Ground source heat pump system end load prediction and adaptive correction method and system

Abstract

The invention relates to the technical field of energy management and intelligent control, and provides a ground source heat pump system end load prediction and adaptive correction method and system.The method comprises the steps that a day-ahead load prediction model is built, and a day-ahead load prediction value is obtained through calculation; constructing a day-ahead load prediction deviation correction model to obtain a day-ahead load prediction deviation correction value; obtaining a day-ahead load prediction value vector based on the day-ahead load prediction deviation correction value; constructing an intra-day load prediction model, and calculating to obtain an intra-day load prediction value; and respectively calculating the credibility of the intra-day load predicted value, the predicted value at the corresponding moment in the day-ahead load predicted value vector and the historical actually measured load data, and calculating to obtain a predicted load issuing value based on the intra-day load predicted value, the predicted value at the corresponding moment in the day-ahead load predicted value vector and the credibility. According to the invention, by constructing the double-layer load prediction model and combining the meteorological parameter correction model, the adaptability of the system to the dynamic change of the environment is improved, and the load prediction precision is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and intelligent control, and in particular to a method and system for predicting and adaptively correcting terminal loads of a ground source heat pump system. Background Art

[0002] With the global energy crisis and the increasing awareness of environmental protection, improving energy efficiency has become an important goal of social development. As key energy facilities in heating, air conditioning and industrial processes, the operating status of the cold and heat source system directly affects the energy efficiency and operating cost of the entire energy management system. However, due to the dynamic changes of external factors such as ambient temperature and humidity and the random fluctuations of load demand, traditional fixed scheduling strategies are often difficult to adapt to the actual operating environment, resulting in energy waste and unstable equipment operation.

[0003] Load forecasting is the core link of the optimal dispatch of cold and hot source systems. Its goal is to predict the system's load demand in advance based on historical operating data and future meteorological parameters. Mainstream methods include those based on statistical models (such as time series analysis and regression modeling) and intelligent algorithms (such as neural networks and support vector machines). However, a single model often shows limitations when dealing with complex nonlinear characteristics or data scarcity. Summary of the invention

[0004] The purpose of the present invention is to solve at least one technical problem in the background technology and to provide a method and system for predicting and adaptively correcting the terminal load of a ground source heat pump system.

[0005] To achieve the above object, the present invention provides a method for predicting and adaptively correcting terminal loads of a ground source heat pump system, comprising: Obtain historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data; Building a day-ahead load forecasting model based on historical operating load data and historical measured load data, and calculating a day-ahead load forecasting value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model is constructed based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and a day-ahead load forecast deviation correction value is calculated based on the day-ahead load forecast deviation correction model; Obtaining a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; Building a daily load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and calculating a daily load forecast value based on the daily load forecasting model; The credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

[0006] According to one aspect of the present invention, the step of building a day-ahead load forecasting model based on historical operating load data and historical measured load data, and calculating a day-ahead load forecasting value based on the day-ahead load forecasting model includes: Using historical operating load data from at least the past two weeks, the Holt-Winters additive model is constructed as a day-ahead load forecasting model to perform basic day-ahead load forecasting: ; Where: For the The basic value of load forecast at the moment of day; For the Load value at the moment; For the Moment smoothing value; For the Moment trend value; For the Time period value; is the cycle length, which is 24 in the day-ahead load forecasting model; is the smoothing coefficient, ; is the trend coefficient, ; is the seasonal coefficient, ; For the future The day-ahead load forecast value is obtained by time prediction: .

[0007] According to one aspect of the present invention, the method of constructing a day-ahead load forecast deviation correction model based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and calculating a day-ahead load forecast deviation correction value based on the day-ahead load forecast deviation correction model includes: Calculate the residual using historical measured load data and historical predicted load data: ; Where: For the day before The residual of load forecast at the moment; For the Load value at the moment; For the Load forecast value before the time; A multivariate linear regression approach was used to predict outdoor temperatures using historical outdoor forecast weather data from at least the past two weeks. and predict outdoor relative humidity , Load forecast residual Construct a day-ahead load forecast deviation correction model: ; After training, the parameters of the day-ahead load forecast deviation correction model are obtained. ; For the future Predicted outdoor temperature at the time and predict outdoor relative humidity , substitute the trained day-ahead load forecast deviation correction model: ; The correction value of the day-ahead load forecast deviation is: .

