CN112128841A - Whole-network balance adjusting method based on load prediction and room temperature feedback correction - Google Patents
Whole-network balance adjusting method based on load prediction and room temperature feedback correction Download PDFInfo
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Abstract
The invention provides a whole network balance adjusting method based on load prediction and room temperature feedback correction. The traditional heating regulation is mostly the regulation of a heat source or a heating power station, and the extensive regulation mode can cause great fluctuation of the room temperature and even waste energy. The regulation and control method provided by the invention adds regulation research on the balance of the whole network on the basis of considering the regulation of a heat source and a heat station. The aim of the whole network balance adjustment is to realize the supply and demand matching of the primary network and the secondary network and the pipe network stability by adjusting the parameters of the heating system. In order to achieve the purpose, a regulation and control period needs to be determined, time nodes need to be regulated, heat supply parameters need to be subjected to period dynamic prediction, the target value of the room temperature of a user is taken as the center, the room temperature is utilized to perform feedback correction on the predicted water supply temperature, and finally the whole network balance regulation method based on load prediction and room temperature feedback correction is formed. The fluctuation of the room temperature is smooth after the method is adopted for adjustment, the phenomena of overheating and supercooling are obviously reduced, and the energy-saving effect is obvious.
Description
The technical field is as follows:
the invention relates to the field of heat supply regulation, in particular to a full-network balance regulation method based on load prediction and room temperature feedback correction.
Background art:
in order to meet the heat supply requirements of users and save energy, the heat supply system needs to be dynamically adjusted along with the change of load. Choi et al, in "chemical modeling and control of the thermal plant in the characterization system of Korea", proposed a controller based on a load prediction model, using the heat supply and the boiler outlet temperature as output parameters, and the results demonstrated that the control algorithm can effectively save energy. Smrekar et al, in a Development of an artificial neural network model for a co-fibrous using real plant data, have established a neural network model for a coal-fired boiler for predicting the mass flow, pressure and temperature of the steam at the boiler outlet. The above studies only consider the heat source side, optimizing heat source operation based on load prediction. Laakkonen et al, in "Predictive Supply Temperature Optimization of distribution Networks Using Delay Distributions", use a neural network model to predict the user heat load and return water Temperature, and propose a powerful optimizer to optimize the Supply water Temperature, reducing heat loss and pumping cost. Dahl et al put forward an autoregressive load prediction model based on weather dynamic uncertainty in Using ambient weather prediction in dispersion operation and load formation, and the model is utilized to optimize the control of a thermal station, so that the water supply temperature is obviously reduced. The above study is a unilateral regulation and control study of the thermal station. In actual regulation, a heat source or a heating station is singly regulated mostly according to experience of technicians and feedback of heat users, consideration on balance of supply and demand of the whole network is lacked, and the conditions of insufficient heat supply or excessive heat supply and energy waste often occur by depending on experience when the regulation is performed. Therefore, accurate regulation strategy research by taking 'source-network-station-load' as an integral object is urgently needed.
Therefore, the invention provides a full-network balance adjusting method based on load prediction and room temperature feedback correction to solve the problems.
The invention content is as follows:
in order to overcome the defects of the prior art, the invention aims to provide a full-network balance adjusting method based on load prediction and room temperature feedback correction.
In order to achieve the purpose, the invention adopts the technical scheme that: a total network balance adjustment method based on load prediction and room temperature feedback correction is characterized in that a heat supply system is taken as a whole, meanwhile, comprehensive consideration is carried out on four aspects of a heat source, a heating station, a network and a terminal, total network adjustment is formulated on the basis of the theoretical supply and demand balance of the heat source, the heating station and a user, the target value of the room temperature of the user is taken as the center, the room temperature is used for carrying out feedback correction on the predicted water supply temperature, and a total network balance adjustment and control route based on load prediction is obtained.
For the primary network, calculating and analyzing the fluid flow time from a heat source to each heat power station, thereby determining a heat source regulation and control period; and regarding the secondary side, considering the comprehensive thermal inertia of the building, and taking the comprehensive delay time, namely the difference between the occurrence time of the indoor temperature peak valley and the occurrence time of the outdoor temperature peak valley as a characterization parameter of the comprehensive thermal inertia in the aspect of temperature change, thereby determining the regulation and control period of the thermal station.
