Virtual power plant electric heating resource adjustment method, device, equipment and storage medium

Abstract

The present application discloses a method, device, equipment and storage medium for regulating electric heating resources in a virtual power plant, and relates to the technical field of intelligent energy management. The application discloses: obtaining current environmental parameters, current time parameters and current user behavior; analyzing the current environmental parameters, current time parameters and current user behavior to obtain predicted user heating demand; obtaining electric heating resource regulation parameters according to predicted user heating demand and current heating resource load; regulating the power consumption of the current electric heating equipment according to the electric heating resource regulation parameters to achieve regulation of the electric heating resources; the method controls the power consumption of the electric heating equipment more finely and improves energy utilization efficiency by obtaining the current environmental parameters, current time parameters and current user behavior, and dynamically adjusts the operating status of the electric heating equipment according to predicted user heating demand and current heating resource load to ensure maximum energy conservation while satisfying user comfort.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent energy management, and in particular to methods, devices, equipment and storage media for regulating electric heating resources in virtual power plants. Background Art

[0002] With the country's support for the new energy industry and the advancement of power system reform, the concept of virtual power plants has received more and more attention and has begun to be implemented on a trial basis in some regions. For example, some cities have begun to try to use virtual power plants to balance supply and demand, improve energy efficiency and promote the consumption of clean energy. In the future, with the advancement of technology and policy support, virtual power plants are expected to be more widely used and developed in China and even around the world.

[0003] With the reform of the power market, the electricity price policy has become more complicated. The policies such as time-of-use electricity price and peak-valley electricity price make the electricity expenditure of users uncertain. The global energy resources are becoming increasingly scarce. Improving energy utilization efficiency has become a social consensus. Especially in the field of heating, energy saving and consumption reduction are particularly important. The development of Internet of Things technology has enabled various sensors and smart devices to be widely used in heating systems, realizing real-time monitoring of environmental parameters and equipment status. However, different users have different requirements for indoor temperature. Some users like a warm and comfortable environment, while some users pay more attention to energy saving. Changes in indoor and outdoor temperature, humidity and other environmental conditions will affect the comfort of users. Traditional heating systems often cannot be adjusted in time to meet diverse needs. At present, the control of electric heating resources is fixed and requires manual control and adjustment. It cannot adapt to the actual heating needs of users, wastes resources and affects the user experience.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a method, device, equipment and storage medium for regulating electric heating resources in a virtual power plant, aiming to solve the technical problem that the current control of electric heating resources is fixed and requires manual control and adjustment, cannot adapt to the actual heating needs of users, wastes resources and affects the user experience.

[0006] To achieve the above objectives, the present application proposes a virtual power plant electric heating resource adjustment method, the virtual power plant electric heating resource adjustment method comprising: Get current environment parameters, current time parameters and current user behavior; Analyze the current environmental parameters, the current time parameters, and the current user behavior to obtain a predicted user heating demand; Obtaining electric heating resource adjustment parameters according to the predicted user heating demand and current heating resource load; The power consumption of the current electric heating equipment is adjusted according to the electric heating resource adjustment parameters to achieve adjustment of the electric heating resources.

[0007] In one embodiment, the analyzing the current environmental parameter, the current time parameter and the current user behavior to obtain the predicted user heating demand includes: Preprocessing the current environment parameter, the current time parameter and the current user behavior to obtain environment characteristics, time characteristics and user behavior characteristics; Obtaining a predicted temperature and a predicted user behavior according to the time characteristics, the environmental characteristics, and the user behavior characteristics; A multi-factor coupling analysis is performed on the environmental characteristics, the predicted temperature, the predicted user behavior and the historical power consumption according to the user heating demand prediction model to obtain the predicted user heating demand.

[0008] In one embodiment, before performing a multi-factor coupling analysis on the environmental characteristics, the predicted temperature, the predicted user behavior, and the historical power consumption according to the user heating demand prediction model to obtain the predicted user heating demand, the method further includes: Obtaining the initial user heating demand prediction model and training data set; Optimizing the parameters of the user heating demand prediction model according to a preset algorithm and the training data set to obtain the number of reference trees and reference leaf nodes; The parameters of the initial user heating demand prediction model are adjusted based on the number of reference trees and the reference leaf nodes to obtain a user heating demand prediction model.

[0009] In one embodiment, obtaining the electric heating resource adjustment parameter according to the predicted user heating demand and the current heating resource load includes: Obtaining a predicted heating resource load based on the current heating resource load, the current environmental parameters and the current time parameters according to a preset load prediction model; Constructing a multi-objective optimization function according to the predicted heating resource load, the predicted user heating demand, the user comfort range and the time-of-use electricity price ladder; The optimization solution is performed according to the multi-objective optimization function to obtain the electric heating resource adjustment parameters.

[0010] In one embodiment, the multi-objective optimization function is constructed according to the predicted heating resource load, the predicted user heating demand, the user comfort range and the time-of-use electricity price ladder, including: Obtaining an energy consumption function, a comfort function, a user heating demand function, and an electricity expense function according to the predicted heating resource load, the predicted user heating demand, the user comfort range, and the time-of-use electricity price ladder; Obtaining a multi-objective optimization function according to the energy consumption function, the comfort function, the user heating demand function and the electricity expense function; The optimization solution is performed according to the multi-objective optimization function to obtain the electric heating resource adjustment parameters, including: Obtaining comprehensive constraints, solving the multi-objective optimization function according to a preset optimization algorithm and the comprehensive constraints, and obtaining target adjustment parameters; The electric heating resource adjustment parameter is obtained according to the target adjustment parameter.