[0008] According to one aspect of the present invention, the day-ahead load forecast value vector obtained based on the day-ahead load forecast deviation correction value is: Based on the day-ahead load forecast deviation correction value , among which, take If it is 0-23, the load forecast value vector for the next 24 hours can be obtained. : .

[0009] According to one aspect of the present invention, the intraday load forecasting model is constructed based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and the intraday load forecast value is calculated based on the intraday load forecasting model, including: The least squares method is used to construct a daily load forecasting model using historical measured load data, historical forecast load data, and historical outdoor forecast meteorological data for at least the past two weeks: ; Where: For the Load forecast value within the day at the time; Consider hours for historical loads; is the historical load factor, ; For the past Load value at the moment; Consider hours for historical residuals; is the historical residual coefficient, ; For the past Moment residual; is the meteorological parameter coefficient vector; C is the constant term coefficient.

[0010] According to one aspect of the present invention, the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility, including: Extracting daily load forecast values And the forecast value of the day-ahead load forecast value vector corresponding to the time , calculate the daily load forecast value , the predicted value of the day-ahead load forecast vector at the corresponding time Compared with the historical measured load value Credibility , : ; ; At the next moment, the forecast load value sent The calculation formula is as follows: .

[0011] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a ground source heat pump system terminal load prediction and adaptive correction system, comprising: Data acquisition module, which acquires historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data; A day-ahead load forecasting model building module is used to build a day-ahead load forecasting model based on historical operating load data and historical measured load data, and to calculate a day-ahead load forecast value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model construction module is used to construct a day-ahead load forecast deviation correction model based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and to calculate a day-ahead load forecast deviation correction value based on the day-ahead load forecast deviation correction model; A day-ahead load forecast value vector acquisition module, which obtains a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; An intraday load forecasting model building module is used to build an intraday load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and to calculate and obtain an intraday load forecast value based on the intraday load forecasting model; The predicted load delivery value acquisition module calculates the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data, and obtains the predicted load delivery value based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

[0012] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the terminal load prediction and adaptive correction method of the ground source heat pump system as described above.

[0013] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the terminal load prediction and adaptive correction method of the ground source heat pump system as described above is implemented.

[0014] According to the solution of the present invention, the present invention introduces meteorological variables such as ambient temperature and humidity to construct a regression model to dynamically correct the prediction results, which helps to improve the prediction accuracy. At the same time, the real-time update of model parameters and the rolling training strategy can ensure that the model adapts to the dynamic changes of the operating environment and improve the robustness of the system.

[0015] In order to achieve closed-loop operation, it is necessary to combine prediction credibility assessment with feedback control mechanism, evaluate model performance in real time, and adjust prediction strategy. This solution can not only improve load prediction accuracy and reduce energy waste, but also significantly reduce operating costs and equipment losses, and promote the development of green energy.

[0016] According to the solution of the present invention, the present invention effectively improves the load prediction accuracy and scheduling decision-making level of the cold and heat source system by constructing a double-layer load prediction model. Its day-ahead prediction model captures long-term trends and seasonal changes, and the intraday prediction model responds to short-term fluctuations. Combined with the meteorological parameter correction model, it improves the system's adaptability to dynamic environmental changes. Through rolling updates and feedback control mechanisms, the system can automatically adjust model parameters to ensure the stability and continuous optimization of prediction results.

[0017] In actual operation, the present invention can significantly reduce energy waste and equipment loss, reduce operating costs, and improve the reliability and economic benefits of system operation. In addition, through dynamic optimization of energy distribution, the system effectively reduces carbon emissions and environmental pollution, and has important social and environmental value. At the same time, the invention has good versatility for a variety of energy systems and can be widely used in scenarios such as regional energy management, intelligent building control, and industrial process optimization, providing technical support for the comprehensive upgrade of intelligent energy management systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart schematically showing a method for predicting and adaptively correcting a terminal load of a ground source heat pump system according to an embodiment of the present invention; Figure 2 The operating principle diagram of the terminal load prediction and adaptive correction method of the ground source heat pump system according to an embodiment of the present invention is schematically shown. DETAILED DESCRIPTION

[0019] The present invention will now be discussed with reference to exemplary embodiments. It should be understood that the embodiments discussed are only to enable those skilled in the art to better understand and thus implement the present invention, rather than implying any limitation on the scope of the present invention.

