Cold and heat combined supply load management system and method based on self-adaptive agent

Abstract

The invention discloses a combined cooling and heating supply load management system and method based on a self-adaptive agent, and relates to the technical field of combined cooling and heating supply management, and the system comprises a combined cooling and heating supply management module, a prediction data collection module, a user side load analysis module and a parameter adjustment management module. The combined cooling and heating supply management module is used for controlling air source heat pump equipment to achieve the combined cooling and heating supply function, predicting the load needed by a user side and adjusting and managing parameters of the air source heat pump equipment, and the prediction data collection module is used for collecting water supply temperature data and historical prediction time data. The user side load analysis module is used for analyzing historical water supply temperature change time data and planning load prediction time, the parameter adjustment management module is used for carrying out load prediction according to the planned time and carrying out heat pump equipment parameter adjustment management, and load prediction and heat pump parameter adjustment are carried out in an intelligent agent self-adaptive learning mode; and the prediction time is planned, so that the unnecessary data processing workload is reduced while the energy supply and demand are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of combined cooling and heating management, and in particular to a combined cooling and heating load management system and method based on an adaptive intelligent agent. Background Art

[0002] An air source heat pump is an energy-saving device that uses high-level energy to make heat flow from low-level heat source air to high-level heat source. It can convert low-level heat energy that cannot be directly used into high-level heat energy that can be used, thereby achieving the purpose of saving some high-level energy. In traditional energy management systems, the operating efficiency of air source heat pumps is often limited by the delay in responding to environmental variables and non-optimal energy allocation. Under changing climate conditions, heat pumps often find it difficult to adjust their operating status in time to adapt to environmental changes, affecting the overall energy efficiency and heat pump performance. Through intelligent adaptive learning to predict loads and adjust heat pump parameters in time, it is possible to more accurately match energy supply and demand, thereby reducing energy waste without sacrificing user comfort and improving the overall energy efficiency of heat pumps. However, in the prior art, real-time collection of environmental data and real-time load prediction are generally used to predict the load required by the user end. However, this ignores the fact that the load required by the user end may remain unchanged for a long time. If the results of the previous and subsequent predictions remain unchanged, there is no need to adjust the heat pump parameters in this case. In view of this situation, the prior art does not set an appropriate time for data prediction, and is unable to reduce unnecessary data processing workload while ensuring energy supply and demand, thereby reducing the necessity and significance of system prediction and data processing. Summary of the invention

[0003] The object of the present invention is to provide a cooling and heating combined supply load management system and method based on an adaptive intelligent agent to solve the problems raised in the prior art.

[0004] To achieve the above object, the present invention provides the following technical solutions: a cooling and heating combined supply load management system based on an adaptive intelligent agent, comprising a cooling and heating combined supply management module, a prediction data acquisition module, a user-side load analysis module and a parameter adjustment management module; The combined cooling and heating management module is used to control the air source heat pump equipment to realize the combined cooling and heating function and to predict the load required by the user end, and to adjust and manage the parameters of the air source heat pump equipment; The prediction data collection module is used to collect water supply temperature data and historical prediction time data predicted by default in the past; The user-side load analysis module is used to analyze historical water supply temperature change time data, plan and adjust load forecast time; The parameter adjustment management module is used to perform load forecasting according to the planned time and to perform parameter adjustment management of the air source heat pump equipment. The parameter refers to the load required by the user end, that is, the water supply temperature of the air source heat pump equipment.

[0005] Preferably, the combined cooling and heating management module includes an air source heat pump control unit, a user-side load prediction unit and a water supply temperature adjustment unit; The air source heat pump control unit is used to control the air source heat pump device to generate hot water or cold water required by the user, and transport the hot water or cold water generated by the heat pump device to the user end through the water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump device through the return pipe for reheating. This process reflects the cold and hot supply function of the air source heat pump device; The user-side load prediction unit is used to perceive environmental data through the adaptive learning method of an intelligent agent, establish a user-side load prediction model using a deep learning algorithm, input environmental data into the model to predict the load required by the user-side, the intelligent agent refers to an intelligent entity, which is a three-dimensional perception, global collaboration, accurate judgment, continuous evolution, and open intelligent system based on the cloud and AI as the core. The intelligent agent itself has adaptive learning capabilities, the deep learning algorithm refers to the Actor-Critic algorithm, which is a deep reinforcement learning algorithm that combines behavioral learning and evaluation feedback, the environmental data includes outdoor temperature, indoor temperature, return water temperature, building attribute data, and heating area data, and the load required by the user-side refers to the water supply temperature; The water supply temperature adjustment unit is used to adjust the water supply temperature of the air source heat pump device to the predicted water supply temperature.