[0011] In one embodiment, the predicted heating resource load is obtained based on the current heating resource load, the current environmental parameter and the current time parameter according to the preset load prediction model, including: Obtaining a time series based on the preset load prediction model according to the current heating resource load, the current environmental parameters and the current time parameters; Decomposing the time series multiple times to obtain multiple decomposition sequences, and obtaining residual components according to the decomposition sequences; A residual predicted heating resource load is obtained according to each residual component, and a predicted heating resource load is obtained according to a plurality of the residual predicted heating resource loads.

[0012] In one embodiment, obtaining a residual predicted heating resource load according to each residual component, and obtaining a predicted heating resource load according to a plurality of the residual predicted heating resource loads, comprises: Get the atrous causal convolution,filter, expansion factor, and kernel size; Sampling the residual component according to the atrous causal convolution, the filter, the expansion coefficient, and the convolution kernel size to obtain a sampling feature; The sampling features are processed across levels through an identity mapping function to obtain a residual prediction of heating resource load; The predicted heating resource load is obtained by predicting the heating resource load according to the multiple residuals.

[0013] In addition, to achieve the above-mentioned purpose, the present application also proposes a virtual power plant electric heating resource adjustment device, the virtual power plant electric heating resource adjustment device comprising: Parameter acquisition module, used to obtain current environment parameters, current time parameters and current user behavior; A heating demand prediction module, used to analyze the current environmental parameters, the current time parameters and the current user behavior to obtain a predicted user heating demand; An adjustment module, used for obtaining electric heating resource adjustment parameters according to the predicted user heating demand and the current heating resource load; The adjustment module is also used to adjust the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve the adjustment of the electric heating resources.

[0014] In addition, to achieve the above-mentioned objectives, the present application also proposes a virtual power plant electric heating resource regulation device, which includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, and the computer program is configured to implement the steps of the virtual power plant electric heating resource regulation method as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the virtual power plant electric heating resource adjustment method as described above are implemented.

[0016] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the virtual power plant electric heating resource adjustment method as described above.

[0017] One or more technical solutions proposed in this application have at least the following technical effects: by obtaining current environmental parameters, current time parameters and current user behavior, the power consumption of electric heating equipment can be more finely controlled to improve energy utilization efficiency; at the same time, according to the predicted user heating demand and the current heating resource load, the operating status of the electric heating equipment is dynamically adjusted to ensure maximum energy saving while meeting user comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1A flow chart of the first embodiment of the method for regulating electric heating resources in a virtual power plant of the present application; Figure 2 The actual operation logic diagram of the electric heating resource regulation system provided in the first embodiment of the electric heating resource regulation method of the virtual power plant of the present application; Figure 3 A flow chart of Embodiment 2 of the method for regulating electric heating resources in a virtual power plant of the present application; Figure 4 A schematic diagram of a heating resource load prediction process provided in Example 2 of the virtual power plant electric heating resource adjustment method of the present application; Figure 5 This is a schematic diagram of the module structure of the electric heating resource adjustment device of the virtual power plant according to the embodiment of the present application; Figure 6 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the virtual power plant electric heating resource adjustment method in the embodiment of the present application. DETAILED DESCRIPTION

[0021] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0022] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solutions of the embodiments of the present application are: obtaining current environmental parameters, current time parameters and current user behavior; analyzing the current environmental parameters, current time parameters and current user behavior to obtain predicted user heating demand; obtaining electric heating resource adjustment parameters based on predicted user heating demand and current heating resource load; adjusting the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve adjustment of electric heating resources.

[0024] In this embodiment, for ease of description, the following description is based on the identification of the virtual power plant electric heating resource regulation equipment as the execution entity.

[0025] Due to the existing technology, different users have different demands for indoor temperature. Some users like a warm and comfortable environment, while some users pay more attention to energy saving. Changes in indoor and outdoor temperature, humidity and other environmental conditions will affect the user's comfort. Traditional heating systems often cannot be adjusted in time to meet diverse needs.

[0026] With the reform of the power market, the electricity price policy has become more complicated. The policies such as time-of-use electricity price and peak-valley electricity price have made the electricity bill expenditure of users uncertain. The global energy resources are becoming increasingly scarce. Improving energy utilization efficiency has become a social consensus. Especially in the field of heating, energy saving and consumption reduction are particularly important. The development of Internet of Things technology has enabled various sensors and smart devices to be widely used in heating systems, realizing real-time monitoring of environmental parameters and equipment status. However, the current control of electric heating resources is fixed and requires manual control and adjustment, which cannot meet the actual heating needs of users, wasting resources and affecting the user experience.

[0027] The present application provides a solution that, by obtaining current environmental parameters, current time parameters, and current user behavior, more finely controls the power consumption of electric heating equipment and improves energy utilization efficiency. At the same time, according to the predicted user heating demand and the current heating resource load, the operating status of the electric heating equipment is dynamically adjusted to ensure maximum energy conservation while meeting user comfort.