[0020] As used herein, the term “including” and variations thereof are to be interpreted as open-ended terms meaning “including, but not limited to.” The term “based on” is to be interpreted as “based, at least in part, on.” The terms “one embodiment” and “an embodiment” are to be interpreted as “at least one embodiment.”

[0021] Figure 1 The flowchart schematically shows a method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to an embodiment of the present invention. Figure 1 As shown, in this embodiment, the terminal load prediction and adaptive correction method of the ground source heat pump system includes: Obtain historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data (outdoor temperature, outdoor relative humidity); Building a day-ahead load forecasting model based on historical operating load data and historical measured load data, and calculating a day-ahead load forecasting value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model is constructed based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and a day-ahead load forecast deviation correction value is calculated based on the day-ahead load forecast deviation correction model; Obtaining a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; Building a daily load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and calculating a daily load forecast value based on the daily load forecasting model; The credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

[0022] In this embodiment, the above-mentioned acquired data will be updated as the system and the prediction model are running.

[0023] Further, according to an embodiment of the present invention, a day-ahead load forecasting model is constructed based on historical operating load data and historical measured load data, and a day-ahead load forecasting value is calculated based on the day-ahead load forecasting model, including: Using historical operating load data from at least the past two weeks, the Holt-Winters additive model is constructed as a day-ahead load forecasting model to perform basic day-ahead load forecasting: ; Where: For the The basic value of load forecast at the moment of day; For the Load value at the moment; For the Moment smoothing value; For the Moment trend value; For the Time period value; is the cycle length, which is 24 in the day-ahead load forecasting model; is the smoothing coefficient, ; is the trend coefficient, ; is the seasonal coefficient, ; For the future The day-ahead load forecast value is obtained by time prediction: .

[0024] Further, according to an embodiment of the present invention, a day-ahead load forecast deviation correction model is constructed based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and a day-ahead load forecast deviation correction value is calculated based on the day-ahead load forecast deviation correction model, including: Calculate the residual using historical measured load data and historical predicted load data: ; Where: For the day before The residual of load forecast at the moment; For the Load value at the moment; For the Load forecast value before the time; A multivariate linear regression approach was used to predict outdoor temperatures using historical outdoor forecast weather data from at least the past two weeks. and predict outdoor relative humidity , Load forecast residual Construct a day-ahead load forecast deviation correction model: ; After training, the parameters of the day-ahead load forecast deviation correction model are obtained. ; For the future Predicted outdoor temperature at the time and predict outdoor relative humidity , substitute the trained day-ahead load forecast deviation correction model: ; The correction value of the day-ahead load forecast deviation is: .

[0025] Further, according to an embodiment of the present invention, the day-ahead load forecast value vector is obtained based on the day-ahead load forecast deviation correction value: Based on the day-ahead load forecast deviation correction value , among which, take If it is 0-23, the load forecast value vector for the next 24 hours can be obtained. : .

[0026] Further, according to an embodiment of the present invention, a daily load forecasting model is constructed based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and a daily load forecast value is calculated based on the daily load forecasting model, including: The least squares method is used to construct a daily load forecasting model using historical measured load data, historical forecast load data, and historical outdoor forecast meteorological data for at least the past two weeks: ; Where: For the Load forecast value within the day at the time; Consider hours for historical loads; is the historical load factor, ; For the past Load value at the moment; Consider hours for historical residuals; is the historical residual coefficient, ; For the past Moment residual; is the meteorological parameter coefficient vector; C is the constant term coefficient.