[0006] Preferably, the prediction data acquisition module includes a water supply temperature data acquisition unit and a data prediction time acquisition unit; The water supply temperature data collection unit is used to collect the water supply temperature data predicted every day when the water supply temperature is predicted by the real-time prediction method by default in the past; The data prediction time acquisition unit is used to acquire the time when the corresponding water supply temperature is predicted.

[0007] Preferably, the user-side load analysis module includes a temperature change time analysis unit and a collection prediction time planning unit; The temperature change time analysis unit is used to count the time when the water supply temperature changes predicted every day in the past, obtain the default initial time T for starting the load forecast every day in the past, record the time interval between the time when the water supply temperature changes for the first time after the T moment and T as the time interval of the first change of the water supply temperature, form a historical first data set with the time interval data of the first change of the water supply temperature every day in the past, analyze the time interval data in the historical first data set, predict the time of the first change of the water supply temperature on the latest day, after predicting the time of the first change of the water supply temperature on the latest day, retrieve the water supply temperature change time data of several days whose first change time of the water supply temperature is the same as the predicted time of the first change of the water supply temperature on the latest day, obtain the time when the water supply temperature changes for the first time after the corresponding time of the first change of the water supply temperature, record the time interval between the obtained time and the corresponding time of the first change of the water supply temperature as the time interval of the second change of the water supply temperature, form a historical second data set with the time interval data of the second change of the water supply temperature corresponding to several days, analyze the historical second data set and predict the time of the second change of the water supply temperature on the latest day, and so on to predict the change time of the water supply temperature on the latest day for several times; The collection prediction time planning unit is used to plan the load prediction time and adjust the default real-time prediction method: perceive the environmental data and predict the load required by the user end, that is, the water supply temperature, at the predicted change time of the water supply temperature for several times in the latest day, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time.

[0008] Preferably, the parameter adjustment management module includes a usage time sensing unit, an adjustment time abnormality prediction unit and a water temperature adjustment time adjustment unit; The usage time sensing unit is used to sense the time period when the user uses hot water in the latest day. A water level sensor is installed in the water tank of the air source heat pump equipment. The water level is monitored by the water level sensor. When the water level drops, it is determined that the user is using hot water. When the water level stops dropping, it is determined that the user stops using hot water, thereby being able to sense the time period when the user uses hot water; The adjustment time abnormality prediction unit is used to determine whether the planned load forecast time falls within the sensed time period: if the planned forecast time is within the sensed time period, the corresponding forecast time is marked as abnormal; otherwise, no abnormality is marked; The water temperature regulation time adjustment unit is used to plan the water supply temperature regulation time: if the planned load forecast times are not marked, the water supply temperature of the air source heat pump equipment will be immediately adjusted to the predicted water supply temperature after the water supply temperature is predicted at the corresponding forecast time; if an abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted at the abnormally marked forecast time after it is sensed that the user stops using hot water; if more than one abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted at the latest forecast time among several abnormally marked forecast times after it is sensed that the user stops using hot water.

[0009] A method for managing combined cooling and heating load based on an adaptive intelligent agent comprises the following steps: S100: Control the air source heat pump equipment to realize the combined cooling and heating function and predict the load required by the user end, and adjust and manage the parameters of the air source heat pump equipment; S200: Collecting water supply temperature data and historical prediction time data predicted by default in the past; S300: Analyze historical water supply temperature change time data, plan and adjust load forecast time; S400: Carry out load forecasting according to the planned time and perform parameter adjustment management of air source heat pump equipment.

[0010] Preferably, in step S100: the air source heat pump device is controlled to generate hot water or cold water required by the user, and the hot water or cold water generated by the heat pump device is transported to the user end through a water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump device through a return pipe for reheating. The environmental data is perceived through adaptive learning of the intelligent agent, a user-end load prediction model is established using a deep learning algorithm, the environmental data is input into the model to predict the load required by the user end, and the water supply temperature of the air source heat pump device is adjusted to the predicted water supply temperature.

[0011] Preferably, in step S200: the water supply temperature data predicted every day when the water supply temperature is predicted by the real-time prediction method in the past is collected, and the time when the corresponding water supply temperature is predicted is obtained.

