CN111520812B - Method and system for estimating room temperature of heat supply residents - Google Patents
Method and system for estimating room temperature of heat supply residents Download PDFInfo
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Abstract
The invention belongs to the field of estimation of room temperature of heat-supplying residents, and provides a method and a system for estimating the room temperature of the heat-supplying residents. The method for estimating the room temperature of the heat supply residents comprises the steps of collecting the return water temperature and the outdoor temperature corresponding to all the heat supply residents at the current moment in real time; estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature; the construction process of the heat supply household room temperature estimation model is as follows: acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period; rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents; and calculating an average set of effective heat supply temperature data of the heat supply residents in a historical preset time period, constructing a linear regression equation and calculating a correlation coefficient of the linear regression equation.
Description
Technical Field
The invention belongs to the field of estimation of room temperature of heating residents, and particularly relates to a method and a system for estimating the room temperature of the heating residents.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The heat supply enterprises need householders' room temperature data to monitor the heating quality of the residential area and guide the selection of the operation parameters of the heat exchange stations of the residential area during the heating operation. Under an ideal condition, room temperature acquisition equipment is installed in each household, and room temperature data is transmitted to a monitoring center of a heating power company in real time, so that heating enterprises can determine district heating parameters such as circulation flow, water supply temperature and the like according to the room temperature of each household, and the aims of saving energy, reducing consumption and improving economic benefit are achieved on the premise of ensuring the heating quality of residents. In actual operation, however, such ideal operation is difficult to achieve due to economic budget. Even if the budget allows, there are various unsatisfactory points in actual operation and use. The method is characterized in the following three aspects: 1) the installation is difficult: some, and even most, households refuse to install room temperature collection equipment for various reasons. The active power supply equipment needs to take power at home of a household, and is complex to install; battery powered devices require holes to be punched in the wall to prevent removal, which can be aesthetically undesirable. 2) Equipment moving and dismantling: an installed equipment resident may remove or move due to privacy concerns, indoor aesthetics, etc., resulting in data disruption and distortion. 3) The equipment fault is difficult to overhaul: because the house entry is relatively complicated, the equipment is difficult to overhaul in case of failure, and the psychology of aversion of the house is also easy to be incurred.
The inventor finds that the conventional related conjecture technology generally predicts the average room temperature or the average return water temperature of the whole community or the whole building, but due to the difference of building structures (including a heat preservation structure, the direct sunlight degree and the like), the using habits of residents (including window opening and the like), and the like, the prediction difference among different residents is huge, and the distortion is larger than the actual situation. And other parts of the equipment are unstable in transmission or are removed by the user to stop uploading. These uncontrollable factors all cause great difficulties in the fine control of the device according to the room temperature.
Disclosure of Invention
In order to solve the problems, the invention provides a method and a system for estimating the room temperature of a heat supply resident, which are used for modeling the room temperature of a resident, realizing accurate room temperature prediction by eliminating invalid data and updating a model according to the latest data uploaded every day, and providing reliable basis for fine regulation and control of the resident.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the present invention provides a method for estimating a room temperature of a hot resident.
A method for estimating the room temperature of a hot resident comprises the following steps:
acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
As an embodiment, the linear regression equation of the estimation model of the room temperature of the hot residents is as follows: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
In one embodiment, the invalid heating temperature data sets of the residents in the historical preset time period are rejected according to the indoor temperature, and the rejection is as follows:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
In one embodiment, the invalid heating temperature data sets of the residents in the historical preset time period are rejected according to the return water temperature, and the rejection is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgWhen it is determined that there is a corresponding timeThe carved backwater temperature is abnormal data and is removed; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
A second aspect of the invention provides a system for estimating a room temperature of a hot resident.
A system for estimating a room temperature of a hot resident, comprising:
an acquisition module for: acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
an estimation module to: estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
As an embodiment, the linear regression equation of the estimation model of the room temperature of the hot residents is as follows: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
As an embodiment, in the estimation module, the invalid heating temperature data set of the household within the historical preset time period is rejected according to the indoor temperature, and the rejection is performed according to:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
As an embodiment, in the estimation module, the invalid heating temperature data set of the household within the historical preset time period is rejected according to the return water temperature, and the rejection is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
A third aspect of the invention provides a computer-readable storage medium.