[0027] Further, according to an embodiment of the present invention, the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility, including: Extracting daily load forecast values And the forecast value of the day-ahead load forecast value vector corresponding to the time , calculate the daily load forecast value , the predicted value of the day-ahead load forecast vector at the corresponding time Compared with the historical measured load value Credibility , : ; ; At the next moment, the forecast load value sent The calculation formula is as follows: .

[0028] In this embodiment, the load forecast value vector of the day-ahead is the load forecast value vector of the next day, corresponding to the forecast loads at 24 moments. For example, when predicting the load at 10 o'clock, it is necessary to extract the load forecast values ​​corresponding to 10 o'clock in the load forecast vector of the day-ahead and calculate the credibility together with the load forecast value within the day.

[0029] According to the above scheme of the present invention, based on the day-ahead load prediction model and the intraday load prediction model, combined with the measured load data, the terminal load prediction and adaptive correction method of the ground source heat pump system of the present invention is constructed, such as Figure 2 As shown, the specific operation steps of this method are as follows: Step 1. Update the model coefficients of the day-ahead load forecast model and the day-ahead load forecast deviation correction model, and calculate the day-ahead load forecast value vector within one day .

[0030] Step 2. Update the model coefficients of the intraday load forecasting model and calculate the intraday load forecast value at the forecast time .

[0031] Step 3. Calculate the credibility of the predicted value at a historical moment , , calculate the forecast load value .

[0032] Step 4. Send the forecast load value , after the actual operation of the guiding system, the actual operation load is obtained , update the historical measured load data set; update the historical predicted load data set based on the day-ahead and intraday load forecast values; update the historical predicted meteorological data set based on the historical outdoor predicted meteorological data.

[0033] Step 5. If the current day's operation (forecast) has not ended, return to step 2 to perform intraday load forecasting; if the operation is completed, determine whether it is necessary to continue running for the next day. If so, obtain the forecast meteorological data for the next day, and then return to step 1 for a loop; otherwise, end the operation.

[0034] According to the above scheme of the present invention, the present invention constructs a regression model to dynamically correct the prediction results by introducing meteorological variables such as ambient temperature and humidity, which helps to improve the prediction accuracy. At the same time, the real-time update of model parameters and the rolling training strategy can ensure that the model adapts to the dynamic changes of the operating environment and improve the robustness of the system.

[0035] In order to achieve closed-loop operation, it is necessary to combine prediction credibility assessment with feedback control mechanism, evaluate model performance in real time, and adjust prediction strategy. This solution can not only improve load prediction accuracy and reduce energy waste, but also significantly reduce operating costs and equipment losses, and promote the development of green energy.

[0036] According to the above scheme of the present invention, the present invention effectively improves the load prediction accuracy and scheduling decision-making level of the cold and heat source system by constructing a double-layer load prediction model. Its day-ahead prediction model captures long-term trends and seasonal changes, and the intraday prediction model responds to short-term fluctuations. Combined with the meteorological parameter correction model, it improves the system's adaptability to dynamic environmental changes. Through rolling updates and feedback control mechanisms, the system can automatically adjust model parameters to ensure the stability and continuous optimization of prediction results.

[0037] In actual operation, the present invention can significantly reduce energy waste and equipment loss, reduce operating costs, and improve the reliability and economic benefits of system operation. In addition, through dynamic optimization of energy distribution, the system effectively reduces carbon emissions and environmental pollution, and has important social and environmental value. At the same time, the invention has good versatility for a variety of energy systems and can be widely used in scenarios such as regional energy management, intelligent building control, and industrial process optimization, providing technical support for the comprehensive upgrade of intelligent energy management systems.

[0038] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a ground source heat pump system terminal load prediction and adaptive correction system, comprising: Data acquisition module, which acquires historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data; A day-ahead load forecasting model building module is used to build a day-ahead load forecasting model based on historical operating load data and historical measured load data, and to calculate a day-ahead load forecast value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model construction module is used to construct a day-ahead load forecast deviation correction model based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and to calculate a day-ahead load forecast deviation correction value based on the day-ahead load forecast deviation correction model; A day-ahead load forecast value vector acquisition module, which obtains a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; An intraday load forecasting model building module is used to build an intraday load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and to calculate and obtain an intraday load forecast value based on the intraday load forecasting model; The predicted load delivery value acquisition module calculates the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data, and obtains the predicted load delivery value based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

[0039] In this embodiment, the above-mentioned acquired data will be updated as the system and the prediction model are running.