[0012] Preferably, in step S300: retrieve the water supply temperature data predicted every day when the water supply temperature was predicted by the default real-time prediction method in the past, count the time when the water supply temperature predicted every day in the past changes, obtain the default initial time T for starting load forecasting every day in the past, record the time interval between the time when the water supply temperature changes for the first time after time T and T as the time interval of the initial change of the water supply temperature, and form the first historical data set with the time interval data of the initial change of the water supply temperature every day in the past, and obtain the first historical data set as {t1 , t 2 , ..., t k}, where k represents the number of data items in the first historical data set, t k Indicates the time interval of the first change of water supply temperature on the kth day in the past, and establishes the first time interval prediction model: R 1 =λ*t k +(1-λ)*ζ k ; The time interval between the first change time of the water supply temperature on the latest day and T is predicted to be R 1 The first change time of the water supply temperature on the latest day is A 1 : A 1 =T+R 1 , where * represents the multiplication sign, λ represents the smoothing coefficient, 0<λ<1, ζ k represents the exponential smoothing value of the time interval of the first change of the water supply temperature on the kth day in the past. ζ is solved by the following method k :According to 1 =λ*t 1 +(1-λ)*[(t 1 +t 2 +t 3 ) / 3] to calculate the exponential smoothing value of the first change time interval of the water supply temperature on the first day in the past 1 , according to 2 =λ*t 1 +(1-λ)*ζ 1 Calculate the exponential smoothing value ζ of the time interval of the first change of the water supply temperature on the second day in the past 2 , according to 3 =λ*t 2 +(1-λ)*ζ 2 Calculate the exponential smoothing value ζ of the time interval of the first change of the water supply temperature on the second day in the past 3 , and so on, we can gradually solve ζ k , in the prediction, R 1 Afterwards, the first change time of the water supply temperature for a total of m days and T+R 1 Similarly, retrieve the water supply temperature change data corresponding to m days and obtain the temperature change at T+R 1 The time when the water supply temperature changes for the first time, and the obtained time and T+R 1 The time interval between the two changes in water supply temperature is recorded as the time interval between the two changes in water supply temperature for m days. The data of the time interval between the two changes in water supply temperature for m days are combined into the historical second data set. The historical second data set is {U 1 , U 2 , ..., U m}, establish the second time interval prediction model: R 2 =λ*U m +(1-λ)*η m ; Predict the second change time of water supply temperature and T+R for the latest day 1 The time interval is R 2 , the second change time of water supply temperature on the latest day is A 2 : A 2 =T+R 1 +R 2 , η m represents the exponential smoothing value of the time interval of the second change of water supply temperature corresponding to the mth day, η m The solution method and ζ k Similarly, the predicted water supply temperature change time data set for the latest day is {A 1 , A 2 , ..., A n}, A n Indicates the time of the nth change in the water supply temperature of the latest day predicted, according to the data set {A 1 , A 2 , ..., A n} and predict the load required by the user end, that is, the water supply temperature, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time; In order to solve the problem that the real-time prediction method cannot reduce unnecessary data processing workload when the load required by the user side may remain unchanged for a long time, the water supply temperature data predicted by the real-time prediction method in the past and the time data of the water supply temperature change are collected through big data technology. Considering that the initial time of collecting environmental data for load prediction every day set by the system default is the same, the default initial time is used as the benchmark to analyze the changes in the time interval between the predicted water supply temperature change time and the initial time in the historical data, and the first time interval prediction model is established through the exponential smoothing algorithm to predict the time when the water supply temperature of the heat pump equipment changes for the first time on the latest day. Considering that the first change in water supply temperature every day may be different in the past, the predicted time of the first change in water supply temperature on the latest day is used as the benchmark. From the historical data, data with the same time of the first change in water temperature as the predicted time is selected, and the time of the second change in the water supply temperature on the latest day is predicted based on the selected data. Similarly, historical data with the same time as the previous prediction is selected as a reference, and the time of several subsequent changes in the water supply temperature on the latest day is predicted. This effectively improves the accuracy of the data prediction results, senses environmental data according to the predicted time, and predicts the load required by the user, that is, the water supply temperature, to achieve dynamic adjustment of the load prediction time, and then adjusts the water supply temperature of the heat pump according to the load prediction result, analyzes the historical water supply temperature change pattern, and performs load prediction when the water supply temperature may change. This reduces unnecessary data processing workload while ensuring energy supply and demand, and improves the necessity and significance of system prediction and data processing.

