Heat exchange station load prediction and control method based on indoor temperature

Abstract

The present invention provides a load prediction and control method for a heat exchange station based on indoor temperature, which belongs to the field of load analysis technology, including: clarifying the location of each user in the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings. According to the user's information feedback and the location positioning of related equipment, the activity pattern of each user is clarified in combination with big data. The heat demand of each building in the future period is analyzed, and the respective heating and hot water supply plans are planned in combination with the activity patterns. The load of the heat exchange station in the future period is calculated according to the supply plan corresponding to each user, and the heating and hot water supply speed and supply amount are adjusted by dynamically controlling the valve opening on the basis of meeting the comfort according to the activity patterns. The load prediction and control method for a heat exchange station based on indoor temperature provided by the present invention can provide a realistic basis for accurate load prediction, and greatly reduce energy consumption, ensure comfort, and protect the environment.

Description

Technical Field

[0001] The present invention belongs to the technical field of load analysis, and more specifically, relates to a method for predicting and controlling the load of a heat exchange station based on indoor temperature. Background Art

[0002] The heat exchange station is the intermediate link to realize the distribution of energy from the primary network (heat source) to the secondary network (user). In recent years, with the development of social economy, the scale of regional heating systems has become larger and larger. Some multi-source common network systems are close to hundreds of millions of square meters, and there are nearly a thousand heat exchange stations. Intelligence has become the mainstream direction of the development of modern temperature control systems. Especially in recent years, temperature control systems have been applied to all aspects of people's lives. At present, the main goal of the centralized heating system is to ensure the indoor temperature of heat users. If the indoor temperature of the user can be detected in real time and the indoor temperature can be controlled on the spot, the main goal of the heating system can be achieved. Then the problem that follows is how to match supply and demand and accurately control according to the heat source of the heating system, the structure of the heat network and the characteristics of the heat exchange station, and optimize the system operation to ensure the reasonable operating parameters of the indoor temperature control point.

[0003] Although there are several existing methods for predicting and analyzing the load of heat exchange stations, the above methods are more of a prediction and analysis of paper data, and do not conduct detailed detection and summary of the factors affecting the load, resulting in low load prediction accuracy, which is reflected in the user's building as too high or too low indoor temperature. In addition, the inability to properly adjust the hot water supplied for heating leads to serious waste of heat exchange station resources and environmental pollution. Summary of the invention

[0004] The purpose of the present invention is to provide a heat exchange station load prediction and control method based on indoor temperature, aiming to solve the problem that the indoor temperature is too high or too low due to the low accuracy of heat exchange station load prediction, and the waste of heat exchange station resources seriously pollutes the environment.

[0005] To achieve the above object, the technical solution adopted by the present invention is: to provide a heat exchange station load prediction and control method based on indoor temperature, including:

[0006] Clarify the location of each user within the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings, including the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building;

[0007] Identify the ideal indoor temperature setting values ​​for each user, and analyze and predict weather conditions in the coming period;

[0008] Based on the information feedback of the users and the location positioning of the relevant devices, the activity patterns of the users are clarified in combination with big data;

[0009] Analyze the heat demand of each building in the future with the goal of maintaining the set value, and plan the heating and hot water supply plan for each building in combination with the activity rules;

[0010] The load of the heat exchange station in the future is calculated according to the supply plan corresponding to each user, and the heating hot water supply speed and supply amount are adjusted by dynamically controlling the valve opening on the basis of meeting the comfort according to the activity rules.

[0011] In a possible implementation, the location of each user in the area under the jurisdiction of the heat exchange station and the basic information of the corresponding building include:

[0012] Identify the heating system that delivers hot water to each of the buildings in the area, and conduct real-time detection and feedback on the operation of the heating system;

[0013] The temperature and flow rate of hot water flowing into and out of the building are detected in real time, and the determined information is uploaded.

[0014] In a possible implementation, the basic conditions include the location pattern of each of the buildings, the thermal insulation of the buildings, and the location and specifications of the heating equipment in the buildings, including:

[0015] Identify the type and layout of the heating equipment; analyze and determine the composition of the insulation materials in the building and the air tightness of the entire building, and finally analyze the impact of the heating equipment on the building temperature.

[0016] In a possible implementation, the final analysis of the influence of the heating equipment on the building temperature includes:

[0017] The temperature change in the building is simulated when hot water of different temperatures and flow rates flows into the heating equipment under different external environments.

