystem determines an optimal operation combination of equipment based on the equipment dynamic model, and adjusts the fan frequency of the cooling tower, the frequency of the chilled water pump and the frequency of the cooling water pump to enable the energy efficiency to be optimal under the condition of meeting the least favorable loop pressure;
and S6, continuously collecting steady-state data uploaded by the edge computing node subsystem by the cloud computing server, and regressively optimizing a load prediction model and a device dynamic model through a machine learning algorithm.
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