[0013] Preferably, in step S400: it is sensed that any time period of the user using hot water in the latest day is [H1, H2], H1 represents the initial time of the corresponding time period, and H2 represents the end time of the corresponding time period, and the judgment data set {A 1 , A 2 , ..., A n} Whether the load forecast time in falls within the time period [H1, H2], and the time A is counted 1 and A 2 falls within the time period [H1,H2], that is, time H1 is between time A 1 Before, and time A 1 At time A 2 Before, and time A 2 Before time H2, set time A 1 and A 2 Mark the abnormality and adjust the water supply temperature of the heat pump equipment to the temperature at time A at time H2. 2 Predicted water supply temperature; After the load forecasting operation is completed, it is further considered that the load forecasting time may be within the time period when the user uses hot water. At this time, the water supply temperature of the heat pump equipment is adjusted immediately after the load required by the user is predicted. The sudden change in temperature may affect the user's hot water usage experience or even cause discomfort. When it is judged that this situation occurs, after the water supply temperature is predicted, it is chosen not to immediately adjust the water supply temperature of the heat pump. Instead, the water supply temperature of the heat pump is adjusted to an appropriate temperature when the user stops using hot water, which effectively avoids the situation where the sudden change in temperature during the use of hot water affects the user's hot water usage experience or even causes discomfort.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses an intelligent agent adaptive learning method to perform load forecasting and parameter adjustment of air source heat pump equipment, effectively ensuring energy supply and demand; the water supply temperature data predicted by the real-time forecasting method in the past and the time data of the water supply temperature change are collected through big data technology. Considering that the system defaults to the same initial time for collecting environmental data for load forecasting every day, the default initial time is used as a benchmark to analyze the changes in the time interval between the predicted water supply temperature change time and the initial time in the historical data, and the first time interval prediction model is established through the exponential smoothing algorithm to predict the time when the water supply temperature of the heat pump equipment changes for the latest day for the first time. Considering that the first change in the water supply temperature every day may be different in the past, the predicted time of the first change in the water supply temperature of the latest day is used as a benchmark. Filter out the data whose initial water temperature change time is the same as the predicted initial change time from the historical data, and continue to predict the time of the second change of the water supply temperature on the latest day based on the filtered data. Similarly, filter out the historical data with the same predicted time as the previous one as a reference, and continue to predict the time of several subsequent changes in the water supply temperature on the latest day, which effectively improves the accuracy of the data prediction results, senses the environmental data according to the predicted time, and predicts the load required by the user end, that is, the water supply temperature, to achieve dynamic adjustment of the load prediction time, and then adjusts the water supply temperature of the heat pump according to the load prediction results, analyzes the historical water supply temperature change law, and performs load prediction when the water supply temperature may change, which reduces unnecessary data processing workload while ensuring energy supply and demand, and improves the necessity and significance of system prediction and data processing; After the load forecasting operation is completed, it is further considered that the load forecasting time may be within the time period when the user uses hot water. At this time, the water supply temperature of the heat pump equipment is adjusted immediately after the load required by the user is predicted. The sudden change in temperature may affect the user's hot water usage experience or even cause discomfort. When it is judged that this situation occurs, after the water supply temperature is predicted, it is chosen not to immediately adjust the water supply temperature of the heat pump. Instead, the water supply temperature of the heat pump is adjusted to an appropriate temperature when the user stops using hot water, which effectively avoids the situation where the sudden change in temperature during the use of hot water affects the user's hot water usage experience or even causes discomfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of a cooling and heating combined power supply load management system based on an adaptive intelligent agent according to the present invention; Figure 2 The present invention is a flow chart of a method for managing cooling and heating combined supply load based on an adaptive intelligent agent. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Embodiment 1: like Figure 1 As shown, this embodiment provides a combined cooling and heating load management system based on an adaptive intelligent agent, the system includes: a combined cooling and heating management module, a prediction data acquisition module, a user-side load analysis module and a parameter adjustment management module; The output end of the combined cooling and heating management module is connected to the input end of the prediction data acquisition module, the output end of the prediction data acquisition module is connected to the input end of the user-side load analysis module, and the output end of the user-side load analysis module is connected to the input end of the parameter adjustment management module; The combined cooling and heating management module is used to control the air source heat pump equipment to realize the combined cooling and heating function and to predict the load required by the user end, and to adjust and manage the parameters of the air source heat pump equipment; The prediction data collection module is used to collect the water supply temperature data and historical prediction time data predicted by the default method in the past; The user-side load analysis module is used to analyze the historical water supply temperature change time data, plan and adjust the load forecast time; The parameter adjustment management module is used to predict the load according to the planned time and perform parameter adjustment management of the air source heat pump equipment. The parameter refers to the load required by the user end, that is, the water supply temperature of the air source heat pump equipment.

[0018] The combined cooling and heating management module includes an air source heat pump control unit, a user-side load prediction unit, and a water supply temperature adjustment unit; The output end of the air source heat pump control unit is connected to the input end of the user-side load prediction unit, and the output end of the user-side load prediction unit is connected to the input end of the water supply temperature adjustment unit; The air source heat pump control unit is used to control the air source heat pump device to generate hot water or cold water required by the user, and transport the hot water or cold water generated by the heat pump device to the user end through the water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump device through the return pipe for reheating. This process reflects the cold and hot supply function of the air source heat pump device; The user-side load prediction unit is used to perceive environmental data through the adaptive learning of intelligent agents, establish a user-side load prediction model using deep learning algorithms, and input environmental data into the model to predict the load required by the user side. Intelligent agents refer to intelligent entities, which are cloud-based, AI-based, and built into a three-dimensional perception, global collaboration, precise judgment, continuous evolution, and open intelligent system. The intelligent agent itself has adaptive learning capabilities. The deep learning algorithm refers to the Actor-Critic algorithm, which is a deep reinforcement learning algorithm that combines behavioral learning and evaluation feedback. Environmental data include outdoor temperature, indoor temperature, return water temperature, building attribute data, and heating area data. The load required by the user side refers to the water supply temperature. The water supply temperature adjustment unit is used to adjust the water supply temperature of the air source heat pump device to the predicted water supply temperature.