[0018] In a possible implementation, the determining of the ideal setting value of the indoor temperature for each user and analyzing and predicting the weather conditions in the next period of time includes:

[0019] A comfortable temperature range is set by each user, and the temperature range is the set value;

[0020] The changes in outside temperature and wind speed in the future period are analyzed, and the influence of the outside environment on the internal temperature of each building is determined in combination with the location of each building.

[0021] In a possible implementation, the determining the activity pattern of each user based on the user's information feedback and the location of the relevant device in combination with big data includes:

[0022] Preliminarily infer the activity range and route of the user based on the basic information input by the user and the big data;

[0023] Based on the real-time feedback of the user's current location from relevant equipment, the user's activity content is determined in combination with the inferred activity range and route, and the time of arrival and departure at the corresponding building is predicted.

[0024] In a possible implementation, combining the inferred activity range and route to determine the activity content of the user and predicting the time of arriving at and leaving the corresponding building includes:

[0025] Determine the time when the user arrives at the building and the time the user stays in the building based on the activity pattern;

[0026] According to the current external ambient temperature, the amount of heat required to maintain the building at the set value and the rate at which the required heat is released are analyzed, and the hot water flowing into the building is dynamically adjusted through the hot water valve corresponding to the building.

[0027] In a possible implementation, analyzing the heat demand of each building in a future period of time with the goal of maintaining the set value includes:

[0028] According to the basic situation and the changes in the environment in the future, the demand for heat of the building is determined with the goal of maintaining the set value, and the temperature value of the hot water flowing into the building and the flow rate in different time periods are determined according to the demand.

[0029] In a possible implementation, analyzing the heat demand of each building in a future period of time with the goal of maintaining the set value includes:

[0030] According to the set values ​​of each user and the basic conditions of each building, the demands in the area are integrated to obtain the load of the heat exchange station, and different hot water supply solutions are fitted according to the determined load;

[0031] The most economical supply scheme is selected as the hot water supply plan.

[0032] In a possible implementation, adjusting the heating hot water supply speed and supply amount by dynamically controlling the valve opening according to the activity law on the basis of satisfying comfort includes:

[0033] Analyze the heat transfer between the building and the external environment under the current situation, clarify the amount of heat loss in the future and the amount of heat that the building needs to supplement, and predict the changes in load in the region and the corresponding energy gap.

[0034] The beneficial effect of the heat exchange station load prediction and control method based on indoor temperature provided by the present invention is that: compared with the prior art, the heat exchange station load prediction and control method based on indoor temperature of the present invention first clarifies the location of each user in the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings, including the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building. Users need to select preset values ​​according to their own conditions. In actual application, the activity patterns of each user are clarified based on the user's information feedback and the location positioning of related equipment combined with big data.

[0035] In the actual hot water supply process, it is necessary to analyze the heat demand of each building in the future with the goal of maintaining the set value, and plan their own heating hot water supply plans in combination with the activity rules. In order to further reduce energy consumption, the heating hot water supply speed and supply volume are adjusted by dynamically controlling the valve opening on the basis of meeting comfort. This application can provide a realistic basis for accurate load prediction by carefully analyzing the building, and through the set value and intelligent control of the valve, it can greatly reduce energy consumption, ensure comfort and protect the environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0037] Figure 1 A flow chart of a method for predicting and controlling heat exchange station load based on indoor temperature provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] See also Figure 1 The load prediction and control method of the heat exchange station based on indoor temperature provided by the present invention is now described. The load prediction and control method of the heat exchange station based on indoor temperature includes:

[0040] Clarify the location of each user within the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings. The basic conditions include the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building.

[0041] Clarify each user's ideal setting value for indoor temperature, and analyze and predict weather conditions in the future.

[0042] Based on user information feedback and the location of related equipment, combined with big data, the activity patterns of each user are clarified.

[0043] With the goal of maintaining the set value, the heat demand of each building in the future is analyzed, and the respective heating and hot water supply plans are planned based on the activity patterns.

[0044] The load of the heat exchange station in the future is calculated based on the supply plan corresponding to each user. At the same time, the heating hot water supply speed and supply volume are adjusted by dynamically controlling the valve opening based on the activity rules and on the basis of meeting the comfort requirements.