S1, respectively predicting loads of a thermal power station and a heat source by using an MLR (maximum likelihood ratio) and a GRNN (generalized regression neural network) network model, and establishing a thermal power station load prediction model and a heat source load prediction model;
s2, determining a regulation and control period a of the heating power station according to the comprehensive heat inertia of the building, and determining a heat source regulation and control period j according to the delay time of a pipe network;
s3, after determining the regulation and control cycles of the heat source and the heat station, respectively establishing time point prediction models for heat load data of the heat source and the heat station at different regulation moments in one day through a GRNN (generalized regression neural network) and an MLR (maximum likelihood ratio), and finding out corresponding time points with highest predicted value precision in the determined corresponding regulation and control cycles, namely the optimal regulation time node of the heat source and the optimal regulation time node of the heat station;
s4, performing room temperature feedback iterative calculation on the predicted temperature supply of the heat station to obtain an adjustment formula (4);
wherein, t'2gIs secondary side predicted supply water temperature t'2hPredicting the return water temperature for the secondary side, wherein the two predicted temperatures are obtained through a thermal station load prediction model in the step S1; t is tnsIndicates a set room temperature; kr、KbIs the heat transfer coefficient of a radiator and the comprehensive heat transfer coefficient of a building envelope, W/(m)2·℃)；Fr、FbThe heat dissipation area of the radiator and the heat transfer area of the building envelope are set; t is tn、twIndoor temperature and outdoor temperature; t is t2g、t2hSupplying water and returning water to the secondary network heating power station; m is2The fitting coefficient is obtained by performing linear fitting by taking the difference between the secondary side predicted water supply temperature and the actual water supply temperature as an independent variable and the difference between the secondary side predicted return water temperature and the actual return water temperature as a dependent variable; Δ tStationA difference between the predicted supply water temperature and the actual supply water temperature for the secondary side;
s5, performing room temperature feedback iterative calculation and adjustment on the heat source prediction temperature to obtain an adjustment formula (7):
wherein, t'1gjThe predicted required water supply temperature of the heat source is obtained through prediction of the heat source load prediction model in the step S1;
heat source required feed water temperature t 'predicted in j-th cycle'1gjSetting the room temperature tnsIndoor temperature tnAnd outdoor temperature twAnd the supply-return average temperature t of the j-th period on the heat source side1pjObtaining room temperature feedback after adjustmentThe temperature of the heat source side supply water;
and S6, after specific regulation and control periods and time nodes of the heat source and the heat station are obtained according to the process, the feedback correction is carried out on the predicted water supply temperature of the heat source according to a room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat source in the step S5 by taking the room temperature target value of the user as the center, meanwhile, the feedback correction is carried out on the predicted water supply temperature of the heat station according to the room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat station in the step S4, and finally, a whole network balance regulation and control route based on load prediction is formed.
In step S6, the two specific feedback correction processes are:
for a thermal station: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: determining the actual indoor temperature tnWhether or not to reach the set room temperature tnsIf equal, the plant feed water temperature is set to the feed water temperature t 'predicted by the plant load prediction model in step S1'2gi；
If not, continuously judging the actual indoor temperature tnWhether it is greater than the set room temperature tnsIf not, the temperature of the water supplied to the thermal station is set to t'2ga+ΔtStation(ii) a If the temperature is higher than the preset temperature, the water supply temperature of the heat station is set to t'2ga-ΔtStation，ΔtStationThe difference value between the predicted water supply temperature and the actual water supply temperature at the set room temperature is obtained by a room temperature feedback iterative calculation adjustment formula of the predicted temperature supply of the thermal power station, and after the database of the thermal power station receives new data each time, the adjusted data is used for fitting a thermal power station load prediction model again to obtain a latest correction model under the new database, and then the secondary side water supply temperature of the next adjustment and control period is predicted;
for the heat source: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: determining the actual indoor temperature tnWhether or not to reach the set room temperature tnsIf equal, the plant water supply temperature is set to the water supply temperature t 'predicted by the heat source load prediction model in step S1'1gj；
If not, regulating and controlling the period t 'for the prediction j'1gjPerforming dynamic debugging to obtain the actual required water supply temperature of the regulation and control period of the heat source j after the indoor temperature and the set temperature are debugged, and obtaining the primary network water supply temperature t by the heat source according to a formula (7)1gjGuiding actual heat supply;
the two are simultaneously used for realizing source-network balance.
