An electrothermal synergistic method for directional transport of multi-scale electrothermal energy flows

Abstract

The present invention discloses an electric-thermal coordination method for directional transport of multi-scale electric-thermal energy flow, which belongs to the field of electric power technology, including real-time acquisition of temperature, power generation, voltage and current data of a heat storage system and a photovoltaic system, prediction of photovoltaic power generation and heat storage power generation by using regression analysis and a decision tree model, optimization and adjustment based on control decisions according to historical power consumption data, prediction of user load by using an ARIMA model, and solving the technical problem of the non-coordination between the heat storage system and the photovoltaic system in the process of energy utilization and scheduling. The present invention realizes efficient coordination between the heat storage system and the photovoltaic system, dynamically adjusts the heat storage and heat release modes according to historical and real-time data, adopts a machine learning model to improve the accuracy of power generation and load prediction, and enhances the system's responsiveness to environmental changes.

Description

Technical Field

[0001] The present invention belongs to the field of electric power technology, and in particular relates to an electrothermal synergistic method for directional transport of multi-scale electrothermal energy flows. Background Art

[0002] With the rapid development of renewable energy technology, the combination of photovoltaic power generation and thermal storage system has attracted increasing attention. This system converts solar energy into electrical energy, stores thermal energy in the thermal storage system, and supplements the insufficient power generation of the photovoltaic system by releasing heat and storing heat, thereby achieving the coordinated use of electricity and thermal energy and improving energy utilization efficiency.

[0003] In this system, the photovoltaic system is responsible for converting solar radiation into electrical energy. The system monitors voltage and current in real time to calculate the power generation. Through an integrated control system, these data are combined with the thermal storage temperature and power generation data of the thermal storage system. The thermal storage system stores thermal energy when there is excess power generation and releases heat during peak demand to heat buildings or generate electricity.

[0004] The existing technology has some defects:

[0005] Energy management is not smart enough: Many existing systems still rely on simple rules and thresholds for energy scheduling and management, and lack real-time monitoring and dynamic adjustment capabilities. This results in insufficient flexibility in system response and inability to effectively cope with photovoltaic power generation fluctuations and load changes;

[0006] Insufficient prediction accuracy: Current prediction models often rely on historical data and fail to fully consider a variety of influencing factors (such as weather changes), resulting in inaccurate prediction results and affecting subsequent decision-making. Summary of the invention

[0007] The purpose of the present invention is to provide an electrothermal coordination method for directional transport of multi-scale electrothermal energy flows, which solves the technical problem of solving the incoordination between the thermal storage system and the photovoltaic system in the process of energy utilization and scheduling.

[0008] To achieve the above object, the present invention adopts the following technical solution:

[0009] An electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow comprises the following steps:

[0010] Step 1: The data collection module obtains the water storage temperature data T of the thermal storage system in real time through the Internet storage , water outlet temperature data T out And return water temperature data T return , construct the heat storage temperature data set, and obtain the voltage V of the heat storage system power generation output thermal and current I thermal , calculate the power generation P of the thermal storage system thermal;Construct thermal storage power generation data set;

[0011] The data collection module obtains the voltage V output by the photovoltaic system in real time through the Internet. pv 、Current I pv , calculate the power generation P of the photovoltaic system pv , generate photovoltaic power data set;

[0012] Step 2: The prediction module retrieves the photovoltaic power data set and predicts the photovoltaic power generation P of the photovoltaic system in the next time period t through regression analysis. pv,forecast ;

[0013] Retrieve the thermal storage power generation data set and use the decision tree regression model to predict the thermal storage power generation P in the next time period t thermal,forecast ;

[0014] Step 3: The prediction module calculates the total predicted power generation P total,forecast ;

[0015] P total,forecast =P pv,forecast +P thermal,forecast ;

[0016] Step 4: The control decision module obtains historical heating demand data and historical electricity consumption data, draws a time series diagram of historical electricity consumption based on the historical electricity consumption data, and calculates the predicted user load P for the next time period t based on the ARIMA model. load,next ;

[0017] Retrieve the total predicted power generation P calculated by the prediction module total,forecast , make decisions, and optimize:

[0018] decision making:

[0019] If P total,forecast <P load,next , the heat storage system enters the heat release mode;

[0020] If P total,forecast ≥P load,next +s, the thermal storage system enters the thermal storage mode;

[0021] Optimizing decisions:

[0022] If T out <T demand , the thermal storage system gives priority to heating;