[0045] The beneficial effect of the heat exchange station load prediction and control method based on indoor temperature provided by the present invention is that: compared with the prior art, the heat exchange station load prediction and control method based on indoor temperature of the present invention first clarifies the location of each user in the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings, including the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building. Users need to select preset values ​​according to their own conditions. In actual application, the activity patterns of each user are clarified based on the user's information feedback and the location positioning of related equipment combined with big data.

[0046] In the actual hot water supply process, it is necessary to analyze the heat demand of each building in the future with the goal of maintaining the set value, and plan their own heating hot water supply plans in combination with the activity rules. In order to further reduce energy consumption, the heating hot water supply speed and supply volume are adjusted by dynamically controlling the valve opening on the basis of meeting comfort. This application can provide a realistic basis for accurate load prediction by carefully analyzing the building, and through the set value and intelligent control of the valve, it can greatly reduce energy consumption, ensure comfort and protect the environment.

[0047] The heat exchange station is the intermediate link for distributing energy from the primary network (heat source) to the secondary network (user). In recent years, with the development of social economy, the scale of regional heating systems has become larger and larger. Some multi-source common network systems are close to hundreds of millions of square meters, and there are nearly a thousand heat exchange stations. Existing large-scale regional heating systems mostly use the secondary network water temperature to control the heat supply of end users to meet the thermal comfort needs of users. However, by observing and analyzing the historical data of the heat exchange station, it can be found that the heat output of the heat exchange station does not meet the thermal and hydraulic balance goals of the heat network very well. For example, some heat exchange stations control the return water temperature of the secondary network, but the return water temperature is the result of all the heat transfer processes of the secondary network. This heat transfer process includes both controllable heat transfer processes and various interferences (uncontrollable) from the indoor and outdoor environments. It is impossible to accurately obtain the return water temperature setting value, and it is impossible to achieve accurate control of the heat supply and indoor temperature. The final result is that the energy consumption of the heat exchange station is high, the room temperature fluctuation range is large, and the indoor temperature control accuracy deviation is large. In addition, some setting parameters are difficult to obtain accurately, and the user's indoor temperature fluctuation cannot be effectively controlled, and the user's thermal comfort experience is poor.

[0048] Although the existing municipal heating system has solved the problem of insufficient heating to a certain extent, it is not very intelligent, and the real-time temperature requirements of users are not clear. In addition, at the beginning of the heating season, the heating temperature is often insufficient and users complain and adjust the system. When the weather changes too much and the temperature changes greatly, the intelligent adjustment of the temperature in real time is impossible.

[0049] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, clarifying the location of each user in the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings includes:

[0050] Identify the heating system that delivers hot water to each building in the area, and conduct real-time detection and feedback on the operation of the heating system.

[0051] The temperature and flow rate of hot water flowing into and out of the building are detected in real time, and the determined information is uploaded.

[0052] As a major node in the heat transfer process of the overall heating system, the indoor temperature is affected by the operating parameters of the heating system and various interferences. Although from the perspective of system operation, the indoor temperature is the only result of the central heating system under a certain parameter state, it is almost impossible to accurately calculate the dynamic indoor temperature value of the heat user.

[0053] From the perspective of the control accuracy of the centralized heating system, the indoor temperature of heat users does not require very precise control, and usually a control accuracy of ±0.5-1°C can meet user needs. Therefore, studying the dynamic characteristics of the indoor temperature of the heating system under different operating and interference conditions is not only necessary for the automatic control of the indoor temperature, but also has important significance for meeting user needs, guiding the reasonable operation of the heating system, and optimizing system operation.

[0054] Therefore, how to achieve accurate prediction of the load of the heat exchange station, reduce resource consumption to protect the environment while meeting the heating needs of users, and reduce the operating cost of the heat exchange station is a technical problem that needs to be solved urgently.

[0055] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, the basic conditions include the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building, including:

[0056] Identify the types and layout of heating equipment; analyze and determine the composition of insulation materials in the building and the airtightness of the entire building, and finally analyze the impact of heating equipment on building temperature.

[0057] With the development of intelligence and big data, how to accurately adjust the heating load according to the actual situation is a technical problem that needs to be solved urgently. If no prediction and data analysis are performed and all users are treated uniformly, some residential areas in the same area have higher indoor temperatures and users usually need to open windows to dissipate heat, while some users have too low indoor temperatures and need to turn on electric furnaces or air conditioners to ensure the indoor temperature is stable and appropriate. This is because the insulation heating methods and specifications of different users' houses are different.