[0019] The prediction data acquisition module includes a water supply temperature data acquisition unit and a data prediction time acquisition unit; The input end of the water supply temperature data acquisition unit is connected to the output end of the water supply temperature adjustment unit, and the output end of the water supply temperature data acquisition unit is connected to the input end of the data prediction time acquisition unit; The water supply temperature data collection unit is used to collect the water supply temperature data predicted every day when the water supply temperature was predicted by the default real-time prediction method in the past; The data prediction time acquisition unit is used to obtain the time for predicting the corresponding water supply temperature.

[0020] The user-side load analysis module includes a temperature change time analysis unit and a collection prediction time planning unit; The input end of the temperature change time analysis unit is connected to the output end of the data prediction time acquisition unit, and the output end of the temperature change time analysis unit is connected to the input end of the acquisition prediction time planning unit; The temperature change time analysis unit is used to count the time when the water supply temperature changes predicted every day in the past, obtain the default initial time T for starting the load forecast every day in the past, record the time interval between the time when the water supply temperature changes for the first time after the T moment and T as the time interval of the first change of the water supply temperature, form a historical first data set with the time interval data of the first change of the water supply temperature every day in the past, analyze the time interval data in the historical first data set, predict the time of the first change of the water supply temperature on the latest day, after predicting the time of the first change of the water supply temperature on the latest day, retrieve the water supply temperature change time data of several days whose first change time of the water supply temperature is the same as the predicted time of the first change of the water supply temperature on the latest day, obtain the time when the water supply temperature changes for the first time after the corresponding time of the first change of the water supply temperature, record the time interval between the obtained time and the corresponding time of the first change of the water supply temperature as the time interval of the second change of the water supply temperature, form a historical second data set with the time interval data of the second change of the water supply temperature corresponding to several days, analyze the historical second data set and predict the time of the second change of the water supply temperature on the latest day, and so on predict the change time of the water supply temperature on the latest day several times; The collection prediction time planning unit is used to plan the load prediction time and adjust the default real-time prediction method: perceive the environmental data and predict the load required by the user end, that is, the water supply temperature, at the predicted change time of the water supply temperature for several times in the latest day, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time.

[0021] The parameter adjustment management module includes a usage time sensing unit, an adjustment time abnormality prediction unit and a water temperature adjustment time adjustment unit; The output ends of the time sensing unit and the acquisition prediction time planning unit are connected to the input end of the adjustment time abnormality prediction unit, and the output end of the adjustment time abnormality prediction unit is connected to the input end of the water temperature adjustment time adjustment unit; The usage time sensing unit is used to sense the time period when the user uses hot water in the latest day. A water level sensor is installed in the water tank of the air source heat pump equipment. The water level is monitored by the water level sensor. When the water level drops, it is judged that the user is using hot water. When the water level stops dropping, it is judged that the user stops using hot water, thereby being able to sense the time period when the user uses hot water; The adjustment time abnormality prediction unit is used to determine whether the planned load forecast time falls within the sensed time period: if the planned forecast time is within the sensed time period, the corresponding forecast time will be marked as abnormal; otherwise, no abnormality will be marked; The water temperature regulation time adjustment unit is used to plan the water supply temperature regulation time: if the planned load forecast times are not marked, the water supply temperature of the air source heat pump equipment will be adjusted to the predicted water supply temperature immediately after the water supply temperature is predicted at the corresponding forecast time; if an abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted at the abnormally marked forecast time after it is sensed that the user stops using hot water; if more than one abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted at the latest forecast time among several abnormally marked forecast times after it is sensed that the user stops using hot water.