[0023] If T storage >T max , the thermal storage system considers entering the heat release mode;

[0024] T demandThe target temperature for heating demand of users; T max is the maximum safe temperature of water stored in the thermal storage system; s is the preset safety margin;

[0025] According to the load fluctuation frequency F load , power generation change frequency F pv , system response time T response and heating demand change F heat , calculate the duration T of the mode switching of the thermal storage system according to the following formula switch :

[0026]

[0027] Among them, k is the adjustment coefficient, which is adjusted according to the actual operation of the thermal storage system and reflects the influence of various factors on the duration;

[0028] Step 5: The control decision module sends the control decision to the thermal storage system. The control decision includes decision-making and optimization decision. The thermal storage system starts the corresponding valves and pumps according to the control decision, adjusts the coordinated exchange of thermal energy and electrical energy, and realizes dynamic regulation of energy.

[0029] Preferably, when executing step 1, the power generation capacity P of the thermal storage system thermal Calculated by the following formula:

[0030] P thermal =V thermal ×I thermal ;

[0031] The power generation of the photovoltaic system P pv Calculated by the following formula:

[0032] P pv =V pv ×I pv .

[0033] Preferably, when executing step 2, the photovoltaic power generation P pv,forecast The specific calculation formula is as follows:

[0034] P pv，forecast =a0+a1·P pv，real +a2·P load，hist +a3·Weather Factors+∈;

[0035] Among them, P pv,forecas is the predicted photovoltaic power generation; a0 is the intercept, which indicates the power generation when all independent variables are 0; a1, a2 and a3 are regression coefficients, which respectively indicate the influence of photovoltaic real-time power generation, historical power consumption and weather factors on power generation; ∈ is the error term, which indicates the prediction error of the model; Ppv,real is the real-time photovoltaic system power generation, P pv,hist is the historical photovoltaic power generation data, P pv,real and P pv,hist All are calculated using the data in the photovoltaic power data set; WeatherFactors is weather data, specifically including solar radiation intensity;

[0036] Thermal storage power generation P thermal,forecast The specific calculation formula is as follows:

[0037] For a given input feature vector X, use the trained decision tree model to make predictions:

[0038] X=[T storage ,T out ,T return ,P thermal,past ];

[0039] P thermal,forecast =Tree(X);

[0040] P thermal,past It refers to the power generation data of the thermal storage system in a certain period of time in the past, which is obtained through the thermal storage power generation data set; Tree represents the decision tree model.

[0041] Preferably, when executing step 4, the historical electricity consumption data is the historical user electricity consumption data obtained by the control decision module from the power grid system via the Internet;

[0042] The execution steps of the control decision module are as follows:

[0043] Step 4-1: The control decision module obtains historical power consumption data;

[0044] Step 4-2: The control decision module cleans the historical power consumption data, removes outliers and missing values, constructs a historical power consumption data set, and arranges the data in the historical power consumption data set in time series, specifically in hourly, daily or weekly time series;

[0045] Step 4-3: Draw a time series graph to obtain the trend, seasonality and periodicity characteristics of the data; draw the autocorrelation graph ACF and partial autocorrelation graph PACF;

[0046] Step 4-4: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q. The number of autoregressive terms p is the number of significant lags obtained from the PACF diagram, the number of moving average terms q is the number of significant lags obtained from the ACF diagram, and the number of differences d is the number of differences performed when the unit root test is used to check the stationarity of the time series.

[0047] Step 4-5: Build ARIMA model:

[0048] ARIMA(p,d,q);

[0049] Step 4-6: Fit the ARIMA model using historical electricity consumption data;

[0050] Step 4-7: Use the fitted ARIMA model to predict the next time period. The prediction formula is:

[0051]

[0052] in, is the predicted user load; μ is the constant term of the model; φ1, φ2, ..., φ p are all autoregressive coefficients; θ1, θ2, ..., θ q are all moving average coefficients; ∈ t-1 ,∈ t-1 , ...,∈ t-q are all error terms;

[0053] Step 4-8: The predicted value As the predicted user load P for the next time period load,next ;

[0054] Step 4-9: Use the mean square error (MSE) or root mean square error (RMSE) indicator to evaluate the predictive ability of the ARIMA model;

[0055] Step 4-10: Regularly adjust the ARIMA model parameters based on the difference between actual electricity consumption and predicted values.