[0058] Based on the above reasons, how to carry out targeted and detailed division of users of different types and areas, and how to better reduce resource consumption to ensure stable and suitable indoor temperature are urgent problems to be solved. This application, through detailed analysis and induction of users, has predicted environmental factors such as weather, and ultimately can achieve the above effects.

[0059] In some embodiments of the method for predicting and controlling the load of a heat exchange station based on indoor temperature provided in the present application, the final analysis of the impact of the heating equipment on the building temperature includes:

[0060] Simulate the temperature changes inside the building when hot water of different temperatures and flows into the heating equipment under different external environments.

[0061] The reality is that each user's building specifications are different, the building layout is different, the building insulation also varies greatly, and the heating equipment layout and specifications will also vary. The ultimate result of this difference is that when the same heat is supplied to users of the same building area, the final indoor temperature will vary greatly, and this difference will directly affect the user's comfort.

[0062] So how to accurately analyze the building to determine its most suitable heating capacity, reduce the user's heating costs, reduce the load of the heat exchange station and reduce resource consumption is a very important issue. To this end, it is necessary to conduct detailed inspection and analysis of the buildings in the area, and conduct on-site inspections when necessary. Understand the location, specifications and shape of the building equipment heating equipment, analyze and summarize the overall thermal insulation of the building. Once the above basic conditions are determined, the indoor and outdoor heat transfer efficiency will also be clear, so the heating capacity and the final indoor temperature can be predicted.

[0063] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, the ideal setting value of the indoor temperature for each user is clarified, and the weather conditions in the next period of time are analyzed and predicted, including:

[0064] Each user sets a comfortable temperature range, which is the set value;

[0065] Analyze the changes in outside temperature and wind speed in the future, and determine the impact of the external environment on the internal temperature of each building based on the location of each building.

[0066] The main factors affecting indoor temperature are outdoor temperature and indoor heat. If the building has good thermal insulation, the indoor heat loss will be small and the indoor temperature will drop slowly. If the indoor heat loss is large, the indoor temperature will be difficult to maintain, which will cause the indoor temperature to drop significantly. It is for the above reasons that it is necessary to predict the weather conditions in the future. After the prediction is completed, it is necessary to calculate the corresponding heat based on the analysis and detection of the building's thermal insulation in order to maintain the corresponding temperature.

[0067] To this end, a preliminary model of the building can be created, and the building's insulation coefficient can be established based on the insulation test and analysis. Once the above is completed, the heat required to maintain a stable indoor temperature can be inferred based on the outdoor temperature changes in the future. Once the above conditions are determined, the load of the heat exchange station is preliminarily determined.

[0068] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, based on user information feedback and location positioning of related equipment, the activity patterns of each user are clarified in combination with big data, including:

[0069] Based on the basic information input by the user and combined with big data, the activity range and route are preliminarily inferred;

[0070] Based on the real-time feedback of the user's current location from relevant devices, combined with the inferred activity range and route, the user's activity content is determined and the time of arrival and departure from the corresponding building is predicted.

[0071] The hot water discharged from the heat exchange station needs to flow into heating facilities such as radiators and floor heating pipes through valves and related pipes. The main factors affecting the indoor temperature are the location and method of the heating facilities and the control of the valves. Because the amount of hot water flowing into the room is different due to different valve openings, it will directly affect the indoor temperature.

[0072] The existing valves of each user can be controlled by the user himself, but usually the valve is in a fully open state. Only when the heating temperature is too high will the valve opening be appropriately reduced. In order to ensure that the users on the top floor can have hot water supply, the hot water pump needs to operate continuously at a high load.

[0073] In order to solve this problem, we can first predict the heat demand of each user based on the outdoor temperature changes and building insulation parameters. Once the above situation is determined, we can judge whether to increase the water temperature or the water flow rate to ensure constant temperature, and achieve controllable temperature regulation by automatically and intelligently adjusting the valve opening.

[0074] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, determining the user's activity content in combination with the inferred activity range and route and predicting the time of arriving at and leaving the corresponding building include:

[0075] Determine the time when users arrive at the building and the time they stay in the building based on the activity patterns;

[0076] According to the current external ambient temperature, the heat required to maintain the building at the set value and the required heat release rate are analyzed, and the hot water flowing into the building is dynamically adjusted through the building's corresponding hot water valve.