[0022] Embodiment 2: like Figure 2 As shown, this embodiment provides a cooling and heating combined supply load management method based on an adaptive intelligent agent, which is implemented based on the cooling and heating combined supply load management system in the embodiment, and specifically includes the following steps: S100: Control the air source heat pump equipment to realize the combined supply of cooling and heating and predict the load required by the user end, adjust and manage the parameters of the air source heat pump equipment, control the air source heat pump equipment to generate hot water or cold water required by the user, and deliver the hot water or cold water generated by the heat pump equipment to the user end through the water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump equipment through the return pipe for reheating. The environmental data is perceived by the intelligent agent adaptive learning method, and a user end load prediction model is established using a deep learning algorithm. The environmental data is input into the model to predict the load required by the user end, and the water supply temperature of the air source heat pump equipment is adjusted to the predicted water supply temperature; S200: Collecting water supply temperature data and historical prediction time data predicted by the default method in the past, collecting water supply temperature data predicted every day when the water supply temperature was predicted by the default real-time prediction method in the past, and obtaining the time when the corresponding water supply temperature was predicted; S300: Analyze the historical water supply temperature change time data, plan and adjust the load forecast time, retrieve the water supply temperature data predicted every day when the water supply temperature was predicted by the default real-time forecast method in the past, count the time when the water supply temperature predicted every day in the past changes, obtain the default initial time for starting load forecasting every day as T, record the time interval between the time when the water supply temperature changes for the first time after time T and T as the time interval of the first change of the water supply temperature, and form the first historical data set with the time interval data of the first change of the water supply temperature every day in the past, and obtain the first historical data set as {t 1 , t 2 , ..., t k}, where k represents the number of data items in the first historical data set, t kIndicates the time interval of the first change of water supply temperature on the kth day in the past, and establishes the first time interval prediction model: R 1 =λ*t k +(1-λ)*ζ k ; The time interval between the first change time of the water supply temperature on the latest day and T is predicted to be R 1 The first change time of the water supply temperature on the latest day is A 1 : A 1 =T+R 1 , where λ represents the smoothing coefficient, 0<λ<1, ζ k represents the exponential smoothing value of the time interval of the first change of the water supply temperature on the kth day in the past. ζ is solved by the following method k :According to 1 =λ*t 1 +(1-λ)*[(t 1 +t 2 +t 3 ) / 3] to calculate the exponential smoothing value of the first change time interval of the water supply temperature on the first day in the past 1 , according to 2 =λ*t 1 +(1-λ)*ζ 1 Calculate the exponential smoothing value ζ of the time interval of the first change of the water supply temperature on the second day in the past 2 , according to 3 =λ*t 2 +(1-λ)*ζ 2 Calculate the exponential smoothing value ζ of the time interval of the first change of the water supply temperature on the second day in the past 3 , and so on, we can gradually solve ζ k , in the prediction, R 1 Afterwards, the first change time of the water supply temperature for a total of m days and T+R 1 Similarly, retrieve the water supply temperature change data corresponding to m days and obtain the temperature change at T+R 1 The time when the water supply temperature changes for the first time, and the obtained time and T+R 1 The time interval between the two changes in water supply temperature is recorded as the time interval between the two changes in water supply temperature for m days. The data of the time interval between the two changes in water supply temperature for m days are combined into the historical second data set. The historical second data set is {U 1 , U 2 , ..., U m}, establish the second time interval prediction model: R 2 =λ*U m +(1-λ)*η m ; Predict the second change time of water supply temperature and T+R for the latest day 1 The time interval is R 2 , the second change time of water supply temperature on the latest day is A 2 : A 2 =T+R 1 +R 2 , η m represents the exponential smoothing value of the time interval of the second change of water supply temperature corresponding to the mth day, η m The solution method and ζ k Similarly, the predicted water supply temperature change time data set for the latest day is {A 1 , A 2 , ..., A n}, A n Indicates the time of the nth change in the water supply temperature of the latest day predicted, according to the data set {A 1 , A 2 , ..., A n} and predict the load required by the user end, that is, the water supply temperature, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time; S400: Carry out load forecasting according to the planned time and perform parameter adjustment management of the air source heat pump equipment, sense the time period when the user uses hot water in the latest day, and determine whether the planned load forecast time falls within the sensed time period: if the planned forecast time is within the sensed time period, the corresponding forecast time will be marked as abnormal; otherwise, no abnormal marking will be performed, and the water supply temperature adjustment time will be planned: if none of the planned load forecast times are marked, the water supply temperature of the air source heat pump equipment will be adjusted to the predicted water supply temperature immediately after the water supply temperature is predicted at the corresponding forecast time; if an abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted by the abnormally marked forecast time after it is sensed that the user stops using hot water; if more than one abnormally marked forecast time appears within the sensed time period, the water supply temperature will be adjusted to the water supply temperature predicted by the latest forecast time among several abnormally marked forecast times after it is sensed that the user stops using hot water; For example: if the user uses hot water in any time period of the latest day as [H1, H2], H1 represents the start time of the corresponding time period, and H2 represents the end time of the corresponding time period, the judgment data set {A 1 , A 2 , ..., A n} Whether the load forecast time in falls within the time period [H1, H2], and the time A is counted 1 and A 2falls within the time period [H1,H2], that is, time H1 is between time A 1 Before, and time A 1 At time A 2 Before, and time A 2 Before time H2, set time A 1 and A 2 Mark the abnormality and adjust the water supply temperature of the heat pump equipment to the temperature at time A at time H2. 2 Predicted supply water temperature.