[0056] Preferably, the load fluctuation frequency F load By analyzing the changes in historical electricity consumption data, the frequency of load changes per unit time is calculated;

[0057] Power generation change frequency F pv It is obtained by monitoring the power generation of the photovoltaic system and recording the number of changes in power generation per unit time;

[0058] System response time T response It is determined by the system's reaction time to changes in input signals and is a preset fixed value;

[0059] Change in heating demand F heat It is obtained by analyzing the changes in historical heating demand data, and represents the number of changes in heating demand within a statistical unit time.

[0060] The electric-thermal coordination method for directional transport of multi-scale electric-thermal energy flows described in the present invention solves the technical problem of solving the incoordination between the heat storage system and the photovoltaic system in the process of energy utilization and scheduling. The present invention realizes efficient coordination between the heat storage system and the photovoltaic system, can monitor and predict the demand for electric energy and thermal energy in real time, dynamically adjust the heat storage and release modes according to historical and real-time data, thereby optimizing energy utilization efficiency, and adopts machine learning models to improve the accuracy of power generation and load prediction, and enhance the system's responsiveness to environmental changes. It not only reduces energy waste, but also effectively improves the utilization rate of renewable energy, thereby facilitating intelligent energy management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is the main flow chart of the present invention;

[0062] Figure 2 It is a flow chart of step 4 of the present invention. DETAILED DESCRIPTION

[0063] Depend on Figure 1-Figure 2 The electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow shown in the figure comprises the following steps:

[0064] Step 1: The data collection module obtains the water storage temperature data T of the thermal storage system in real time through the Internet storage , water outlet temperature data T out And return water temperature data T return , construct the heat storage temperature data set, and obtain the voltage V of the heat storage system power generation output thermal and current I thermal , calculate the power generation P of the thermal storage system thermal ;Construct thermal storage power generation data set;

[0065] Power generation of thermal storage system P thermal Calculated by the following formula:

[0066] P thermal =V thermal ×I thermal ;

[0067] The data collection module obtains the voltage V output by the photovoltaic system in real time through the Internet. pv 、Current I pv , calculate the power generation P of the photovoltaic system pv , generate photovoltaic power data set;

[0068] The power generation of the photovoltaic system P pv Calculated by the following formula:

[0069] P pv =V pv ×I pv .

[0070] In this embodiment, the frequency of data acquisition is once a minute to ensure real-time performance.

[0071] Step 2: The prediction module retrieves the photovoltaic power data set and predicts the photovoltaic power generation P of the photovoltaic system in the next time period t through regression analysis. pv,forecast ;

[0072] Photovoltaic power generation P pv,forecast The specific calculation formula is as follows:

[0073] P pv，forecast =a0+a1·P pv，real +a2·P load，hist +a3·Wether Factors+∈;

[0074] Among them, P pv,forecas is the predicted photovoltaic power generation; a0 is the intercept, which indicates the power generation when all independent variables are 0; a1, a2 and a3 are regression coefficients, which respectively indicate the influence of photovoltaic real-time power generation, historical power consumption and weather factors on power generation; ∈ is the error term, which indicates the prediction error of the model; P pv,real is the real-time photovoltaic system power generation, P pv,hist is the historical photovoltaic power generation data, P pv,real and P pv,hist All are calculated using data from the photovoltaic power data set;

[0075] Weather Factors refers to weather data, including solar radiation intensity. Solar radiation intensity can usually be obtained through meteorological data services.

[0076] Retrieve the thermal storage power generation data set and use the decision tree regression model to predict the thermal storage power generation P in the next time period t thermal,forecast ;

[0077] Thermal storage power generation P thermal,forecast The specific calculation formula is as follows:

[0078] For a given input feature vector X, use the trained decision tree model to make predictions:

[0079] X=[T storage ,T out ,T return ,P thermal,past ];

[0080] P thermal,forecast =Tree(X);

[0081] P thermal,pastIt refers to the power generation data of the thermal storage system in a certain period of time in the past, which is obtained through the thermal storage power generation data set; Tree represents the decision tree model.

[0082] In the regression analysis of this embodiment, a multivariate linear regression or random forest regression model is used to improve the prediction accuracy. The effect of each model will be evaluated by the mean square error (MSE) to ensure the reliability of the prediction results.