[0028] It can be seen from the above embodiments that the present application discloses a virtual power plant electric heating resource adjustment method, device, equipment and storage medium, which relates to the field of energy intelligent management technology, and discloses: obtaining current environmental parameters, current time parameters and current user behavior; analyzing the current environmental parameters, current time parameters and current user behavior to obtain predicted user heating demand; obtaining electric heating resource adjustment parameters according to the predicted user heating demand and the current heating resource load; adjusting the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve electric heating resource adjustment; the method obtains the current environmental parameters, current time parameters and current user behavior to more finely control the power consumption of the electric heating equipment and improve energy utilization efficiency, and at the same time dynamically adjusts the operating status of the electric heating equipment according to the predicted user heating demand and the current heating resource load to ensure maximum energy saving while meeting user comfort.

[0029] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a virtual power plant electric heating resource adjustment device, etc. The following takes the virtual power plant electric heating resource adjustment device as an example to illustrate this embodiment and the following embodiments.

[0030] Based on this, the embodiment of the present application provides a virtual power plant electric heating resource adjustment method, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for regulating electric heating resources in a virtual power plant of the present application.

[0031] In this embodiment, the virtual power plant electric heating resource adjustment method includes steps S10 to S40: Step S10, obtaining current environment parameters, current time parameters and current user behavior.

[0032] It is understandable that the current environmental parameters may include outdoor temperature and indoor temperature, and the current time parameter may be obtained according to the room-to-room granularity requirements, including year, month, day and specific time.

[0033] It should be noted that current user behavior can be collecting users' daily activity data, such as waking up time, returning home time, sleeping time, etc., through smart devices (such as smart watches, smart speakers); monitoring the occupancy status of family members through smart door locks, smart meters and other devices; collecting users' temperature comfort preferences through user feedback (such as mobile applications, smart speakers), etc.

[0034] Step S20, analyzing the current environmental parameters, the current time parameters and the current user behavior to obtain a predicted user heating demand.

[0035] It should be noted that, before the multi-factor coupling analysis of the environmental characteristics, the predicted temperature, the predicted user behavior and the historical electricity consumption is performed according to the user heating demand prediction model to obtain the predicted user heating demand, it also includes: obtaining the initial user heating demand prediction model and the training data set; optimizing the parameters of the user heating demand prediction model according to the preset algorithm and the training data set to obtain the number of reference trees and reference leaf nodes; and adjusting the parameters of the initial user heating demand prediction model based on the number of reference trees and the reference leaf nodes to obtain the user heating demand prediction model.

[0036] It is understandable that the initial user heating demand prediction model can be obtained by performing Bootstrap sampling based on the training data set to obtain K samples, and modeling them into a decision tree, that is, a weak classifier; then each weak classifier will produce a corresponding decision result, and then select the corresponding variables to model the decision tree, so as to obtain a random forest with K decision trees; finally, the voting mechanism is used to find the tree with the highest score, and it is output as the final result to obtain the initial user heating demand prediction model. For details, please refer to the following formula:

[0037] Among them, S i represents the data in the i-th training data set, C k represents the kth decision tree, η(C k , S i ) is C k For S iThe decision value of , max represents the functional form of the voting mechanism.

[0038] It should be noted that after the model training is completed, the model can also be retrained. The selected model can be trained using training data, and the model parameters can be optimized through cross-validation. Hyperparameters such as learning rate, number of iterations, number of hidden layer nodes, etc. can be adjusted to improve the prediction performance of the model. Key indicators such as mean square error (MSE), mean absolute error (MAE), R² score and other test data can be used to evaluate the prediction performance of the model to ensure the accuracy and stability of the model.

[0039] It should be emphasized that the user heating demand prediction model is optimized according to the preset algorithm and the training data set, and the preset algorithm in obtaining the number of reference trees and reference leaf nodes can be the gray wolf algorithm, the butterfly algorithm or the ant colony algorithm.

[0040] It is worth noting that the number of random forest trees and leaf nodes has a great impact on the prediction results. Too many trees will cause overfitting, while too small a number will lead to unstable prediction models. Leaf nodes are the number of features randomly selected from all feature sets and used as the number of segmentation variables. In order to improve the prediction performance of the RF algorithm, the preset algorithm is used to optimize to obtain the optimal number of trees and leaf nodes.

[0041] In a feasible implementation, step S10 may include steps A11 to A13: Step A11, pre-processing the current environment parameters, the current time parameters and the current user behavior to obtain environment characteristics, time characteristics and user behavior characteristics.

[0042] It should be noted that preprocessing the current environment parameters, the current time parameters and the current user behavior to obtain environment characteristics, time characteristics and user behavior characteristics can be performed by quantifying the current environment parameters, the current time parameters and the current user behavior to obtain quantized environment parameters, quantized time parameters and quantized current user behavior parameters; standardizing the quantized environment parameters, the quantized time parameters and the quantized current user behavior parameters to obtain standard environment parameters, standard time parameters and standard user behavior parameters; and performing dimensionality reduction on the standard environment parameters, standard time parameters and standard user behavior parameters to obtain environment characteristics, time characteristics and user behavior characteristics.