[0023] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A cooling and heating combined power supply load management system based on an adaptive intelligent agent, characterized in that: It includes a cooling and heating combined supply management module, a forecasting data acquisition module, a user-side load analysis module, and a parameter adjustment management module; The combined cooling and heating management module is used to control the air source heat pump equipment to realize the combined cooling and heating function and to predict the load required by the user end, and to adjust and manage the parameters of the air source heat pump equipment; The prediction data collection module is used to collect water supply temperature data and historical prediction time data predicted by default in the past; The user-side load analysis module is used to analyze historical water supply temperature change time data, plan and adjust load forecast time; The parameter adjustment management module is used to perform load forecasting according to the planned time and to perform parameter adjustment management of the air source heat pump equipment.

2. The cooling and heating combined supply load management system based on an adaptive intelligent agent according to claim 1, characterized in that: The combined cooling and heating management module includes an air source heat pump control unit, a user-side load prediction unit, and a water supply temperature adjustment unit; The air source heat pump control unit is used to control the air source heat pump device to generate hot water or cold water required by the user, and deliver the hot water or cold water generated by the heat pump device to the user end through the water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump device through the return pipe for reheating; The user-side load prediction unit is used to perceive environmental data by means of intelligent agent adaptive learning, establish a user-side load prediction model using a deep learning algorithm, and input environmental data into the model to predict the user-side required load, where the user-side required load refers to the water supply temperature; The water supply temperature adjustment unit is used to adjust the water supply temperature of the air source heat pump device to the predicted water supply temperature.

3. The cooling and heating combined supply load management system based on an adaptive intelligent agent according to claim 2, characterized in that: The prediction data acquisition module includes a water supply temperature data acquisition unit and a data prediction time acquisition unit; The water supply temperature data collection unit is used to collect the water supply temperature data predicted every day when the water supply temperature is predicted by the real-time prediction method by default in the past; The data prediction time acquisition unit is used to acquire the time when the corresponding water supply temperature is predicted.

4. The cooling and heating combined supply load management system based on an adaptive intelligent agent according to claim 3, characterized in that: The user-side load analysis module includes a temperature change time analysis unit and a collection prediction time planning unit; The temperature change time analysis unit is used to count the time when the water supply temperature changes predicted every day in the past, obtain the default initial time T for starting the load forecast every day in the past, record the time interval between the time when the water supply temperature changes for the first time after the T moment and T as the time interval of the first change of the water supply temperature, form a historical first data set with the time interval data of the first change of the water supply temperature every day in the past, analyze the time interval data in the historical first data set, predict the time of the first change of the water supply temperature on the latest day, after predicting the time of the first change of the water supply temperature on the latest day, retrieve the water supply temperature change time data of several days whose first change time of the water supply temperature is the same as the predicted time of the first change of the water supply temperature on the latest day, obtain the time when the water supply temperature changes for the first time after the corresponding time of the first change of the water supply temperature, record the time interval between the obtained time and the corresponding time of the first change of the water supply temperature as the time interval of the second change of the water supply temperature, form a historical second data set with the time interval data of the second change of the water supply temperature corresponding to several days, analyze the historical second data set and predict the time of the second change of the water supply temperature on the latest day, and so on to predict the change time of the water supply temperature on the latest day for several times; The collection prediction time planning unit is used to plan the load prediction time and adjust the default real-time prediction method: perceive the environmental data and predict the load required by the user end, that is, the water supply temperature, at the predicted change time of the water supply temperature for several times in the latest day, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time.

5. The cooling and heating combined supply load management system based on an adaptive intelligent agent according to claim 4, characterized in that: The parameter adjustment management module includes a usage time sensing unit, an adjustment time abnormality prediction unit and a water temperature adjustment time adjustment unit; The usage time sensing unit is used to sense the time period during which the user used hot water in the latest day; The adjustment time abnormality prediction unit is used to determine whether the planned load forecast time falls within the sensed time period: if the planned forecast time is within the sensed time period, the corresponding forecast time is marked as abnormal; Otherwise, no exception marking is performed; The water temperature adjustment time adjustment unit is used to plan the water supply temperature adjustment time: if the planned load forecast time is not marked, the water supply temperature of the air source heat pump device is immediately adjusted to the predicted water supply temperature after the water supply temperature is predicted at the corresponding forecast time; if an abnormally marked forecast time appears within the sensed time period, the water supply temperature is adjusted to the water supply temperature predicted at the abnormally marked forecast time after it is sensed that the user stops using hot water; If more than one predicted time marked as abnormal appears within the sensed time period, the water supply temperature is adjusted to the water supply temperature predicted by the latest predicted time among the predicted times marked as abnormal after sensing that the user stops using hot water.