Compared with the prior art, the invention has the beneficial effects that:
the invention realizes the regulation and control strategy of the whole heating system which reasonably makes the supply of heat source and heat supply quantity, the operation of the heating power network and the heat distribution quantity of the heating power station by taking the demand of a heat user as an important regulation and control target through the determination of the prediction model, the feedback correction, the regulation and control period and the regulation and control time point, and finally determines the whole network balance regulation method based on the load prediction and the room temperature feedback correction. Compared with the optimized regulation and control of a single heat source or a heat station, the method has better effect and can better realize the supply and demand balance of a heat supply system by taking the 'source-network-station-load' as an integral object to regulate and control the whole network. By adopting the full-network balance adjustment method based on load prediction and room temperature feedback correction, the fluctuation range of the indoor temperature is obviously reduced, the phenomena of supercooling and overheating are reduced, the safety of a pipe network is improved, and the energy-saving operation of a heat supply system is realized. The invention considers the indoor temperature of the user side, realizes the synchronous regulation of the heat source and the heating power station according to the feedback of the heat user, and realizes the feedback control.
Drawings
FIG. 1 is a load prediction-based global network balance regulation route diagram (Δ t in FIG. 1)StationThe difference between the predicted water supply temperature of the secondary side and the actual water supply temperature at the set room temperature is obtained. Taking the left side of the figure as an example, predicting the secondary side by using MLR and establishing a model, determining a regulation period and a time node, and setting the room temperature tnsAnd a predicted feed water temperature t 'from room temperature using room temperature as the center in the a-th cycle'2gaFeedback correction is performed).
FIG. 2 is a flow chart of the determination of an adjustment time node; processing original operation data recorded at intervals of 10min into corresponding time sequences of various heat stations and heat source adjusting time tables, establishing prediction models for different adjusting times of the three heat stations by using an MLR (Multi-level routine) model, wherein the output parameters of the model are water supply temperature, and the input parameters are outdoor temperature, indoor temperature and historical water supply temperature; a prediction model is established for different adjusting moments of a heat source by utilizing a GRNN neural network, the output parameters are water supply temperature, and the input parameters are outdoor temperature, differential pressure, flow, historical water supply temperature and the like. And the time node corresponding to the highest precision in the prediction results of various heat stations and heat sources is the solved optimal regulation time node.
Fig. 3 is a graph of the integrated delay time analysis in the embodiment.
The optimal prediction results of the adjustment time nodes in the embodiment of fig. 4 are shown.
In the embodiment of fig. 5, the actual water supply temperature before the regulation of the heat source and the three types of heating power stations and the change curve graphs of the predicted water supply temperature, the corrected water supply temperature, the indoor temperature and the outdoor temperature during the regulation are shown.
The specific implementation mode is as follows:
the present invention is further explained with reference to the following examples and drawings, but the scope of the present invention is not limited thereto.
The invention relates to a full-network balance regulation method (see figure 1) based on load prediction and room temperature feedback correction, which comprises the steps of determining regulation and control periods and regulation and control time nodes of a heating power station and a heat source, carrying out period dynamic prediction on heat supply parameters, carrying out feedback correction on predicted water supply temperature by using room temperature as a center, establishing a clear full-network balance regulation and control technical route, and regulating and controlling the full network by taking 'source-network-station-load' as an integral object, and specifically comprises the following steps:
s1, respectively predicting loads of a thermal power station and a heat source by using an MLR (maximum likelihood ratio) and a GRNN (generalized regression neural network) network model, and establishing a thermal power station load prediction model and a heat source load prediction model;
the specific steps of step S1 are;
step S11, establishing a heating power station load prediction model by using MLR multiple linear regression, wherein the expression of the regression model is as follows:
wherein, thetai(i-0, 1,2, …, n) is a model regression coefficient, Xi(i is 0,1,2, …, n) is the characteristic value of the ith sample (X0 is 1 when i is 0), and the sample is the heat station heating history data of the previous year;
predicting the load of the heating power station;
the MLR multiple linear regression is that the relation between the dependent variable and the independent variable is found according to the data statistics principle, compared with other prediction models, the MLR multiple linear regression is simple in structure, high in calculation speed and high in prediction accuracy, and is suitable for short-term prediction.
Step S12, establishing a heat source load prediction model by using a GRNN network model, wherein the expression is as follows:
wherein, Xi，YiObtaining the observed value from historical data; n is the sample volume; σ is a smoothing factor;
and the predicted value is the heat source load.