[0043] Among them, it should be noted that there are multiple types of information about the current environmental parameters, the current time parameters and the current user behavior, which may be text, numbers, etc. The mixture of multiple different information has a certain impact on the model analysis and prediction of user heating demand. Different environments, different times and different behaviors are represented by quantification in a fixed numerical format.

[0044] Among them, there are certain differences in the numerical values ​​of the quantified environmental parameters, time parameters, and user behaviors. In order to facilitate subsequent data analysis, the environmental parameters, time parameters, and user behaviors can be further standardized.

[0045] It is understandable that neither the current parameters nor the historical parameters can completely guarantee that the data information is completely correct and complete. The data can be preprocessed before being analyzed through the model. Preprocessing can also include replacement, elimination and mean replacement of missing values; it can also include correction and elimination of outliers; such data cleaning can be processed according to actual conditions, and this embodiment does not limit this.

[0046] Step A12, obtaining predicted temperature and predicted user behavior according to the time characteristics, the environmental characteristics and the user behavior characteristics.

[0047] It is understandable that a detailed analysis based on the described time characteristics, environmental characteristics and user behavior characteristics can be performed to obtain an accurate predicted temperature and predicted user behavior.

[0048] It is understandable that time characteristics may include different time periods of the day, different days of the week, and different seasons of the year. These time factors will have a significant impact on temperature and user behavior. For example, in the afternoon of summer, the temperature may be higher, and the user's behavior may tend to choose indoor activities.

[0049] It should be understood that environmental characteristics may include geographical location, climate type, current weather conditions, etc. Different geographical locations and climate types will lead to different temperature changes, and current weather conditions such as sunny, cloudy, rainy, etc. will also affect the temperature and user behavior. For example, in a coastal city, due to the influence of sea breeze, the temperature may be relatively low, and the user's behavior may be more inclined to outdoor activities.

[0050] User behavior characteristics may include the user's age, gender, occupation, hobbies, etc. These factors will affect the user's behavior choices at different temperatures. For example, young people may prefer to do outdoor sports in high temperatures, while the elderly may prefer to do relatively quiet activities indoors.

[0051] Through comprehensive analysis of these time characteristics, environmental characteristics, and user behavior characteristics, we can use advanced prediction models and algorithms to obtain an accurate predicted temperature and predicted user behavior.

[0052] It should be noted that the predicted temperature can be a prediction of the temperature for a period of time in the future based on the time and the weather temperature in historical time. It can be predicted by a neural network or by other methods, and this embodiment does not limit this.

[0053] Furthermore, user behavior may be predicted based on time and user historical behavior, may be predicted through a neural network, or may be predicted through other methods, which is not limited in this embodiment.

[0054] Step A13, performing a multi-factor coupling analysis on the environmental characteristics, the predicted temperature, the predicted user behavior and the historical power consumption according to the user heating demand prediction model to obtain the predicted user heating demand.

[0055] It is understandable that performing a multi-factor coupling analysis of the environmental characteristics, the predicted temperature, the predicted user behavior and the historical electricity consumption according to the user heating demand prediction model can be to fuse the environmental characteristics, the predicted temperature, the predicted user behavior and the historical electricity consumption to obtain a fusion feature, and perform analysis based on the fusion feature to obtain a predicted user heating demand.

[0056] Furthermore, according to the user heating demand prediction model, a multi-factor coupling analysis is performed on the environmental characteristics, the predicted temperature, the predicted user behavior and the historical electricity consumption to obtain the predicted user heating demand. The real-time data and the historical data are input into the trained GBDT model to obtain the user heating demand forecast for a period of time in the future. The prediction results may include hourly or daily electricity consumption, indoor temperature change trends, etc.

[0057] It should be noted that when the model is used, the accuracy and stability of the prediction results can be further analyzed to ensure the reliability of the model, compare the differences between the prediction results and the actual data, and evaluate the prediction ability of the model.

[0058] In this embodiment, through multi-factor coupling analysis and multi-model prediction, the system can more accurately predict the user's heating needs and reduce prediction errors. By dynamically adjusting the operating status of the electric heating equipment and optimizing load distribution, it can improve energy utilization efficiency, avoid resource waste and equipment overload, and through intelligent temperature control and user behavior adaptation, it can ensure that the indoor temperature is always within the user's comfort range, thereby improving the user's living experience.

[0059] The above are only feasible implementations of step S20 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S20.

[0060] Step S30, obtaining electric heating resource adjustment parameters according to the predicted user heating demand and the current heating resource load.

[0061] It is understandable that the prediction of user heating demand can be based on the output of the user heating demand prediction model, and further combined with the real-time load situation of the current heating resources for comprehensive consideration.

[0062] It should be understood that predicting a user's heating demand can be to predict whether the user will use electricity for heating in the future, the total energy consumption of electricity, and the duration of electricity use.

[0063] It should be understood that the heating resource load may include factors such as the current operating status of the electric heating equipment, the power supply situation, and possible changes in the external environment.

[0064] It should be understood that the current heating resource load can be obtained based on the current user's electricity consumption. At the same time, the electricity load curve can be further obtained based on the current heating resource load and the historical heating resource load. The electric heating resources are adjusted based on the electricity load curve to obtain the electric heating adjustment parameters.

