CN114857659A - Heat exchange station secondary network water supply temperature control algorithm based on segmented RC model - Google Patents
Heat exchange station secondary network water supply temperature control algorithm based on segmented RC model Download PDFInfo
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- 230000002068 genetic effect Effects 0.000 claims abstract description 9
- 230000011218 segmentation Effects 0.000 claims abstract description 9
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
- F24D19/10—Arrangement or mounting of control or safety devices
- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
- F24D19/1009—Arrangement or mounting of control or safety devices for water heating systems for central heating
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/17—District heating
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Abstract
The invention discloses a segmented RC model-based control algorithm for the temperature of water supplied by a secondary network of a heat exchange station, which comprises the following steps: determining the order of an RC model according to the characteristics of the enclosure structure, establishing an equivalent RC model, and identifying parameters by using a genetic algorithm; determining the optimal segmentation number of the RC model, segmenting data according to the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference, and determining the parameters of each segment by using a genetic algorithm; and predicting the minimum secondary network water supply temperature for keeping the indoor temperature at the designed indoor temperature by using the identified model, and generating a secondary network water supply temperature control strategy. The invention can be used for optimizing the operation of the heat exchange station, solves the problem of thermal unbalance of the heat exchange station by adjusting the temperature of the supplied water of the secondary network, and reduces the energy consumption of the heat exchange station as far as possible on the premise of ensuring the thermal comfort of users.
Description
Technical Field
The invention relates to the field of control strategies of the water supply temperature of a secondary network of a heat exchange station, in particular to a prediction control algorithm of the water supply temperature of the secondary network of the heat exchange station based on a segmented RC model parameter identification process.
Background
At present, a large amount of research on an automatic control algorithm of a district heating system is carried out by scholars, but a reliable and stable building data model is not used for supporting, and the scholars cannot finely guide the operation of a heat exchange station. The researchers have made a lot of researches on the RC model, but the research finds that the existing RC model still has some problems which are difficult to solve:
(1) the prediction result of the existing RC model shows larger deviation under different working conditions, which shows that the heat transfer coefficient and the heat capacity of the building are different under different working conditions. The existing method is difficult to accurately predict the temperature of the secondary network water supply under all working conditions.
(2) Although the existing RC model considers the heat accumulation effect of the building, the existing RC model mainly considers the attenuation effect on the strength and cannot well reflect the delay effect on the time.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the heat exchange station secondary network water supply temperature prediction control algorithm based on the segmented RC model parameter identification process is provided, the automatic control of the heat exchange station secondary network water supply temperature is realized quickly and efficiently, the problem of heat exchange station thermal unbalance is relieved, and the energy utilization efficiency of the heat exchange station is improved.
The invention aims to realize the purpose through the following scheme, and the heat exchange station secondary network water supply temperature control algorithm based on the segmented RC model comprises the following steps:
step 1: building an equivalent RC model of the worst tail end of the secondary network, selecting a proper equivalent RC model order according to the type of an actual building and the characteristics of a building envelope, and developing a segmented RC model for predicting the water supply temperature of the secondary network of the heat exchange station based on kirchhoff's law and the building thermal process principle;
step 2: identifying the model set up in the step 1 by adopting a genetic algorithm, so that the model after parameter identification can reflect the relation between indoor temperature and outdoor temperature as well as water supply temperature, and recording a prediction error e;
and 3, step 3: the model is under different working conditions, the difference of prediction results is large, so that the model is segmented according to outdoor temperature, indoor temperature, water supply temperature, indoor and outdoor temperature difference, indoor and water supply temperature difference and the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference, prediction errors e are calculated, the data are segmented equidistantly according to the ratio of the indoor and outdoor temperature difference to the outdoor and water supply temperature difference with the minimum prediction errors e under the same segmentation number through comparison, the data are segmented equidistantly according to the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference, the number of the segments is 2, and parameter identification is carried out on each segment of the data by using a genetic algorithm;
and 4, step 4: according to the step 3, continuously increasing the number of the segments, recording a prediction error e until the prediction error is not obviously reduced along with the increase of the number of the segments, wherein the number of the segments is the optimal number of the segments, segmenting the data according to the optimal number of the segments, and identifying parameters of each segment of the data;
and 5: will design the indoor temperature T s And inputting the predicted outdoor temperature into the segmented equivalent RC model after parameter identification to predict the temperature T of the secondary network water supply g The water supply temperature is the minimum secondary network water supply temperature for maintaining the indoor temperature at the designed indoor temperature, and the operation of the heat exchange station is controlled by the water supply temperature.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects: the RC model built by the invention further embodies the heat storage and release capacity of the building envelope by using the delay time, and fully considers the heat storage and release characteristics of the building. The invention improves the model precision by adopting a sectional mode, the obtained model fully reflects the heat capacity and the heat resistance under different working conditions, and a satisfactory prediction result can be obtained under all working conditions of a heating season. The model is used for controlling the water supply temperature of the secondary network of the heat exchange station, so that the problem of thermal unbalance of the heat exchange station is relieved, and the energy utilization efficiency of the heat exchange station is effectively improved.