And S2, determining a regulation and control period a of the heating power station according to the comprehensive heat inertia of the building, and determining a heat source regulation and control period j according to the delay time of the pipe network. The heat source regulation and control period is determined by calculating and analyzing the fluid flow time from the heat source to each heating power station, and in practice, the heat transfer lag time (pipe network delay time) of the pipe network can be considered to be equal to the fluid flow time. And (3) determining the regulation and control period of the thermal power station (namely, the secondary side), considering the comprehensive thermal inertia of the building, and taking the comprehensive delay time (the difference between the occurrence time of the indoor temperature peak valley and the occurrence time of the outdoor temperature peak valley) as a characterization parameter of the comprehensive thermal inertia in the aspect of temperature change.
S3, after determining the regulation and control cycles of the heat source and the heat station, respectively establishing time point prediction models for heat load data of the heat source and the heat station at different regulation moments in one day through a GRNN (generalized regression neural network) and an MLR (maximum likelihood ratio), and finding out corresponding time points with highest predicted value precision in the determined corresponding regulation and control cycles, namely the optimal regulation time node of the heat source and the optimal regulation time node of the heat station;
s4, a room temperature feedback iterative calculation adjustment formula for predicting temperature supply of the heating station is obtained;
when the heating system operates in a stable working condition in the regulation and control period of the a-th heating station, a formula exists:
wherein the "parameter" with "" denotes a prediction parameter, t'2gIs secondary side predicted supply water temperature t'2hPredicting the return water temperature for the secondary side, wherein the two predicted temperatures are obtained through a thermal station load prediction model in the step S1; t is tnsIndicates a set room temperature; kr、KbIs the heat transfer coefficient of a radiator and the comprehensive heat transfer coefficient of a building envelope, W/(m)2·℃)；Fr、FbThe heat dissipation area of the radiator and the heat transfer area of the building envelope are set; t is tn、twIndoor temperature and outdoor temperature; t is t1g、t1hSupplying water temperature and return water temperature for the primary net heat source; t is t2g、t2h-water supply temperature and water return temperature of the secondary network heating power station.
The difference between the predicted water supply temperature and the actual water supply temperature at the set room temperature can be obtained by using the formula (1) as follows:
using historical operating data of a secondary network (the secondary network has the same meaning with the secondary side and refers to a heating power station, a user side and a hot water pipe network between the heating power station and the user), including water supply temperature and return water temperature of the secondary network heating power station, taking the difference between the predicted water supply temperature and the actual water supply temperature as an independent variable, and taking the difference between the predicted return water temperature and the actual return water temperature as a dependent variable to perform linear fitting to obtain a formula (3):
t′2h-t2h＝m2·(t′2g-t2g) (3)
wherein m is2Are fitting coefficients.
And (3) combining the formula (2) and the formula (3) to finally obtain the secondary side predicted water supply temperature t 'at the set room temperature'2gThe difference from the actual water supply temperature is:
and the formula (4) is a room temperature feedback iterative calculation adjustment formula for the predicted temperature supply of the heat station.
S5, performing room temperature feedback iterative calculation and adjustment formula of heat source prediction and temperature supply:
the heat consumption required by the target room temperature and the heat consumption required by the actual room temperature in the jth regulation and control period of the heat source have the following formula:
wherein Q' is the heat load required by the heat source target room temperature; q is the heat load required by the actual room temperature of the heat source;
the flow ratio is 1 in a regulation period; t is t1gj、t1hjThe water supply temperature and the return water temperature of the heat source required by the actual room temperature in the j-th period (the room temperature is taken as a target, and when tn is not equal to tns, the water supply temperature t1gj suitable for the actual room temperature is obtained by debugging); t'1gj、t'1hjThe supply water temperature and the return water temperature required for the heat source predicted in step S12 at the j-th cycle.
At this time t1gj＝t1pj+ΔtHeat generation，t1hj＝t1pj-ΔtHeat generationObtaining t1gj-t1hj＝2ΔtHeat generationSubstituting the obtained product into formula (5) to obtain formula (6), wherein t1pjThe supply-return average temperature of the j-th period on the heat source side.