[0065] It should be emphasized that by predicting user heating demand and current heating resource load, the electric heating resource adjustment parameters can be determined, such as adjusting the power output of electric heating equipment, adjusting the power distribution strategy, optimizing thermal energy storage and release, etc.

[0066] It is worth mentioning that the purpose of adjusting the parameters is to ensure efficient use of energy and stable operation of the heating system while meeting the heating needs of users. Through the implementation of these adjustment parameters, dynamic optimization management of electric heating resources can be achieved, thereby improving the operating efficiency of the entire system and the comfort of users.

[0067] Step S40, adjusting the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve adjustment of the electric heating resources.

[0068] It should be noted that the virtual power plant electric heating resource regulation method in this embodiment is applied to the electric heating resource regulation system, which may include a cloud, an edge device, and an electric heating device. The current environmental parameters, the current time parameters, and the current user behavior are obtained through the edge device, and the current environmental parameters, the current time parameters, and the current user behavior are analyzed through the cloud to obtain the predicted user heating demand, and the heating resource regulation parameters are obtained according to the predicted user heating demand and the current heating resource load; the power consumption of the current electric heating device is adjusted according to the heating resource regulation parameters through the edge device to achieve the regulation of the electric heating resources.

[0069] In specific implementation, according to the described electric heating resource adjustment parameters, the current electric heating equipment can be accurately adjusted for power consumption, which can better achieve effective management and optimization of electric heating resources. By finely adjusting the power consumption of electric heating equipment, it is possible to ensure the reasonable allocation and use of electric heating resources in different time periods and under different environmental conditions. This can not only improve the overall efficiency of the electric heating system, but also reduce energy consumption to a certain extent, thereby achieving the purpose of energy conservation and emission reduction, better realizing the scientific management and reasonable use of electric heating resources, and ensuring the optimal operation of electric heating equipment under different environments and requirements.

[0070] Furthermore, the actual operation logic diagram of the electric heating resource regulation system can be referred to Figure 2 , Figure 2 Electric heating resources are regulated without affecting user comfort. First, the cloud monitoring system sends the load regulation target to the smart energy terminal on the edge side; secondly, the smart energy terminal on the edge side converts the load regulation target into the temperature regulation instruction of the electric heating system, that is, the electric heating resource adjustment parameter, based on the collected real-time operation status information of the electric heating system, that is, the current environmental parameters, the current time parameters and the current user behavior, based on the built-in strategy model; then, the smart energy terminal sends the regulation instruction to the electric heating control system and continuously monitors the changes in the electric heating load power; finally, based on the power monitoring situation, the smart energy terminal continuously optimizes the electric heating control instruction and sends it to the electric heating control system for execution until the electric heating load power reaches the target value.

[0071] It should be emphasized that a multi-objective optimization model is constructed with the optimal economic operation of the system as the goal, taking into account factors such as the internal energy balance of multiple flexible resources, energy storage SOC, and ensuring the basic production and life of users, to construct a collaborative control strategy model for multiple flexible resources; secondly, the collaborative control strategy model for multiple flexible resources is containerized and deployed in the smart energy terminal; then, the smart energy terminal receives the overall control target issued by the virtual power plant platform, and converts the overall control target into control instructions for each flexible resource (distributed power source, energy storage, adjustable load) based on the built-in strategy model, and sends the control instructions to each flexible resource for power control, and continuously monitors the user's total line and each branch load power data; finally, based on the power monitoring situation, the smart energy terminal collaborative control strategy model continuously adjusts the control instructions and sends them to each flexible resource for execution, so that the total line power reaches the target value.

[0072] This embodiment provides a method for regulating electric heating resources in a virtual power plant. By obtaining current environmental parameters, current time parameters and current user behavior, the power consumption of electric heating equipment can be more finely controlled to improve energy utilization efficiency. At the same time, according to the predicted user heating demand and the current heating resource load, the operating status of the electric heating equipment is dynamically adjusted to ensure maximum energy saving while meeting user comfort.

[0073] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S30 also includes steps S31 to S33: Step S31, obtaining a predicted heating resource load based on the current heating resource load, the current environmental parameters and the current time parameters according to a preset load prediction model.

[0074] It is understandable that the current heating resource load may refer to the energy or power being used by the electric heating system at the current moment, which is usually the real-time power consumption of the electric heating equipment, and the unit may be kilowatt (kW) or watt (W).

[0075] It is understandable that the current environmental parameters may include external environmental factors that affect the operation of the electric heating equipment, including but not limited to outdoor temperature, humidity, wind speed, etc.

[0076] It is understandable that the current time parameter refers to the current date and time information, including hours, dates, seasons, etc. The time parameters can help the model capture the periodicity and trend characteristics in time.

[0077] It should be noted that environmental parameters directly affect heating demand. For example, the lower the outdoor temperature, the greater the heating demand. Environmental parameters as input to the model can improve the accuracy of the prediction because these parameters can reflect the impact of the external environment on heating demand.