A computer-readable storage medium, on which a computer program is stored which, when executed by a processor, carries out the steps of:
receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
A fourth aspect of the invention provides a computer apparatus.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps when executing the program of:
receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
The invention has the beneficial effects that:
because the heating has hysteresis, and the influence of the heating on the room temperature is delayed, the average return water temperature and the last average return water temperature are used as the calculation basis, the modeling is carried out aiming at the room temperature of the home terminal, the invalid data are removed, and the model is updated according to the latest data uploaded every day, so that the accurate room temperature prediction is realized, and the reliable basis can be provided for the refined regulation and control of the home terminal.
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The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
FIG. 1 is a flow chart of a method for estimating a room temperature of a hot resident according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for estimating the room temperature of a hot resident according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a system for estimating a room temperature of a hot-dwelling according to an embodiment of the invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
Fig. 1 shows a method for estimating a room temperature of a hot-dwelling unit according to the embodiment, which includes:
s101: acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
s102: and estimating the average indoor temperature of all the heat-supply residents by using the heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature.
As shown in fig. 2, the construction process of the estimation model of the room temperature of the hot residents is as follows:
step (a): acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
step (b): rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
step (c): and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
In the embodiment, regression analysis is performed according to the existing historical data, and the parameters of the regression equation can be dynamically updated for the new data uploaded in real time, so that prediction is more accurate; the real-time parameter updating mode is more necessary for residents with unstable and incomplete data transmission, and the prediction is more accurate along with the accumulation of data quantity.
The invalid data mainly comprises the following three types:
1. invalid data when the room temperature is less than a preset minimum room temperature threshold value: generally, the temperature is sharply reduced in a short time due to the self-windowing heat dissipation of residents; when the user closes the window, the temperature rises along with the window;
2. invalid data when the room temperature is greater than a preset maximum room temperature threshold value: generally, sunlight is directly emitted to equipment at certain moments in the day due to the installation position of a room temperature collector, so that the detected temperature is too high, and when the sunlight is not directly emitted any more, the temperature is reduced;
3. invalid data when the return water temperature is less than a preset minimum return water temperature threshold value: generally, the temperature rises when heating is recovered due to the fact that a user closes a valve autonomously, a filter is blocked, heating power is abnormally stopped, and the like.
The following preset time period is taken as an example of 24 hours:
the method comprises the following steps of eliminating invalid heat supply temperature data sets of all residents within a preset time period by taking the indoor temperature and the return water temperature as the basis, and obtaining the valid heat supply temperature data sets of all the residents:
the following is based on the indoor temperature:
T1＝t1
Tn＝kTn-1+(1-k)tn n>1
k is a preset prediction parameter and is selected to be between 0.9 and 0.95;
tnuploading the room temperature value for the nth real uploading room temperature value;
Tnis a weighted average of the first n room temperature values;
wherein if the actual temperature value uploaded by the above calculation is more than 24 hours from the last value, the calculation is restarted.
Order to
TowDetermining the temperature difference value for windowing, typically 2 ℃;
Towjudging the temperature difference value for closing the window, wherein the typical value is 1 ℃;
Towthe temperature difference value is judged for the beginning of illumination, typically 3 ℃;
Tssjudging the temperature difference value for the end of illumination, wherein the typical value is 1 ℃;
when T isn-tn≥TowJudging to enter a windowing state; t in the state of opening windown-tn＜TcwJudging that the window is closed; judging the heat supply temperature data in the windowing state as abnormal data, and rejecting the abnormal data;
when the condition is satisfied within a certain time interval, tn-Tn≥TssWhen and at this temperatureJudging that the sunlight starts to directly irradiate under the condition that the windowing abnormality does not exist within 6 hours before the data moment; t in the direct projection staten-Tn＜TsfIf so, judging to recover to normal; and under the condition of no abnormal windowing and in the direct sunlight state, the heating temperature data are judged to be abnormal data and are all eliminated.
Wherein: in a certain time interval of direct sunlight, the typical value is generally selected from ten am to four pm by referring to the local sunrise and sunset time.
The following is based on the return water temperature:
calculating according to the heating stop reporting state of the whole community:
Tgavgaverage return water temperature for all heating residents;
Ttavgand reporting the average return water temperature of the stopped household for all heat supplies. Return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgAnd judging the data to be abnormal data and removing the data. Where k is the judgment parameter and the typical value is 0.3.
This embodiment processes all heating data for heating analysis and identifies abnormal household behavior in the process.