Obtaining the adjusted j period primary network water supply temperature value (average supply and return water temperature t)p＝(tg+th) /2, therefore t1pj＝(t'1gj+t'1hj) L 2, so t'1hj＝2t1pj-t'1gj)。
Wherein, t'1gjThe predicted required water supply temperature of the heat source is obtained through prediction of the heat source load prediction model in the step S12;
equation (7) is a room temperature feedback iterative calculation adjustment equation of the predicted supply temperature of the heat source, and the required supply temperature t 'of the heat source is predicted in the jth period'1gjSetting the room temperature tnsIndoor temperature tnAnd outdoor temperature twAnd the supply-return average temperature t of the j-th period on the heat source side1pjObtaining the temperature of the heat source side water supply fed back by the room temperature after adjustment;
and S6, after specific regulation and control periods and time nodes of the heat source and the heat station are obtained according to the process, the feedback correction is carried out on the predicted water supply temperature of the heat source according to a room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat source in the step S5 by taking the room temperature target value of the user as the center, meanwhile, the feedback correction is carried out on the predicted water supply temperature of the heat station according to the room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat station in the step S4, and finally, a whole network balance regulation and control route based on load prediction is formed.
The two specific feedback correction processes are as follows:
for a thermal station: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: judge the actual indoorTemperature tnWhether or not to reach the set room temperature tnsWhen the water supply temperatures are equal to each other, the plant water supply temperature is set to the water supply temperature t 'predicted by the prediction model in step S11'2gi；
If not, continuously judging the actual indoor temperature tnWhether it is greater than the set room temperature tnsIf not, the temperature of the water supplied to the thermal station is set to t'2ga+ΔtStation(ii) a If the temperature is higher than the preset temperature, the water supply temperature of the heat station is set to t'2ga-ΔtStation，ΔtStationThe difference value between the predicted water supply temperature and the actual water supply temperature at the set room temperature is obtained by a room temperature feedback iterative calculation adjustment formula of the predicted temperature supply of the thermal station, and after the database of the thermal station receives new data each time, the adjusted data is used for fitting the prediction model of the thermal station again to obtain the latest correction model under the new database, and then the prediction of the secondary side water supply temperature of the next regulation and control period is carried out;
for the heat source: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: determining the actual indoor temperature tnWhether or not to reach the set room temperature tnsWhen the water supply temperatures are equal to each other, the plant water supply temperature is set to the water supply temperature t 'predicted by the prediction model in step S12'1gj；
If not, regulating and controlling the period t 'for the prediction j'1gjPerforming dynamic debugging to obtain the actual required water supply temperature of the regulation and control period of the heat source j after the indoor temperature and the set temperature are debugged, and obtaining the primary network water supply temperature t by the heat source according to a formula (7)1gjGuiding actual heat supply;
the two are simultaneously used for realizing source-network balance.
In the application, source-network-station-load refers to an integral system of a heat source-network-heat station-heat user in a heat supply system, and the supply of heat source heat supply and the operation of a heat network (the determination of regulation and control period and regulation time node) and the heat distribution of the heat station take the requirements of the heat user, namely, parameters such as indoor temperature, outdoor temperature and historical water supply temperature as control targets. The method has the greatest characteristic that the method introduces indoor temperature parameters, has indoor temperature feedback and can maintain a certain indoor temperature.
Through the determination of the prediction model, the feedback correction, the regulation and control period and the regulation time point, the regulation and control strategy of the whole heating system which takes the demand of a heat user as an important regulation and control target, reasonably establishes the supply of heat source heating load, the operation of a heating power network and the heat distribution quantity of a heating power station is realized, and finally the whole network balance regulation method based on the load prediction and the room temperature feedback correction is determined.
Example 1
The embodiment of the invention relates to a full-network balance adjusting method based on load prediction and room temperature feedback correction, which comprises the following steps:
1. and determining a heat source regulation period. The actual heat supply area of a research object is about 2600 ten thousand square meters, the heat source is divided into a thermal power plant and a peak shaving boiler room, the main heat source outlet is divided into two main pipe networks of a south line and a north line, and the two pipe networks are converged and communicated in the middle. The total network has 899 heating power stations and 1025 units, and all the heating power station units are controlled by valves. And 2500 sets of typical room temperature acquisition points are installed for remotely monitoring the room temperature of a typical heat user, so that data information monitoring of the terminal heat supply state of the heat user is realized. And calculating the fluid flow time from the heat source to each heating power station in the heat supply network under study by using the conclusion that the transmission heat lag time of the pipe network is close to the fluid flow time. On the premise of meeting the primary network stability, in order to realize refined energy-saving operation of a heating system, the heat source regulation and control period of the heat supply network is determined to be 12 hours.