[0078] It should be noted that by combining "current heating resource load, current environmental parameters and current time parameters", the preset load forecasting model can more comprehensively understand the current system operating status and external environmental conditions, thereby more accurately predicting future heating resource loads and comprehensively improving the model's forecasting accuracy and robustness.

[0079] It is understandable that the preset load forecasting model can be a machine learning model: Random Forest: suitable for multi-factor analysis and can handle nonlinear relationships. Gradient Boosting: boosting tree model, capable of handling complex nonlinear relationships. Support Vector Machine (SVM): suitable for high-dimensional data and can handle multi-factor coupling problems. It can also be a deep learning model: LSTM (Long Short-Term Memory): suitable for time series data and can capture time dependencies. GRU (Gated Recurrent Unit): a simplified version of LSTM, suitable for large-scale data sets. 1D CNN (Convolutional Neural Network): suitable for feature extraction and can capture local features.

[0080] It should be understood that predicting the heating resource load may be a prediction of the electricity load that may be brought about by the heating equipment in a certain period of time in the future.

[0081] In a feasible implementation, step S31 may include steps A311 to A313: Step A311, obtaining a time series based on the preset load prediction model according to the current heating resource load, the current environmental parameters and the current time parameters.

[0082] It should be noted that the current heating resource load and the current environmental parameters are time-marked based on the current time parameters to obtain an initial sequence; further, a noise sequence is obtained, and the noise sequence is added to the initial sequence to obtain a time series.

[0083] Step A312, decomposing the time series multiple times to obtain multiple decomposition sequences, and obtaining residual components according to the decomposition sequences.

[0084] It should be noted that the local maximum and local minimum of the time series are found, and the upper envelope u(t) and the lower envelope l(t) are generated by the interpolation method based on the local maximum and local minimum.

[0085] It should be noted that the upper envelope is generated by connecting all local maximum points, and the lower envelope is generated by connecting all local minimum points.

[0086] Calculate the average value of the upper and lower envelopes m(t) = u(t) + l(t) / 2. Further extract the instantaneous high-frequency component and subtract the mean value m(t) from the original signal to obtain the instantaneous high-frequency component h(t) = x(t) − m(t).

[0087] Check whether h(t) satisfies the IMF conditions: the number of extreme points (maxima and minima) and the number of zero crossings differ by at most one over the entire data segment. At any time, the local means of the upper and lower envelopes are zero.

[0088] It should be noted that if h(t) satisfies the IMF condition, it is taken as the first IMF; otherwise, h(t) is taken as a new input signal and the judgment is repeated.

[0089] Furthermore, the residual component is calculated by subtracting the first IMF from the original signal to obtain a new signal r1(t)=x(t)−IMF1, and taking r1(t) as the new input signal until the residual signal rn(t) becomes a monotonic function or satisfies other stopping conditions. Then the original signal x(t) can be expressed as the sum of several IMFs and a residual term.

[0090] Step A313, obtaining a residual predicted heating resource load according to each residual component, and obtaining a predicted heating resource load according to a plurality of the residual predicted heating resource loads.

[0091] It should be noted that obtaining the residual predicted heating resource load according to each residual component and obtaining the predicted heating resource load according to multiple residual predicted heating resource loads can be performed by obtaining a void causal convolution, a filter, an expansion coefficient and a convolution kernel size; sampling the residual components according to the void causal convolution, the filter, the expansion coefficient and the convolution kernel size to obtain sampling characteristics; performing cross-level processing on the sampling characteristics through an identity mapping function to obtain the residual predicted heating resource load; and obtaining the predicted heating resource load according to multiple residual predicted heating resource loads.

[0092] It should be noted that the process of predicting heating resource load can refer to Figure 4 In the figure, the time series is decomposed to obtain n residual components, and each residual component is predicted to obtain multiple prediction values, which are superimposed based on the prediction values ​​of each component as the final predicted heating resource load.

[0093] Furthermore, the heating resource prediction for each residual component can be achieved through a temporal convolutional network, which is a deep learning model for processing time series data. Unlike traditional recurrent neural networks (RNNs) and long short-term memory networks (LSTMs), TCNs use the architecture of convolutional neural networks (CNNs) to process time series data, which has the advantages of parallel computing and can capture long dependencies. TCNs have performed well in many fields, especially in time series prediction, speech recognition, and natural language processing.

[0094] It should be noted that the atrous causal convolution, filter, expansion coefficient and convolution kernel size are obtained, and the convolution function is further constructed according to the atrous causal convolution, filter, expansion coefficient and convolution kernel size. For details, please refer to the following formula:

[0095] Among them, X represents the residual component of the input function, f is the filter, d is the dilation coefficient, k is the convolution kernel size, and s-di represents the convolution operation.

[0096] It should be further explained that the sampling features are processed across levels through the identity mapping function to obtain the residual prediction heating resource load, which can refer to the following formula:

[0097] Among them, θ represents the activation function, and y represents the residual predicted heating resource load.

[0098] In this embodiment, the residual components obtained by decomposition reflect the random fluctuations and noise in the time series. By adding white noise and decomposing it multiple times and then taking the average value, the influence of modal aliasing and endpoint effects can be reduced, and the complex heating resource load data can be decomposed into multiple decomposition sequences of different frequencies. The multi-level decomposition helps to capture the changing characteristics on different time scales, improve the accuracy of the prediction, and effectively reduce the prediction error.