[0040] Further, according to an embodiment of the present invention, a day-ahead load forecasting model is constructed based on historical operating load data and historical measured load data, and a day-ahead load forecasting value is calculated based on the day-ahead load forecasting model, including: Using historical operating load data from at least the past two weeks, the Holt-Winters additive model is constructed as a day-ahead load forecasting model to perform basic day-ahead load forecasting: ; Where: For the The basic value of load forecast at the moment of day; For the Load value at the moment; For the Moment smoothing value; For the Moment trend value; For the Time period value; is the cycle length, which is 24 in the day-ahead load forecasting model; is the smoothing coefficient, ; is the trend coefficient, ; is the seasonal coefficient, ; For the future The day-ahead load forecast value is obtained by time prediction: .

[0041] Further, according to an embodiment of the present invention, a day-ahead load forecast deviation correction model is constructed based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and a day-ahead load forecast deviation correction value is calculated based on the day-ahead load forecast deviation correction model, including: Calculate the residual using historical measured load data and historical predicted load data: ; Where: For the day before The residual of load forecast at the moment; For the Load value at the moment; For the Load forecast value before the time; A multivariate linear regression approach was used to predict outdoor temperatures using historical outdoor forecast weather data from at least the past two weeks. and predict outdoor relative humidity , Load forecast residual Construct a day-ahead load forecast deviation correction model: ; After training, the parameters of the day-ahead load forecast deviation correction model are obtained. ; For the future Predicted outdoor temperature at the time and predict outdoor relative humidity , substitute the trained day-ahead load forecast deviation correction model: ; The correction value of the day-ahead load forecast deviation is: .

[0042] Further, according to an embodiment of the present invention, the day-ahead load forecast value vector is obtained based on the day-ahead load forecast deviation correction value: Based on the day-ahead load forecast deviation correction value , among which, take If it is 0-23, the load forecast value vector for the next 24 hours can be obtained. : .

[0043] Further, according to an embodiment of the present invention, a daily load forecasting model is constructed based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and a daily load forecast value is calculated based on the daily load forecasting model, including: The least squares method is used to construct a daily load forecasting model using historical measured load data, historical forecast load data, and historical outdoor forecast meteorological data for at least the past two weeks: ; Where: For the Load forecast value within the day at the time; Consider hours for historical loads; is the historical load factor, ; For the past Load value at the moment; Consider hours for historical residuals; is the historical residual coefficient, ; For the past Moment residual; is the meteorological parameter coefficient vector; C is the constant term coefficient.

[0044] Further, according to an embodiment of the present invention, the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility, including: Extracting daily load forecast values And the forecast value of the day-ahead load forecast value vector corresponding to the time , calculate the daily load forecast value , the predicted value of the day-ahead load forecast vector at the corresponding time Compared with the historical measured load value Credibility , : ; ; At the next moment, the forecast load value sent The calculation formula is as follows: .

[0045] In this embodiment, the load forecast value vector of the day-ahead is the load forecast value vector of the next day, corresponding to the forecast loads at 24 moments. For example, when predicting the load at 10 o'clock, it is necessary to extract the load forecast values ​​corresponding to 10 o'clock in the load forecast vector of the day-ahead and calculate the credibility together with the load forecast value within the day.

[0046] According to the above scheme of the present invention, based on the day-ahead load prediction model and the intraday load prediction model, combined with the measured load data, the prediction and correction method of the terminal load prediction and adaptive correction system of the ground source heat pump system of the present invention is constructed, such as Figure 2 As shown, the specific operation steps of the system are as follows: Step 1. Update the model coefficients of the day-ahead load forecast model and the day-ahead load forecast deviation correction model, and calculate the day-ahead load forecast value vector within one day .

[0047] Step 2. Update the model coefficients of the intraday load forecasting model and calculate the intraday load forecast value at the forecast time .

[0048] Step 3. Calculate the credibility of the predicted value at a historical moment , , calculate the forecast load value .