[0077] The actual situation is that the user's valve opening remains at a specific angle for a long time, but the outdoor temperature is different at different times of the day and changes in real time. However, the temperature of the heating hot water supplied at this time will not change quickly. If no changes are made, the indoor temperature cannot be guaranteed to be stable, and there is a high possibility that the indoor temperature is too high.

[0078] More importantly, when there is no activity in the room, heating is no longer needed. If no treatment is taken, it will undoubtedly cause huge waste. It is based on the above situation that it is necessary to clarify the activity patterns of people in different buildings and different rooms. After mastering the above rules, the valves on the heating pipes in the building can be automatically adjusted through artificial intelligence, ultimately achieving effective energy conservation.

[0079] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, analyzing the heat demand of each building in the future period of time with the goal of maintaining the set value includes:

[0080] According to the basic situation and the changes in the environment in the future, the building's demand for heat is determined with the goal of maintaining the set value. Based on the demand, the temperature of the hot water flowing into the building and the flow rate in different time periods are determined.

[0081] The heat exchange station needs to heat the circulating water. If the water temperature is high, the corresponding heat will be lost faster, and the final result is that the indoor temperature can be quickly increased. In the same period of time, if the water temperature is appropriately lowered and the water flow rate is increased, the same heating effect can be achieved. It is for the above reasons that how to achieve the control of water temperature and flow in a more economical and environmentally friendly way is a technical problem that needs to be solved.

[0082] In this application, the thermal insulation of the building has been reliably tested and analyzed. At this time, it is only necessary to analyze the changes in outdoor temperature over a period of time in the future, as well as the activity patterns and temperature settings of each user in the area, simulate the energy consumption at different flow rates and temperatures, and ultimately determine the most economical heating plan.

[0083] In some embodiments of the indoor temperature-based heat exchange station load prediction and control method provided in the present application, analyzing the heat demand of each building in the future period of time with the goal of maintaining the set value includes:

[0084] According to the set values ​​of each user and the basic conditions of each building, the demands in the area are integrated to obtain the load of the heat exchange station, and different hot water supply plans are fitted according to the determined load;

[0085] Select the most economical supply plan as the hot water supply plan.

[0086] In order to explain in more detail, relevant personnel first need to test and check the thermal insulation performance and building structure of each building in the area under the jurisdiction of the heat exchange station. After confirmation, build a heat exchange model, that is, create a rough heat exchange model of each building in the area. When the model is created, it is necessary to predict the weather conditions in the future, mainly to predict the changes in the external ambient temperature in the future.

[0087] After the above basic conditions are determined, the user and relevant personnel set the target temperature. The target temperature is the temperature range that needs to be stable indoors. After the setting is completed, the heat required to maintain the above temperature indoors is calculated. Through factors such as heat transfer efficiency, the temperature and flow rate of the water in the heat exchange station are finally calculated.

[0088] Once the above situation is determined, through the user's settings and the monitored user's activity trajectory, it is ultimately possible to reduce energy consumption as much as possible while ensuring user comfort.

[0089] In some embodiments of the method for predicting and controlling the load of a heat exchange station based on indoor temperature provided in the present application, adjusting the heating hot water supply speed and supply amount by dynamically controlling the valve opening according to the activity rules on the basis of satisfying the comfort includes:

[0090] Analyze the heat transfer between the building and the external environment under the current situation, clarify the amount of heat loss in the future and the amount of heat that the building needs to supplement, and predict the changes in load in the region and the corresponding energy gap.

[0091] In actual implementation, users can first upload their own information, including the arrangement of working hours, the number of people indoors and related activity patterns. Once the above situation is determined, the information uploaded by the user is combined with big data and the positioning feedback of related mobile devices, and finally the activity patterns of each user indoors can be inferred. After the above is determined, combined with the indoor structure and thermal insulation of the building, the principle is to ensure the temperature set by the user, and the goal is to reduce energy consumption. During the time when the user leaves the building, the valve opening is remotely and intelligently reduced or closed, and the temperature is returned to an appropriate level by adjusting the valve accordingly before the user arrives indoors.