Drawings
FIG. 1 is a flow chart of an algorithm for implementing predictive control of the temperature of the water supply of the secondary network in accordance with the present invention;
FIG. 2 is a first order modified equivalent RC model established in the present invention;
FIG. 3 is a graph comparing the predicted results and actual results of the model established in the present invention with room temperature;
FIG. 4 is a flow chart of RC model segmentation identification in the present invention;
FIG. 5 is a comparison graph of the theoretical reference value of the secondary network water supply temperature of the first-order equivalent RC model and the secondary network water supply temperature in the actual engineering.
Detailed Description
The present invention will be described with reference to the accompanying drawings, which are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification. After reading the teaching of the present invention, those skilled in the art can make various changes or modifications to the invention, and these equivalents also fall within the scope of the claims appended to the present application.
A heat exchange station secondary network water supply temperature predictive control algorithm based on a segmented RC model parameter identification process has the core of an accurate model parameter identification process, and the key process of the algorithm for realizing the predictive control of the secondary network water supply temperature is shown in the attached figure 1, and the specific realization comprises the following steps:
step 1: and (2) selecting a proper equivalent RC model order according to the type of the actual building and the characteristics of the enclosure structure, wherein the attached figure 2 is a first-order equivalent RC model structure which is established by mainly considering the heat capacity and the heat resistance of the enclosure structure. The input variable of the model comprises the building outdoor temperature, and the output variable of the model is the temperature of the secondary network water supply. The mathematical description is as follows:
in the formula: c is the equivalent heat capacity of indoor air, J/K; t is t w Outdoor dry bulb temperature, deg.C; t is t n Indoor air temperature, deg.C; t is t g Water temperature, deg.C, for the secondary net supply; m is the heat quantity of partial conversion of the solar radiation on the building enclosure, W; n is the delay time of the outer shield structure, H; m is the delay time of the inner enclosure structure, H.
Step 2: and (3) identifying the model built in the step (1) by adopting a genetic algorithm, and calling a genetic algorithm tool box in matlab to realize parameter identification.
In the embodiment, an actual central heating system in Henan is adopted to provide indoor temperature and secondary network water supply temperature monitoring data for the model, and outdoor dry bulb temperature data is acquired by a local meteorological monitoring department. The identification results are shown in Table 1. The prediction error e is calculated by the formula:
TABLE 1 RC model parameter identification results
And step 3: FIG. 3 is a graph comparing the predicted results of the model versus room temperature with the actual results. It can be found from fig. 3 that: under different working conditions, the prediction results of the model have larger difference, so that segmentation is carried out according to outdoor temperature, indoor temperature, water supply temperature, indoor and outdoor temperature difference, indoor and water supply temperature difference and the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference, and the prediction error e is calculated, as shown in the attached figure 4. After comparison, the data are segmented equidistantly according to the ratio of the indoor and outdoor temperature difference with the minimum prediction error e to the outdoor and water supply temperature difference under the same segmentation number. And (3) carrying out equidistant segmentation on the data according to the ratio of the indoor and outdoor temperature difference to the indoor temperature water supply temperature difference, wherein the number of the segments is 2. And respectively carrying out parameter identification on each section of data by using a genetic algorithm.
And 4, step 4: as shown in fig. 4, the number of segments is continuously increased, the prediction error e is recorded, until the prediction error is not significantly reduced along with the increase of the number of segments, the number of segments is the optimal number of segments, the optimal number of segments is calculated to be 4, the data is segmented according to the optimal segment 4, and the parameter identification is performed on each segment of data, and the specific parameter identification result is shown in table 2. The data classification criteria for each segment are shown in table 3.
And 5: inputting the designed indoor temperature Ts and the predicted outdoor temperature into a segmented equivalent RC model after parameter identification, predicting the water supply temperature Tg of the secondary network, wherein the water supply temperature is the minimum water supply temperature of the secondary network for maintaining the indoor temperature at the designed indoor temperature, controlling the operation of the heat exchange station by using the water supply temperature, and comparing the theoretical baseline of the water supply temperature of the secondary network of the first-order equivalent RC model with the water supply temperature of the secondary network in the actual engineering with the attached figure 5. The reference value curve is a theoretical baseline of the temperature of the water supply of the secondary network, which is generated by taking the design temperature of 18 ℃ as an input value; "first-stage high" means a secondary water supply temperature curve generated when the indoor temperature is 19 ℃ as an input value; the 'first-level low' is a secondary water supply temperature curve generated when the indoor temperature is 17 ℃ as an input value, and the 'first-level high' and 'first-level low' curves are used for measuring the energy consumption level of the heat exchange station. The graph shows that the actual water supply temperature is always greater than the theoretical basic value, most of the actual water supply temperature is higher than the first-level high curve, and the actual water supply temperature is higher than the theoretical basic value, so that the energy consumption of the heat exchange station is higher, the heat supply of a terminal user is over and over, and the heat supply is wasted.