[0049] Step 4. Send the forecast load value , after the actual operation of the guiding system, the actual operation load is obtained , update the historical measured load data set; update the historical predicted load data set based on the day-ahead and intraday load forecast values; update the historical predicted meteorological data set based on the historical outdoor predicted meteorological data.

[0050] Step 5. If the current day's operation (forecast) has not ended, return to step 2 to perform intraday load forecasting; if the operation is completed, determine whether it is necessary to continue running for the next day. If so, obtain the forecast meteorological data for the next day, and then return to step 1 for a loop; otherwise, end the operation.

[0051] According to the above scheme of the present invention, the present invention constructs a regression model to dynamically correct the prediction results by introducing meteorological variables such as ambient temperature and humidity, which helps to improve the prediction accuracy. At the same time, the real-time update of model parameters and the rolling training strategy can ensure that the model adapts to the dynamic changes of the operating environment and improve the robustness of the system.

[0052] In order to achieve closed-loop operation, it is necessary to combine prediction credibility assessment with feedback control mechanism, evaluate model performance in real time, and adjust prediction strategy. This solution can not only improve load prediction accuracy and reduce energy waste, but also significantly reduce operating costs and equipment losses, and promote the development of green energy.

[0053] According to the above scheme of the present invention, the present invention effectively improves the load prediction accuracy and scheduling decision-making level of the cold and heat source system by constructing a double-layer load prediction model. Its day-ahead prediction model captures long-term trends and seasonal changes, and the intraday prediction model responds to short-term fluctuations. Combined with the meteorological parameter correction model, it improves the system's adaptability to dynamic environmental changes. Through rolling updates and feedback control mechanisms, the system can automatically adjust model parameters to ensure the stability and continuous optimization of prediction results.

[0054] In actual operation, the present invention can significantly reduce energy waste and equipment loss, reduce operating costs, and improve the reliability and economic benefits of system operation. In addition, through dynamic optimization of energy distribution, the system effectively reduces carbon emissions and environmental pollution, and has important social and environmental value. At the same time, the invention has good versatility for a variety of energy systems and can be widely used in scenarios such as regional energy management, intelligent building control, and industrial process optimization, providing technical support for the comprehensive upgrade of intelligent energy management systems.

[0055] Furthermore, to achieve the above-mentioned purpose, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the terminal load prediction and adaptive correction method of the ground source heat pump system as described above is implemented.

[0056] Furthermore, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the terminal load prediction and adaptive correction method of the ground source heat pump system as described above is implemented.

[0057] Those skilled in the art will appreciate that the modules and algorithm steps described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0058] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices and equipment can refer to the corresponding processes in the aforementioned method implementation methods, and will not be repeated here.

[0059] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0060] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present invention.

[0061] In addition, each functional module in the embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0062] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the energy-saving signal sending / receiving method of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical disks.

[0063] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the inventive concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other.

[0064] It should be understood that the size of the serial numbers of each step in the content of the invention and the implementation methods of the present invention does not absolutely mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation methods of the present invention.

Claims

1. A method for predicting and adaptively correcting the terminal load of a ground source heat pump system, characterized in that: include: Obtain historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data; Building a day-ahead load forecasting model based on historical operating load data and historical measured load data, and calculating a day-ahead load forecasting value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model is constructed based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and a day-ahead load forecast deviation correction value is calculated based on the day-ahead load forecast deviation correction model; Obtaining a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; Building a daily load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and calculating a daily load forecast value based on the daily load forecasting model; The credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

2. The method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to claim 1, characterized in that: The method of constructing a day-ahead load forecasting model based on historical operating load data and historical measured load data, and calculating a day-ahead load forecasting value based on the day-ahead load forecasting model includes: Using historical operating load data from at least the past two weeks, the Holt-Winters additive model is constructed as a day-ahead load forecasting model to perform basic day-ahead load forecasting: ； Where: For the The basic value of load forecast at the moment of day; For the Load value at the moment; For the Moment smoothing value; For the Moment trend value; For the Time period value; is the cycle length, which is 24 in the day-ahead load forecasting model; is the smoothing coefficient, ; is the trend coefficient, ; is the seasonal coefficient, ; For the future The day-ahead load forecast value is obtained by time prediction: .