A linear regression model for estimation of the room temperature of the hot residents was constructed as follows:
aiming at the rejected effective data set { room temperature, return water temperature and outdoor temperature }
The first step is as follows: taking an average value;
calculating the average value (including average room temperature, average return water temperature and average outdoor temperature) of all residents within a certain time period, wherein the typical calculation is that the average value is calculated according to the following formula of 0: 00-24: 00 calculate the daily average.
The second step is that: training and testing sets;
according to the data set of each heating season of each household, taking p% as a training set, taking the rest (1-p%) as a test set, and taking the value range of p as 0-100.
The third step: performing linear regression;
Tr＝f(Tn-1,Tn,Tout)
Tr＝α1×Tn-1+α2×Tn+β×Tout+Tb
Traverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
Performing regression calculation according to the formula to obtain alpha1、α2、β、TbFour parameters.
The fourth step: carrying out effectiveness treatment;
if the calculated alpha is1、α2And if the beta parameters have indexes less than or equal to zero, making the indexes equal to zero, and performing linear regression for reducing the parameters again until all the parameters are greater than zero. The regression equation of the heat supply user can be obtained through the obtained parameters.
When the training set is selected to be 80%, the prediction conclusion of the regression can be verified according to the 20% test set, and the prediction error of more than 95% can be within +/-0.5 ℃.
According to regression parameters of residents, the building heat preservation condition (beta reflection) and the high thermal efficiency index (alpha) can be judged1、α2Reflective), heating behavior (T)bCan reflect); the obtained regression equation can accurately predict the room temperature of the householder according to the outdoor temperature and the return water temperature, and the room temperature of the householder can be continuously and effectively predicted after the room temperature measuring device fails.
According to the embodiment, the return water temperature index of the next period can be given according to the room temperature expected to be reached by residents by a regression equation and by combining with the outdoor temperature of weather forecast, the index can be directly given to a heat exchange station regulation and control system, and the system can be directly adjusted to a return water tracking temperature mode; or a more refined regulation strategy is that the calculated backwater temperature index is directly given to a valve of a resident, and the valve automatically regulates and controls the opening according to the backwater temperature to achieve the temperature value (the valve operation principle is as follows, the sizes of the backwater temperature index and the current backwater temperature value are judged, if the index is higher than the current value, the valve is opened, the water flow speed is increased, the backwater temperature is increased, and vice versa), so as to further determine the regulation strategy.
Example two
As shown in fig. 3, the present embodiment provides a system for estimating a room temperature of a hot-resident, which includes:
(1) an acquisition module for: acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
(2) an estimation module to: and estimating the average indoor temperature of all the heat-supply residents by using the heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature.
As shown in fig. 2, the process of constructing the estimation model of the room temperature of the hot residents in this embodiment is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
the method comprises the following steps of removing invalid heat supply temperature data sets of residents in a historical preset time period by taking indoor temperature as a basis, wherein the removing basis is as follows:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
Specifically, the process of removing the invalid heating temperature data set of the household within the historical preset time period based on the indoor temperature is the same as the first embodiment, and will not be described here again.
And rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
And calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
Specifically, the linear regression equation of the estimation model of the room temperature of the heating residents is as follows: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
EXAMPLE III
The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1: receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
step 2: estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
step 2.1: acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
step 2.2: rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
the method comprises the following steps of removing invalid heat supply temperature data sets of residents in a historical preset time period by taking indoor temperature as a basis, wherein the removing basis is as follows:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
And rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
Step 2.3: and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
Specifically, the linear regression equation of the estimation model of the room temperature of the heating residents is as follows: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
Because the heating has hysteresis, the influence of the heating on the room temperature is delayed, the average return water temperature and the last average return water temperature are used as calculation bases in the embodiment, the modeling is carried out aiming at the room temperature of the home terminal, the invalid data are removed, the model is updated according to the latest data uploaded every day, the accurate room temperature prediction is realized, and the reliable basis can be provided for the refined regulation and control of the home terminal.
Example four
The embodiment provides a computer device, which includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the following steps:
step 1: receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
step 2: estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
step 2.1: acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
step 2.2: rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
the method comprises the following steps of removing invalid heat supply temperature data sets of residents in a historical preset time period by taking indoor temperature as a basis, wherein the removing basis is as follows:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
And rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
Step 2.3: and calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model.