2. And determining the regulation period of the thermal station. In order to analyze the comprehensive delay time of the comprehensive heat inertia of the heating building through the actual operation condition, the secondary network quality adjustment temperature-supply stable condition of the heating station is selected for analysis. And considering the reasons of sensor interference and the like, the small fluctuation of the water supply temperature is taken as the standard for measuring whether the internal heat source supplies heat constantly, and the working conditions that the fluctuation of the water supply temperature at the previous moment and the fluctuation of the water supply temperature at the next moment are all taken as the stable water supply temperature. The definition of the comprehensive delay time is the difference between the occurrence time of the indoor temperature peak valley and the occurrence time of the outdoor temperature peak valley, two typical households a and b are selected for each type of heating power station on the basis of the historical operating data of 2017-2018 heat supply seasons, the comprehensive delay time is analyzed, and the result is shown in figure 3. As can be seen from fig. 3, the response times of three different buildings are greatly different, the comprehensive delay time of the non-energy-saving radiator and the secondary network of the energy-saving radiator heat supply building is about 6h and 8h, and for the energy-saving floor heating heat supply building, the comprehensive delay time is about 12 h. The data analysis result is consistent with the theory, the heat inertia of the energy-saving building is larger than that of the non-energy-saving building, and the heat accumulation of the floor heating system is better than that of the radiator because the floor heating system is laid under the ground. Based on the analysis and summary of the comprehensive delay time, the adjusting time of the thermal station supplying heat to the energy-saving floor heating, the energy-saving radiator and the non-energy-saving radiator in the heat supply network is respectively set as 12h, 8h and 6 h.
3. Determination of regulation time nodes of the thermal power station and the heat source.
Dividing 24h a day into 13 initial adjusting moments according to 2h intervals, then establishing a prediction model with different time nodes for the heating power station by utilizing MLR multiple linear regression, wherein the time node with the highest precision in a prediction result is the adjusting time point of the heating power station. As shown in fig. 4. The optimal regulation and control time nodes of the heating power station for supplying heat to the energy-saving floor heating device, the energy-saving radiator and the non-energy-saving radiator in one day are respectively 8: 00/20: 00. 6:00/14:00/22:00 and 4:00/10:00/16:00/22: 00.
The method is the same as the method for determining the time nodes of the thermal power station, 24h a day is divided into 13 initial adjusting moments according to 2h intervals, prediction models of different time nodes are respectively established by utilizing a GRNN neural network, the time node with the highest precision in a prediction result is a heat source adjusting time point, and the optimal time for adjusting and controlling the heat source is 8: 00/20: 00, the final result conforms to the weather and user heat consumption rules.
4. And (4) performing feedback correction on the heat supply parameters by taking the target room temperature value as a center. The specific correction process is as follows:
1) feedback correction to thermal station:
if the actual indoor temperature tnExpected set room temperature tnsAnd c, correcting t 'of the secondary net of the heat station of the next period through a heat station room temperature feedback iterative calculation adaptation formula according to the fact that the heat demand quantity of the end user of the next period should be reduced'2gaReducing, i.e. guiding the heating of the heating station according to the reduced correction value, and storing the correction water supply temperature of the period a in the storage deviceA temperature supply database; if tn＝tnsIf the heat demand of the end user at the next time is consistent with the prediction, the predicted value t 'is used'2gaGuiding heat supply, and storing the predicted water supply temperature of the period a in a temperature supply database; if tn＜tnsAnd then, the heat demand of the end user at the next moment is increased, and the t 'of the secondary network of the heat station at the next moment is corrected through a heat station room temperature feedback iterative calculation adaptation formula'2gaAnd (4) increasing, namely guiding the heating station to supply heat according to the increased correction value, and storing the corrected water supply temperature of the period a in a temperature supply database. After the database receives new data every time, the updated data is used for fitting the prediction model again to obtain a latest correction model under the new database, and errors are corrected in time by combining the deviation between the actual room temperature of the user side and the set room temperature of the thermal power station, so that the on-line dynamic supply and demand balance between the tail end and the thermal power station is achieved.