[0083] Step 3: The prediction module calculates the total predicted power generation P total,forecast ;

[0084] P total,forecast =P pv,forecast +P thermal,forecast ;

[0085] Step 4: The control decision module obtains historical heating demand data and historical electricity consumption data, draws a time series diagram of historical electricity consumption based on the historical electricity consumption data, and calculates the predicted user load P for the next time period t based on the ARIMA model. load,next ;

[0086] The historical electricity consumption data is the historical user electricity consumption data obtained by the control decision module from the power grid system through the Internet;

[0087] The execution steps of the control decision module are as follows:

[0088] Step 4-1: The control decision module obtains historical power consumption data; collects and organizes historical power consumption data P load,t , ensure that the data is arranged in time series (for example, by hour, day, or week). The data should include multiple time records to facilitate analysis.

[0089] Step 4-2: The control decision module cleans the historical power consumption data, removes outliers and missing values, constructs a historical power consumption data set, and arranges the data in the historical power consumption data set in time series, specifically in hourly, daily or weekly time series;

[0090] In this embodiment, during the data cleaning process, the Z-score or IQR method can be used to detect outliers to ensure data quality.

[0091] Step 4-3: Draw a time series graph to obtain the trend, seasonality and periodicity characteristics of the data; draw the autocorrelation graph ACF and partial autocorrelation graph PACF;

[0092] Draw a time series graph of historical electricity consumption to observe trends and seasonal changes in the data. Determine whether there is a clear upward or downward trend and whether there are cyclical fluctuations.

[0093] The autocorrelation plot ACF and partial autocorrelation plot PACF are used to help determine the autocorrelation of the data and select the parameters of the ARIMA model.

[0094] Step 4-4: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q. The number of autoregressive terms p is the number of significant lags obtained from the PACF diagram, the number of moving average terms q is the number of significant lags obtained from the ACF diagram, and the number of differences d is the number of differences performed when the unit root test is used to check the stationarity of the time series.

[0095] Use a unit root test (such as the Augmented Dickey-Fuller test) to check the stationarity of the time series. The mean and variance of a stationary series do not change over time. If the series is not stationary, then it needs to be differentiated:

[0096] P load,diff =P load,t -P load,t -1;

[0097] Perform the difference until the series is stationary, and record the number of differences d.

[0098] Step 4-5: Build ARIMA model:

[0099] ARIMA(p,d,q);

[0100] Step 4-6: Fit the ARIMA model using historical electricity consumption data; the fitting process includes estimating the model parameters using the maximum likelihood estimation method (MLE).

[0101] Step 4-7: Use the fitted ARIMA model to predict the next time period. The prediction formula is:

[0102]

[0103] in, is the predicted user load; μ is the constant term of the model; φ is the autoregressive coefficient; θ is the moving average coefficient; ∈ is the error term;

[0104] Step 4-8: The predicted value As the predicted user load P for the next time period load,next ;

[0105] Step 4-9: Use the mean square error (MSE) or root mean square error (RMSE) indicator to evaluate the predictive ability of the ARIMA model;

[0106] Step 4-10: Regularly adjust the ARIMA model parameters based on the difference between actual electricity consumption and predicted values.

[0107] Retrieve the total predicted power generation P calculated by the prediction module total,forecast , make decisions, and optimize:

[0108] decision making:

[0109] If P total,forecast <P load,next , the heat storage system enters the heat release mode;

[0110] If P total,forecast ≥P load,next +s, the thermal storage system enters the thermal storage mode;

[0111] Optimizing decisions:

[0112] If T out <T demand , the thermal storage system gives priority to heating;

[0113] If T storage >T max , the thermal storage system considers entering the heat release mode;

[0114] T demand The target temperature for heating demand of users; T max is the maximum safe temperature of water stored in the thermal storage system; s is the preset safety margin;

[0115] In this embodiment, s is taken as P load,next The percentage value can be determined by considering the following factors:

[0116] Load volatility: If the user's load varies greatly, it is recommended to set a larger safety margin to ensure that the demand can be met during peak hours. Generally, the value of s can be considered to be between 5% and 15%, depending on the fluctuation range of historical load data.

[0117] Thermal storage system response time: If the thermal storage system has a long response time, it is recommended to set a higher safety margin to ensure that the system can adjust in time in the event of a sudden increase in load. It can be considered to be between 10% and 20%.

[0118] Thermal storage system efficiency: If the thermal storage system has a higher efficiency, the safety margin can be reduced appropriately because the system can adapt to load changes more quickly. In this case, s can be set to 5% to 10%.

[0119] Taking all factors into consideration when setting the value of s, a reasonable initial value can generally be between 5% and 15%.