3. The method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to claim 2, characterized in that: The method of constructing a day-ahead load forecast deviation correction model based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and calculating a day-ahead load forecast deviation correction value based on the day-ahead load forecast deviation correction model includes: Calculate the residual using historical measured load data and historical predicted load data: ； Where: For the day before The residual of load forecast at the moment; For the Load value at the moment; For the Load forecast value before the time; A multivariate linear regression approach was used to predict outdoor temperatures using historical outdoor forecast weather data from at least the past two weeks. and predicted outdoor relative humidity , Load forecast residual Construct a day-ahead load forecast deviation correction model: ； After training, the parameters of the day-ahead load forecast deviation correction model are obtained. ; For the future Predicted outdoor temperature at the time and predicted outdoor relative humidity , substitute the trained day-ahead load forecast deviation correction model: ； The correction value of the day-ahead load forecast deviation is: 。 4. The method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to claim 3, characterized in that: The day-ahead load forecast value vector obtained based on the day-ahead load forecast deviation correction value is: Based on the day-ahead load forecast deviation correction value , among which, take If it is 0-23, the load forecast value vector for the next 24 hours can be obtained. : 。 5. The method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to claim 4, characterized in that: The intraday load forecasting model is constructed based on the historical operating load data, the historical forecast load data and the historical outdoor forecast meteorological data, and the intraday load forecast value is calculated based on the intraday load forecasting model, including: The least squares method is used to construct a daily load forecasting model using historical measured load data, historical forecast load data, and historical outdoor forecast meteorological data for at least the past two weeks: ； Where: For the Load forecast value within the day at the time; Consider hours for historical loads; is the historical load factor, ; For the past Load value at the moment; Consider hours for historical residuals; is the historical residual coefficient, ; For the past Moment residual; is the meteorological parameter coefficient vector; C is the constant term coefficient.

6. The method for predicting and adaptively correcting the terminal load of a ground source heat pump system according to claim 5, characterized in that: The credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data is calculated respectively, and the forecast load delivery value is obtained based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation, including: Extracting daily load forecast values And the forecast value of the day-ahead load forecast value vector corresponding to the time , calculate the daily load forecast value respectively , the predicted value of the day-ahead load forecast vector at the corresponding time Compared with the historical measured load value Credibility , : ； ； At the next moment, the forecast load value sent The calculation formula is as follows: 。 7. The terminal load prediction and adaptive correction system of the ground source heat pump system is characterized by: include: Data acquisition module, which acquires historical operating load data, historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data; A day-ahead load forecasting model building module is used to build a day-ahead load forecasting model based on historical operating load data and historical measured load data, and to calculate a day-ahead load forecast value based on the day-ahead load forecasting model; A day-ahead load forecast deviation correction model construction module is used to construct a day-ahead load forecast deviation correction model based on historical forecast load data, historical measured load data, and historical outdoor forecast meteorological data, and to calculate a day-ahead load forecast deviation correction value based on the day-ahead load forecast deviation correction model; A day-ahead load forecast value vector acquisition module, which obtains a day-ahead load forecast value vector based on the day-ahead load forecast deviation correction value; An intraday load forecasting model building module is used to build an intraday load forecasting model based on historical operating load data, historical forecast load data and historical outdoor forecast meteorological data, and to calculate and obtain an intraday load forecast value based on the intraday load forecasting model; The predicted load delivery value acquisition module calculates the credibility of the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the historical measured load data, and obtains the predicted load delivery value based on the intraday load forecast value, the forecast value at the corresponding time in the day-ahead load forecast value vector and the credibility calculation.

8. An electronic device, characterized in that The method comprises a processor, a memory and a computer program stored in the memory and executable on the processor, wherein when the computer program is executed by the processor, the method for predicting and adaptively correcting the terminal load of a ground source heat pump system as described in any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the terminal load prediction and adaptive correction method of the ground source heat pump system according to any one of claims 1 to 6 is implemented.

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