2) Feedback correction for heat source:
the method comprises the steps of uploading collected primary network heat supply parameters to a monitoring management control platform, predicting water supply temperature t '1 gj of the next period (j period) by combining online collected prediction required data through a model algorithm pre-loaded on a server, dynamically correcting the j period t' 1gj predicted by the model according to a room temperature feedback iterative calculation adjustment formula of a heat source to obtain indoor temperature and a heat source j period actual required water supply temperature t1gj after set temperature feedback correction, guiding heat supply of the heat source by using the corrected actual required water supply temperature t1gi, and uploading the actual required water supply temperature t1gj of the j period to a historical temperature supply database (the heat source and a heat station are respectively provided with a historical temperature supply database for storing temperature supply data and correcting a prediction model in real time).
The actual water supply temperature before the regulation of the heat source and the three types of heat stations, the predicted water supply temperature during the regulation, the corrected water supply temperature, the indoor temperature and the outdoor temperature are shown in fig. 5. It can be seen from the figure that the corrected water supply temperature can better respond to the change of the outdoor temperature and the indoor temperature, and the fluctuation of the room temperature is obviously reduced and is closer to the target room temperature value when the actual heat supply is guided by correcting the temperature during the regulation and control period.
The regulation and control method of the invention increases the regulation research of the whole network balance on the basis of considering the regulation of the heat source and the heating power station. The aim of the whole network balance adjustment is to realize the supply and demand matching of the primary network and the secondary network and the pipe network stability by adjusting the parameters of the heating system. In order to achieve the aim, a regulation and control period is required to be determined, time nodes are required to be regulated, heat supply parameters are subjected to periodic dynamic prediction, the target value of the room temperature of a user is taken as a center, the predicted water supply temperature is subjected to feedback correction by utilizing the room temperature, and finally the whole network balance regulation based on load prediction and room temperature feedback correction is formed.
Nothing in this specification is said to apply to the prior art.
Claims (6)
1. A total network balance adjustment method based on load prediction and room temperature feedback correction is characterized in that a heat supply system is taken as a whole, meanwhile, comprehensive consideration is carried out on four aspects of a heat source, a heating station, a network and a terminal, total network adjustment is formulated on the basis of the theoretical supply and demand balance of the heat source, the heating station and a user, the target value of the room temperature of the user is taken as the center, the room temperature is used for carrying out feedback correction on the predicted water supply temperature, and a total network balance adjustment and control route based on load prediction is obtained.
2. The conditioning method according to claim 1, characterized in that for the primary network, the fluid flow times from the heat source to the thermal stations are analyzed computationally, so as to determine a heat source conditioning cycle; and regarding the secondary side, considering the comprehensive thermal inertia of the building, and taking the comprehensive delay time, namely the difference between the occurrence time of the indoor temperature peak valley and the occurrence time of the outdoor temperature peak valley as a characterization parameter of the comprehensive thermal inertia in the aspect of temperature change, thereby determining the regulation and control period of the thermal station.
3. The adjusting method according to claim 1, characterized in that the specific steps of the adjusting method are as follows:
s1, respectively predicting loads of a thermal power station and a heat source by using an MLR (maximum likelihood ratio) and a GRNN (generalized regression neural network) network model, and establishing a thermal power station load prediction model and a heat source load prediction model;
s2, determining a regulation and control period a of the heating power station according to the comprehensive heat inertia of the building, and determining a heat source regulation and control period j according to the delay time of a pipe network;
s3, after determining the regulation and control cycles of the heat source and the heat station, respectively establishing time point prediction models for heat load data of the heat source and the heat station at different regulation moments in one day through a GRNN (generalized regression neural network) and an MLR (maximum likelihood ratio), and finding out corresponding time points with highest predicted value precision in the determined corresponding regulation and control cycles, namely the optimal regulation time node of the heat source and the optimal regulation time node of the heat station;
s4, performing room temperature feedback iterative calculation on the predicted temperature supply of the heat station to obtain an adjustment formula (4);