[0120] According to the load fluctuation frequency F load , power generation change frequency F pv , system response time Tresponse and heating demand change F heat , calculate the duration T of the mode switching of the thermal storage system according to the following formula switch :

[0121]

[0122] Among them, k is the adjustment coefficient, which is adjusted according to the actual operation of the thermal storage system and reflects the influence of various factors on the duration;

[0123] Load fluctuation frequency F load By analyzing the changes in historical electricity consumption data, the frequency of load changes per unit time is calculated;

[0124] Power generation change frequency F pv It is obtained by monitoring the power generation of the photovoltaic system and recording the number of changes in power generation per unit time;

[0125] System response time T response It is determined by the system's reaction time to changes in input signals and is a preset fixed value;

[0126] Change in heating demand F heat It is obtained by analyzing the changes in historical heating demand data, and represents the number of changes in heating demand within a statistical unit time.

[0127] Step 5: The control decision module sends the control decision to the thermal storage system. The thermal storage system starts the corresponding valves and pumps according to the control decision, adjusts the coordinated exchange of thermal energy and electrical energy, and realizes dynamic regulation of energy.

[0128] The control system of valves and pumps can use PID control algorithm to achieve precise adjustment. At the same time, the control system will monitor the working status of valves and pumps in real time to ensure the safety and efficiency of the system.

[0129] In this embodiment, the valve is used to regulate the flow of fluid, control the flow direction of hot water in the thermal storage system, and ensure the effective storage and release of thermal energy. When the system enters the heat release mode, the valve will open to allow hot water to flow out of the thermal storage tank to meet the heating demand; conversely, in the heat storage mode, the valve is closed to prevent hot water from flowing out, thereby achieving thermal energy storage.

[0130] Pumps are responsible for circulating water in the thermal storage system to ensure that heat is efficiently transferred between the thermal storage medium and the user. The pumps are generally powered by electricity generated by the photovoltaic system, ensuring that the system operates efficiently when sunlight conditions are good. The operation of the pumps is essential to maintaining the dynamic balance of the system and helps achieve a fast response.

[0131] The data collection module, prediction module, and control decision module are all deployed on the cloud server platform.

[0132] The electric-thermal coordination method for directional transport of multi-scale electric-thermal energy flows described in the present invention solves the technical problem of solving the incoordination between the heat storage system and the photovoltaic system in the process of energy utilization and scheduling. The present invention realizes efficient coordination between the heat storage system and the photovoltaic system, can monitor and predict the demand for electric energy and thermal energy in real time, dynamically adjust the heat storage and release modes according to historical and real-time data, thereby optimizing energy utilization efficiency, and adopts machine learning models to improve the accuracy of power generation and load prediction, and enhance the system's responsiveness to environmental changes. It not only reduces energy waste, but also effectively improves the utilization rate of renewable energy, thereby facilitating intelligent energy management.

Claims

1. An electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow, characterized in that: The steps include: Step 1: The data collection module obtains the water temperature data T of the thermal storage system in real time through the Internet storage , water outlet temperature data T out And return water temperature data T return , construct the heat storage temperature data set, and obtain the voltage V of the heat storage system power generation output thermal and current I thermal , calculate the power generation P of the thermal storage system thermal ;Construct thermal storage power generation data set; The data collection module obtains the voltage V output by the photovoltaic system in real time through the Internet. pv 、Current I pv , calculate the power generation P of the photovoltaic system pv , generate photovoltaic power data set; Step 2: The prediction module retrieves the photovoltaic power data set and predicts the photovoltaic power generation P of the photovoltaic system in the next time period t through regression analysis. pv,forecast ; Retrieve the thermal storage power generation data set and use the decision tree regression model to predict the thermal storage power generation P in the next time period t thermal,forecast ; When executing step 2, the photovoltaic power generation P pv,forecast The specific calculation formula is as follows: P pv，forecast =a0+a1·P pv,real +a2·P load,hist +a3·Weather Factors+∈; Among them, P pv,forecas is the predicted photovoltaic power generation; a0 is the intercept, which indicates the power generation when all independent variables are 0; a1, a2 and a3 are regression coefficients, which respectively indicate the influence of photovoltaic real-time power generation, historical power consumption and weather factors on power generation; ∈ is the error term, which indicates the prediction error of the model; P pv,real is the real-time photovoltaic system power generation, P pv,hist is the historical photovoltaic power generation data, P pv,real and P pv,hist All are calculated using data from the photovoltaic power data set; Weather Factors is weather data, including solar radiation intensity; Thermal storage power generation P thermal,forecast The specific calculation formula is as follows: For a given input feature vector X, use the trained decision tree model to make predictions: X=[T storage ,T out ,T return ,P thermal,past ]; P thermal,forecast =Tree(X); P thermal,past It refers to the power generation data of the thermal storage system in a certain period of time in the past, which is obtained through the thermal storage power generation data set; Tree represents the decision tree model; Step 3: The prediction module calculates the total predicted power generation P total,forecast ; P total,forecast =P pv,forecast +P thermal,forecast ; Step 4: The control decision module obtains historical heating demand data and historical electricity consumption data, draws a time series diagram of historical electricity consumption based on the historical electricity consumption data, and calculates the predicted user load P for the next time period t based on the ARIMA model. load,next ; Retrieve the total predicted power generation P calculated by the prediction module total,forecast , make decisions, and optimize: decision making: If P total,forecast <P load,next , the heat storage system enters the heat release mode; If P total,forecast ≥P load,next +s, the thermal storage system enters the thermal storage mode; Optimizing decisions: If T out <T demand , the thermal storage system gives priority to heating; If T storage >T max , the thermal storage system considers entering the heat release mode; T demand The target temperature for heating demand of users; T max is the maximum safe temperature of water stored in the thermal storage system; s is the preset safety margin; According to the load fluctuation frequency F load , power generation change frequency F pv , system response time T response and heating demand change F heat , calculate the duration T of the mode switching of the thermal storage system according to the following formula switch : Among them, k is the adjustment coefficient, which is adjusted according to the actual operation of the thermal storage system and reflects the influence of various factors on the duration; Step 5: The control decision module sends the control decision to the thermal storage system. The control decision includes decision-making and optimization decision. The thermal storage system starts the corresponding valves and pumps according to the control decision, adjusts the coordinated exchange of thermal energy and electrical energy, and realizes dynamic regulation of energy.