[0092] At this time, it is necessary to use user records and big data to infer the time interval between users leaving the building, and remotely control the valves to achieve efficient use of resources.

[0093] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A heat exchange station load prediction and control method based on indoor temperature, characterized in that: include: Clarify the location of each user within the area under the jurisdiction of the heat exchange station and the basic conditions of the corresponding buildings, including the location pattern of each building, the thermal insulation of the building, and the location and specifications of the heating equipment in the building; Identify the ideal indoor temperature setting values ​​for each user, and analyze and predict weather conditions in the coming period; Based on the information feedback of the users and the location positioning of the relevant devices, the activity patterns of the users are clarified in combination with big data; Analyze the heat demand of each building in the future with the goal of maintaining the set value, and plan the heating and hot water supply plan for each building in combination with the activity rules; Calculate the load of the heat exchange station in the future according to the supply plan corresponding to each user, and adjust the heating hot water supply speed and supply amount by dynamically controlling the valve opening on the basis of meeting the comfort according to the activity rules; The basic information of the location of each user and the corresponding building in the area under the jurisdiction of the heat exchange station includes: Identify the heating system that delivers hot water to each of the buildings in the area, and conduct real-time detection and feedback on the operation of the heating system; Real-time detection of the temperature and flow rate of hot water flowing into and out of the building, and uploading the determined information; The basic conditions include the location pattern of each of the buildings, the thermal insulation of the buildings, and the location and specifications of the heating equipment in the buildings, including: Identify the type and layout of the heating equipment; analyze and determine the composition of the insulation materials in the building and the air tightness of the entire building, and finally analyze the impact of the heating equipment on the building temperature; The analysis of the heat demand of each building in the future with the goal of maintaining the set value includes: According to the basic situation and the changes in the environment in the future, the demand for heat of the building is determined with the goal of maintaining the set value, and the temperature value of the hot water flowing into the building and the flow rate in different time periods are determined according to the demand; The analysis of the heat demand of each building in the future with the goal of maintaining the set value includes: According to the set values ​​of each user and the basic conditions of each building, the demands in the area are integrated to obtain the load of the heat exchange station, and different hot water supply solutions are fitted according to the determined load; The most economical supply scheme is selected as the hot water supply plan.

2. The method for predicting and controlling the load of a heat exchange station based on indoor temperature according to claim 1, characterized in that: The final analysis of the influence of the heating equipment on the building temperature includes: The temperature change in the building is simulated when hot water of different temperatures and flow rates flows into the heating equipment under different external environments.

3. The method for predicting and controlling the load of a heat exchange station based on indoor temperature according to claim 1, characterized in that: The determining of the ideal setting value of the indoor temperature for each user and the analysis and prediction of the weather conditions in the future include: A comfortable temperature range is set by each user, and the temperature range is the set value; The changes in outside temperature and wind speed in the future period are analyzed, and the influence of the outside environment on the internal temperature of each building is determined in combination with the location of each building.

4. The method for predicting and controlling the load of a heat exchange station based on indoor temperature according to claim 1, characterized in that: The method of clarifying the activity patterns of each user based on the user's information feedback and the location positioning of related devices in combination with big data includes: Preliminarily infer the activity range and route of the user based on the basic information input by the user and the big data; Based on the real-time feedback of the user's current location from relevant equipment, the user's activity content is determined in combination with the inferred activity range and route, and the time of arrival and departure at the corresponding building is predicted.

5. The method for predicting and controlling the load of a heat exchange station based on indoor temperature according to claim 4, characterized in that: The determining of the user's activity content by combining the inferred activity range and route and predicting the time of arriving at and leaving the corresponding building comprises: Determine the time when the user arrives at the building and the time the user stays in the building based on the activity pattern; According to the current external ambient temperature, the amount of heat required to maintain the building at the set value and the rate at which the required heat is released are analyzed, and the hot water flowing into the building is dynamically adjusted through the hot water valve corresponding to the building.

6. The method for predicting and controlling heat exchange station load based on indoor temperature according to claim 1, characterized in that: The method of adjusting the heating hot water supply speed and supply amount by dynamically controlling the valve opening according to the activity rules on the basis of satisfying comfort comprises: Analyze the heat transfer between the building and the external environment under the current situation, clarify the amount of heat loss in the future and the amount of heat that the building needs to supplement, and predict the changes in load in the region and the corresponding energy gap.

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