wherein, t'2gIs secondary side predicted supply water temperature t'2hPredicting the return water temperature for the secondary side, wherein the two predicted temperatures are obtained through a thermal station load prediction model in the step S1; t is tnsIndicates a set room temperature; kr、KbIs the heat transfer coefficient of a radiator and the comprehensive heat transfer coefficient of a building envelope, W/(m)2·℃)；Fr、FbThe heat dissipation area of the radiator and the heat transfer area of the building envelope are set; t is tn、twIndoor temperature and outdoor temperature; t is t2g、t2hSupplying water and returning water to the secondary network heating power station; m is2The fitting coefficient is obtained by performing linear fitting by taking the difference between the secondary side predicted water supply temperature and the actual water supply temperature as an independent variable and the difference between the secondary side predicted return water temperature and the actual return water temperature as a dependent variable; Δ tStationA difference between the predicted supply water temperature and the actual supply water temperature for the secondary side;
s5, performing room temperature feedback iterative calculation and adjustment on the heat source prediction temperature to obtain an adjustment formula (7):
wherein, t'1gjThe predicted required water supply temperature of the heat source is obtained through prediction of the heat source load prediction model in the step S1;
heat source required feed water temperature t 'predicted in j-th cycle'1gjSetting the room temperature tnsIndoor temperature tnAnd outdoor temperature twAnd the supply-return average temperature t of the j-th period on the heat source side1pjObtaining the temperature of the heat source side water supply fed back by the room temperature after adjustment;
and S6, after specific regulation and control periods and time nodes of the heat source and the heat station are obtained according to the process, the feedback correction is carried out on the predicted water supply temperature of the heat source according to a room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat source in the step S5 by taking the room temperature target value of the user as the center, meanwhile, the feedback correction is carried out on the predicted water supply temperature of the heat station according to the room temperature feedback iterative calculation and adjustment formula for the predicted temperature supply of the heat station in the step S4, and finally, a whole network balance regulation and control route based on load prediction is formed.
4. The adjusting method according to claim 3, wherein in step S6, the two feedback corrections are:
for a thermal station: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: determining the actual indoor temperature tnWhether or not to reach the set room temperature tnsIf equal, the plant feed water temperature is set to the feed water temperature t 'predicted by the plant load prediction model in step S1'2gi；
If not, continuously judging the actual indoor temperature tnWhether it is greater than the set room temperature tnsIf not, the temperature of the water supplied to the thermal station is set to t'2ga+ΔtStation(ii) a If the temperature is higher than the preset temperature, the water supply temperature of the heat station is set to t'2ga-ΔtStation，ΔtStationNamely the predicted temperature at the set room temperature obtained by the room temperature feedback iterative calculation adjustment formula of the predicted temperature of the heating stationThe difference value of the water temperature and the actual water supply temperature is obtained, after the database of the thermal power station receives new data each time, the adjusted data is used for fitting the thermal power station load prediction model again to obtain the latest correction model under the new database, and then the secondary side water supply temperature of the next regulation and control period is predicted;
for the heat source: determining a user-set room temperature tnsAnd dynamically feeding back the indoor temperature of the user in a regulation and control period: determining the actual indoor temperature tnWhether or not to reach the set room temperature tnsIf equal, the plant water supply temperature is set to the water supply temperature t 'predicted by the heat source load prediction model in step S1'1gj；
If not, regulating and controlling the period t 'for the prediction j'1gjPerforming dynamic debugging to obtain the actual required water supply temperature of the regulation and control period of the heat source j after the indoor temperature and the set temperature are debugged, and obtaining the primary network water supply temperature t by the heat source according to a formula (7)1gjGuiding actual heat supply;
the two are simultaneously used for realizing source-network balance.
5. The adjustment method according to claim 3,
in step S1, an MLR multiple linear regression is used to establish a thermal station load prediction model, where the expression of the regression model is:
wherein, thetai(i-0, 1,2, …, n) is a model regression coefficient, Xi(i is 0,1,2, …, n) is the eigenvalue of the ith sample (X0 is 1 when i is 0), and the sample is the heating history data of the thermal station;
predicting the load of the heating power station;
establishing a heat source load prediction model by using a GRNN network model, wherein the expression is as follows:
wherein, Xi，YiObtaining the observed value from historical data; n is the sample volume; σ is a smoothing factor;
and the predicted value is the heat source load.
6. The adjusting method according to claim 3, characterized in that the heat source regulation cycle is 12 hours, and the adjusting time of the thermal station supplying heat to the energy-saving floor heating, the energy-saving radiator and the non-energy-saving radiator in the heat supply network is respectively set to 12 hours, 8 hours and 6 hours; the optimal regulation and control time nodes of the heating power station for supplying heat to the energy-saving floor heating system, the energy-saving radiator and the non-energy-saving radiator are respectively 8: 00/20: 00. 6:00/14:00/22:00 and 4:00/10:00/16:00/22: 00; the optimal time node for heat source regulation is 8: 00/20: 00.
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