[0099] The above are only feasible implementations of step S31 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S31.

[0100] Step S32, constructing a multi-objective optimization function according to the predicted heating resource load, the predicted user heating demand, the user comfort range and the time-of-use electricity price ladder.

[0101] It should be understood that multi-objective optimization function refers to the process of finding the best solution among multiple objectives. In practical applications, many problems involve multiple objectives, which may conflict with each other, so it is necessary to find a balance point to meet the best combination of all objectives.

[0102] It should be noted that multi-objective optimization problems can usually be expressed by a mathematical model, which contains multiple objective functions and a set of constraints.

[0103] In a feasible implementation, step S32 may include steps A321-A322: Step A321, obtaining energy consumption function, comfort function, user heating demand function and electricity expense function according to the predicted heating resource load, the predicted user heating demand, user comfort range and time-of-use electricity price ladder.

[0104] It should be noted that the energy consumption for a period of time in the future is calculated based on the predicted heating resource load. The energy consumption function can refer to the following formula:

[0105] Among them, P heat (t) represents the heating power at time t. The predicted heating resource load can be a histogram curve or other formats. The heating power at each time point can be obtained based on the predicted heating resource load.

[0106] It should be emphasized that the goal of the energy consumption function is to minimize the total energy consumption of the user's heating demand. Furthermore, the predicted user heating demand may be a user heating demand function according to different heating demands of the user. The specific user heating demand function may refer to the following formula:

[0107] Among them, T room (t) represents the indoor temperature at time t; T sset (t) represents the set temperature at time t. The goal of the user heating demand function is to meet the user's heating needs and ensure user comfort.

[0108] Furthermore, the goal of obtaining the comfort function according to the user comfort range is to ensure that the indoor temperature is within the user comfort range. The comfort function spoiler can refer to the following formula:

[0109] Among them, T comfort It represents the middle value of the user's comfort range. The goal of the comfort function is to ensure that the indoor temperature is within the user's comfort range.

[0110] Among them, the goal of the time-of-use electricity price ladder is to minimize the user's electricity expenditure. For details, please refer to the following formula:

[0111] Among them, P price (t) represents the electricity price at time t.

[0112] Step A322, obtaining a multi-objective optimization function according to the energy consumption function, the comfort function, the user heating demand function and the electricity expenditure function.

[0113] It should be noted that the multi-objective optimization function obtained according to the energy consumption function, the comfort function, the user heating demand function and the electricity expense function can be obtained by combining the energy consumption function, the comfort function, the user heating demand function and the electricity expense function to construct a multi-objective optimization function. The objective function can be specifically referred to the following formula:

[0114] Among them, ω 1 ,ω 2 ,ω 3 ,ω 4 is the weight of each objective function, which can be preset. This embodiment does not limit this. Generally, ω 1 +ω 2 +ω 3 +ω 4 =1, or other adjustment methods can be set to determine the weight of each function.

[0115] Furthermore, it is important to emphasize that multiple objectives can be combined into a single objective function using a weighted summation method.

[0116] In this embodiment, by constructing a multi-objective optimization function, multiple objectives such as heating resource load, user heating demand, user comfort range, and time-of-use electricity price ladder can be comprehensively considered to construct a multi-objective optimization function. Based on the multi-objective optimization function, various factors can be fully considered, and finally the electric heating adjustment parameters that can meet various factors are obtained, so as to better flexibly control the electric heating resources and improve the user experience.

[0117] The above are only feasible implementations of step S32 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S32.

[0118] Step S33, performing optimization and solving according to the multi-objective optimization function to obtain the electric heating resource adjustment parameters.

[0119] It should be noted that the objective function and constraints are transformed into fuzzy sets, and the multi-objective optimization problem is solved by fuzzy logic.

[0120] It should be further explained that the fuzzy optimization algorithm is a strategy that converts the objective function and constraints into fuzzy sets and solves multi-objective optimization problems through fuzzy logic.

[0121] It should be emphasized that by converting the objective function and constraints into fuzzy sets and using fuzzy logic for reasoning and defuzzification, the fuzzy optimization method can provide effective solutions when dealing with multi-objective optimization problems with uncertainty and fuzziness. In the flexible regulation of electric heating resources, the fuzzy optimization method can help us find the best balance between minimizing energy consumption, maximizing user comfort and minimizing electricity bills.

[0122] It is worth noting that other optimization methods are also possible, which are not limited in this embodiment and can be adjusted according to actual conditions.

[0123] In a feasible implementation, step S33 may include steps A331-A332: Step A331, obtaining comprehensive constraints, solving the multi-objective optimization function according to a preset optimization algorithm and the comprehensive constraints, and obtaining target adjustment parameters.

[0124] It should be noted that the comprehensive constraint conditions may include constraints from multiple aspects, such as physical constraints, safety constraints, and user demand constraints.

[0125] In a specific implementation, the comprehensive constraint conditions may include indoor temperature constraints of a maximum indoor temperature range and a minimum indoor temperature range; maximum power consumption constraints; maximum electricity price constraints and minimum electricity price constraints; and user activity time constraints.