2. The electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow according to claim 1, characterized in that: When executing step 1, the power generation capacity P of the thermal storage system thermal Calculated by the following formula: P thermal =V thermal ×I thermal ; The power generation of the photovoltaic system P pv Calculated by the following formula: P pv =V pv ×I pv 。 3. The electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow according to claim 1, characterized in that: When executing step 4, the historical electricity consumption data is the historical user electricity consumption data obtained by the control decision module from the power grid system through the Internet; The execution steps of the control decision module are as follows: Step 4-1: The control decision module obtains historical power consumption data; Step 4-2: The control decision module cleans the historical power consumption data, removes outliers and missing values, constructs a historical power consumption data set, and arranges the data in the historical power consumption data set in time series, specifically in hourly, daily or weekly time series; Step 4-3: Draw a time series graph to obtain the trend, seasonality and cyclical characteristics of the data; Draw the autocorrelation graph ACF and partial autocorrelation graph PACF; Step 4-4: Determine the parameters of the ARIMA model, including the number of autoregressive terms p, the number of differences d, and the number of moving average terms q. The number of autoregressive terms p is the number of significant lags obtained from the PACF diagram, the number of moving average terms q is the number of significant lags obtained from the ACF diagram, and the number of differences d is the number of differences performed when the unit root test is used to check the stationarity of the time series. Step 4-5: Build ARIMA model: ARIMA(p,d,q); Step 4-6: Fit the ARIMA model using historical electricity consumption data; Step 4-7: Use the fitted ARIMA model to predict the next time period. The prediction formula is: in, is the predicted user load; μ is the constant term of the model; φ1, φ2, ..., φ p are all autoregressive coefficients; θ1, θ2, ..., θ q are all moving average coefficients; ∈ t-1 ,∈ t-1 , ...,∈ t-q are all error terms; Step 4-8: The predicted value As the predicted user load P for the next time period load,next ; Step 4-9: Use the mean square error (MSE) or root mean square error (RMSE) indicator to evaluate the predictive ability of the ARIMA model; Step 4-10: Regularly adjust the ARIMA model parameters based on the difference between actual electricity consumption and the predicted value.

4. The electrothermal synergistic method for directional transport of multi-scale electrothermal energy flow according to claim 1, characterized in that: The load fluctuation frequency F load By analyzing the changes in historical electricity consumption data, the frequency of load changes per unit time is calculated; Power generation change frequency F pv It is obtained by monitoring the power generation of the photovoltaic system and recording the number of changes in power generation per unit time; System response time T response It is determined by the system's reaction time to changes in input signals and is a preset fixed value; Change in heating demand F heat It is obtained by analyzing the changes in historical heating demand data, and represents the number of changes in heating demand within a statistical unit time.

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