TABLE 2 concrete parameter identification results of the segmented model
TABLE 3 data segmentation criteria
The foregoing illustrates and describes the principles, general features, and advantages of the present invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are intended to further illustrate the principles of the invention, and that various changes and modifications may be made without departing from the spirit and scope of the invention, which is also intended to be covered by the appended claims. The scope of the invention is defined by the claims and their equivalents.
Claims (6)
1. The utility model provides a heat exchange station secondary network water supply temperature control algorithm based on segmentation RC model which characterized in that:
comprises the following steps:
the method comprises the following steps: selecting a proper equivalent RC model order according to the type of an actual building and the characteristics of a building envelope, and developing an improved RC model for predicting the temperature of the water supply of a secondary network of the heat exchange station based on the kirchhoff law and the building thermal process principle;
step two: identifying the model set up in the first step by adopting a genetic algorithm, so that the model after parameter identification can reflect the relation between indoor temperature and outdoor temperature as well as water supply temperature, and recording a prediction error e;
step three: segmenting the data at equal intervals according to the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference, wherein the number of the segments is three, and performing parameter identification on each segment of data by using a genetic algorithm;
step four: according to the third step, continuously increasing the number of the segments, recording the prediction error e until the prediction error is not obviously reduced along with the increase of the number of the segments, wherein the number of the segments is the optimal number of the segments, segmenting the data according to the optimal number of the segments, and identifying the parameters of each segment of the data;
step five: will design the indoor temperature T s And inputting the predicted outdoor temperature into the segmented equivalent RC model after parameter identification to predict the temperature T of the secondary network water supply g The water supply temperature is the minimum secondary network water supply temperature for maintaining the indoor temperature at the designed indoor temperature, and the operation of the heat exchange station is controlled by the water supply temperature.
2. The segmented RC model-based heat exchange station secondary network water supply temperature control algorithm according to claim 1, characterized in that:
in the first step, an improved first-order RC model is established, and input variables of the model comprise the temperature of a building outdoor dry bulb and the temperature of indoor air; the output variable of the model is the temperature of the secondary network water supply; the undetermined parameters are the equivalent heat capacity of indoor air, the partially converted heat of the solar radiation acting on the enclosure structure, the delay time of the outer enclosure structure and the delay time of the inner enclosure structure.
3. The segmented RC model-based heat exchange station secondary network water supply temperature control algorithm according to claim 1, characterized in that:
in the second step, the prediction error e is measured by the root mean square error RMSE.
4. The segmented RC model-based heat exchange station secondary network water supply temperature control algorithm according to claim 1, characterized in that:
and in the third step, the data are segmented equidistantly according to the ratio of the indoor and outdoor temperature difference to the indoor water supply temperature difference.
5. The segmented RC model-based heat exchange station secondary network water supply temperature control algorithm according to claim 1, characterized in that:
and in the fourth step, determining the optimal number of the segments, carrying out equidistant segmentation on the data, gradually increasing the number of the segments, and calculating the prediction error e, wherein the number of the segments when the prediction error is not obviously reduced is the optimal number of the segments along with the increase of the number of the segments.
6. The segmented RC model-based heat exchange station secondary network water supply temperature control algorithm according to claim 1, characterized in that:
and in the fifth step, the model is used for predicting the minimum secondary network water supply temperature when the designed indoor temperature is kept indoors, and the water supply temperature can be used for guiding the control strategy of the heat exchange station.
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| JP2013174373A (en) * | 2012-02-24 | 2013-09-05 | Corona Corp | Hot water heating device |
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| CN112993979A (en) * | 2021-02-22 | 2021-06-18 | 广东电网有限责任公司韶关供电局 | Power distribution network reactive power optimization method and device, electronic equipment and storage medium |
| CN113623719A (en) * | 2021-06-23 | 2021-11-09 | 国家电投集团东北电力有限公司大连开热分公司 | Heat exchange station prediction control method based on effective room temperature detection |
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2013174373A (en) * | 2012-02-24 | 2013-09-05 | Corona Corp | Hot water heating device |
| CN103995977A (en) * | 2014-05-30 | 2014-08-20 | 国家电网公司 | Double-fed wind turbine generator set parameter identification method based on LVRT transient response characteristic analysis |
| CN111523707A (en) * | 2020-04-08 | 2020-08-11 | 江苏方天电力技术有限公司 | Method for modeling variable working condition characteristics of comprehensive energy system equipment based on segmented data fitting |
| CN112032882A (en) * | 2020-08-07 | 2020-12-04 | 南京南瑞继保电气有限公司 | Scheduling method of ice storage air conditioning system |
| CN112361450A (en) * | 2020-09-02 | 2021-02-12 | 天津大学 | Heat exchange station feedback prediction regulation and control method based on indoor temperature |
| CN112993979A (en) * | 2021-02-22 | 2021-06-18 | 广东电网有限责任公司韶关供电局 | Power distribution network reactive power optimization method and device, electronic equipment and storage medium |
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