6. A method for managing combined cooling and heating load based on an adaptive intelligent agent, characterized in that: The following steps are involved: S100: Control the air source heat pump equipment to realize the combined cooling and heating function and predict the load required by the user end, and adjust and manage the parameters of the air source heat pump equipment; S200: Collecting water supply temperature data and historical prediction time data predicted by default in the past; S300: Analyze historical water supply temperature change time data, plan and adjust load forecast time; S400: Carry out load forecasting according to the planned time and perform parameter adjustment management of air source heat pump equipment.

7. The method for managing combined cooling and heating load based on an adaptive intelligent agent according to claim 6, characterized in that: In step S100: the air source heat pump device is controlled to generate hot water or cold water required by the user, and the hot water or cold water generated by the heat pump device is transported to the user end through the water supply pipe. After the user uses the hot water, the cooled water returns to the heat pump device through the return pipe for reheating. The environmental data is perceived through the intelligent agent adaptive learning method, and a user-end load prediction model is established using a deep learning algorithm. The environmental data is input into the model to predict the load required by the user end, and the water supply temperature of the air source heat pump device is adjusted to the predicted water supply temperature.

8. The method for managing combined cooling and heating load based on an adaptive intelligent agent according to claim 7, characterized in that: In step S200: the water supply temperature data predicted every day when the water supply temperature is predicted by the real-time prediction method in the past is collected, and the time when the corresponding water supply temperature is predicted is obtained.

9. The method for managing combined cooling and heating load based on an adaptive intelligent agent according to claim 8, characterized in that: In step S300: retrieve the water supply temperature data predicted every day when the water supply temperature was predicted by the default real-time prediction method in the past, count the time when the water supply temperature predicted every day in the past changes, obtain the default initial time T for starting load forecasting every day in the past, record the time interval between the time when the water supply temperature changes for the first time after time T and T as the time interval of the initial change of the water supply temperature, and form the first historical data set with the time interval data of the initial change of the water supply temperature every day in the past, and obtain the first historical data set as {t1, t2, ..., t k }, where k represents the number of data items in the first historical data set, t k Indicates the time interval of the first change of water supply temperature on the kth day in the past, and establishes the first time interval prediction model: R1=λ*t k +(1-λ)*γ k ; The predicted time interval between the first change time of the water supply temperature on the latest day and T is R1, and the first change time of the water supply temperature on the latest day is A1: A1=T+R1, where λ represents the smoothing coefficient, 0<λ<1, ζ k represents the exponential smoothing value of the time interval of the first change of the water supply temperature on the kth day in the past. ζ is solved by the following method k :According to ζ1=λ*t1+(1-λ)*[(t1+t2+t3) / 3], we can calculate the exponential smoothing value ζ1 of the time interval of the first change of the water supply temperature on the first day in the past. According to ζ2=λ*t1+(1-λ)*ζ1, we can calculate the exponential smoothing value ζ2 of the time interval of the first change of the water supply temperature on the second day in the past. According to ζ3=λ*t2+(1-λ)*ζ2, we can calculate the exponential smoothing value ζ3 of the time interval of the first change of the water supply temperature on the second day in the past. And so on, we can gradually solve the problem and get ζ k After R1 is predicted, the water supply temperature first change time of m days is retrieved, which is the same as T+R1. The water supply temperature change data corresponding to m days are retrieved, and the time when the water supply temperature first changes after T+R1 is obtained. The time interval between the obtained time and T+R1 is recorded as the water supply temperature second change time interval. The water supply temperature second change time interval data corresponding to m days are combined into the historical second data set, and the historical second data set is {U1, U2, ..., U m }, establish the second time interval prediction model: R2=λ*U m +(1-l)*n m ; The predicted time interval between the second change time of the water supply temperature on the latest day and T+R1 is R2, and the second change time of the water supply temperature on the latest day is A2: A2=T+R1+R2, η m represents the exponential smoothing value of the time interval of the second change of water supply temperature corresponding to the mth day, η m The solution method and ζ k Similarly, the predicted water supply temperature change time data set for the latest day is {A1, A2, ..., A n }, A n Indicates the predicted time of the nth change of the water supply temperature on the latest day, according to the data set {A1, A2, ..., A n } and predict the required load at the user end, that is, the water supply temperature, and adjust the water supply temperature of the air source heat pump equipment to the water supply temperature predicted at the corresponding time.

10. The method for managing combined cooling and heating load based on an adaptive intelligent agent according to claim 9, characterized in that: In step S400: it is sensed that the user uses hot water in any time period of the latest day as [H1, H2], H1 represents the start time of the corresponding time period, and H2 represents the end time of the corresponding time period, and the judgment data set {A1, A2, ..., A n } falls within the time period [H1, H2]. If time A1 and A2 fall within the time period [H1, H2], time A1 and A2 are marked as abnormal. At time H2, the water supply temperature of the heat pump equipment is adjusted to the water supply temperature predicted at time A2.

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