Specifically, the linear regression equation of the estimation model of the room temperature of the heating residents is as follows:
Tr＝α1×Tn-1+α2×Tn+β×Tout+Tb(ii) a Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
Because the heating has hysteresis, the influence of the heating on the room temperature is delayed, the average return water temperature and the last average return water temperature are used as calculation bases in the embodiment, the modeling is carried out aiming at the room temperature of the home terminal, the invalid data are removed, the model is updated according to the latest data uploaded every day, the accurate room temperature prediction is realized, and the reliable basis can be provided for the refined regulation and control of the home terminal.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes 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 (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method for estimating a room temperature of a hot resident, comprising:
acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model; and rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
2. The method of claim 1, wherein the linear regression equation of the estimation model of the hot household room temperature is: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
3. The method as claimed in claim 1, wherein the invalid heating temperature data sets of the residents within the historical predetermined time period are removed according to the indoor temperature by:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
4. A system for estimating a room temperature of a hot resident, comprising:
an acquisition module for: acquiring the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
an estimation module to: estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model;
and rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
5. The system of claim 4, wherein the linear regression equation of the estimation model of the hot household room temperature is: t isr＝α1×Tn-1+α2×Tn+β×Tout+Tb；
Wherein, TrAverage indoor temperature for all heating residents; t isn-1The previous average backwater temperature is obtained; t isnIs the average backwater temperature; t isoutIs the average outdoor temperature; alpha is alpha1: the heating coefficient is 1; alpha is alpha2: the heating coefficient is 2; beta: the heat preservation coefficient; t isb: and (5) stopping heating.
6. The system as claimed in claim 4, wherein the estimation module is configured to reject the invalid heating temperature data sets of the residents within the historical predetermined time period based on the indoor temperature by:
rejecting heat supply temperature data in a window opening state and heat supply temperature data corresponding to direct sunlight in a window closing state;
the window opening and closing state of the heat supply residents and the direct sunlight state in the window closing state are obtained by comparing the difference value between the indoor temperature at the current moment and the average indoor temperature before the current moment with the corresponding preset temperature difference value.
7. A computer-readable storage medium, on which a computer program is stored, which program, when executed by a processor, carries out the steps of:
receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model;
and rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
8. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of:
receiving the return water temperature and the outdoor temperature corresponding to all heat supply residents at the current moment in real time;
estimating the average indoor temperature of all the heat-supply residents by using a heat-supply resident room temperature estimation model so as to obtain the operation parameters of the heat exchange station in the area to which the heat-supply residents belong, wherein the operation parameters are matched with the current average indoor temperature;
the construction process of the heat supply household room temperature estimation model is as follows:
acquiring a heat supply temperature data set { indoor temperature, return water temperature and outdoor temperature } corresponding to all heat supply residents at each moment in a historical preset time period;
rejecting an invalid heat supply temperature data set of the heat supply residents within a historical preset time period to obtain an effective heat supply temperature data set of the heat supply residents;
calculating an average set of effective heat supply temperature data { average indoor temperature, average return water temperature and average outdoor temperature } of the heat supply residents in a historical preset time period, constructing a linear regression equation, and calculating correlation coefficients of the linear regression equation to obtain a heat supply resident room temperature estimation model; and rejecting the invalid heat supply temperature data set of the resident in the historical preset time period by taking the return water temperature as a basis, wherein the rejection basis is as follows:
return water temperature T at a certain momentout＜kTgavg+(1-k)TtavgJudging the return water temperature at the corresponding moment as abnormal data, and removing; wherein k is a judgment parameter and is a known parameter; t isgavgAverage return water temperature for all heating residents; t istavgAnd reporting the average return water temperature of the stopped household for all heat supplies.
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| CN114076343A (en) * | 2020-08-19 | 2022-02-22 | 京东城市(北京)数字科技有限公司 | Heat exchange station temperature control method and device, computer equipment and storage medium |
| CN113531646B (en) * | 2021-06-07 | 2024-02-09 | 青岛经济技术开发区海尔热水器有限公司 | Heating equipment control method, device, equipment and storage medium |
| CN113606651A (en) * | 2021-07-23 | 2021-11-05 | 淄博热力有限公司 | Room temperature soft measurement system based on machine learning algorithm |
| CN114543278B (en) * | 2022-03-09 | 2024-01-09 | 朱建 | Air conditioner room temperature control method and device |
| CN115186978A (en) * | 2022-05-31 | 2022-10-14 | 河北工大科雅能源科技股份有限公司 | Method and device for evaluating heat supply effect of heat supply system |
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