[0126] It should be emphasized that based on the comprehensive constraints, the energy consumption f1(x) is calculated by the power consumption of the electric heating equipment, the user comfort f2(x) is calculated by the deviation between the indoor temperature and the user comfort standard, and the electricity expenditure f3(x) is calculated by the power consumption and electricity price.

[0127] It should be noted that a variety of power consumption-comfort-electricity costs are generated based on comprehensive constraints, and each power consumption-comfort-electricity cost corresponds to a target parameter relative to the current parameter. In simple terms, any target parameter is selected according to the constraints, and the power consumption-comfort-electricity cost is calculated based on the target parameter.

[0128] In the specific implementation, the initial population {x 1 ,x 2, x N}, where N is the population size, each individual in the population represents a target parameter, and the fitness of each individual is evaluated to evaluate whether each target parameter is the optimal electric heating resource adjustment parameter; the fitness calculation of each individual can be optimized according to the power consumption-comfort-electricity cost corresponding to each target parameter.

[0129] Step A332, obtaining electric heating resource adjustment parameters according to the target adjustment parameters.

[0130] It is understandable that the target adjustment parameters may include power consumption, indoor temperature setting and heating time.

[0131] It should be noted that obtaining the electric heating resource adjustment parameters according to the target adjustment parameters can be to find the nearest power level for power consumption conversion; directly perform temperature control according to the indoor temperature; the heating time can also determine the start time and the shutdown time according to the current time.

[0132] In this embodiment, through the multi-objective optimization algorithm, the power consumption of the electric heating equipment can be accurately controlled to avoid overheating or insufficient heating, thereby reducing energy waste. The indoor temperature setting can be dynamically adjusted according to user comfort and energy consumption targets to ensure maximum energy savings while meeting user needs.

[0133] The above are only feasible implementations of step S33 provided in this embodiment, and this embodiment does not specifically limit the specific implementation of step S33.

[0134] This embodiment provides a method for regulating electric heating resources in a virtual power plant, which minimizes energy waste and improves energy utilization efficiency through precise control and dynamic adjustment; it can significantly reduce users' electricity bills through time-of-use electricity price optimization and energy storage utilization; it can ensure that the indoor temperature is always within the user's comfort range through intelligent temperature control and user behavior adaptation, thereby improving the user's living experience, and thus realizing refined, dynamic and intelligent control of electric heating equipment, which not only improves energy utilization efficiency, reduces users' electricity bills, and improves users' comfort, but also enhances the reliability and stability of electric heating resource control.

[0135] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the electric heating resource adjustment method of the virtual power plant of the present application. More simple transformations based on this technical concept are all within the scope of protection of the present application.

[0136] This application also provides a virtual power plant electric heating resource adjustment device, please refer to Figure 5 , the virtual power plant electric heating resource adjustment device comprises: The parameter acquisition module 10 is used to acquire the current environment parameters, the current time parameters and the current user behavior; A heating demand prediction module 20 is used to analyze the current environmental parameters, the current time parameters and the current user behavior to obtain a predicted user heating demand; An adjustment module 30, configured to obtain an electric heating resource adjustment parameter according to the predicted user heating demand and the current heating resource load; The adjustment module 30 is further used to adjust the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve the adjustment of the electric heating resources.

[0137] The virtual power plant electric heating resource adjustment device provided by the present application adopts the virtual power plant electric heating resource adjustment method in the above-mentioned embodiment, which can solve the technical problem that the current control of electric heating resources is fixed and requires manual control and adjustment, cannot adapt to the actual heating needs of users, wastes resources and affects the user experience. Compared with the prior art, the beneficial effects of the virtual power plant electric heating resource adjustment device provided by the present application are the same as the beneficial effects of the virtual power plant electric heating resource adjustment method provided by the above-mentioned embodiment, and the other technical features of the virtual power plant electric heating resource adjustment device are the same as the features disclosed in the above-mentioned embodiment method, which will not be repeated here.

[0138] The present application provides a virtual power plant electric heating resource regulation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the virtual power plant electric heating resource regulation method in the above-mentioned embodiment one.

[0139] Reference below Figure 6 , which shows a schematic diagram of the structure of a virtual power plant electric heating resource adjustment device suitable for implementing the embodiment of the present application. The virtual power plant electric heating resource adjustment device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The virtual power plant electric heating resource regulation device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0140] like Figure 6As shown, the virtual power plant electric heating resource regulation device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the virtual power plant electric heating resource regulation device are also stored in RAM1004. The processing device 1001, ROM1002 and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the virtual power plant electric heating resource regulation device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a virtual power plant electric heating resource regulation device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0141] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0142] The virtual power plant electric heating resource adjustment device provided by the present application adopts the virtual power plant electric heating resource adjustment method in the above-mentioned embodiment, which can solve the technical problem that the current control of electric heating resources is fixed and requires manual control and adjustment, cannot adapt to the actual heating needs of users, wastes resources and affects the user experience. Compared with the prior art, the beneficial effects of the virtual power plant electric heating resource adjustment device provided by the present application are the same as the beneficial effects of the virtual power plant electric heating resource adjustment method provided by the above-mentioned embodiment, and the other technical features of the virtual power plant electric heating resource adjustment device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0143] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0144] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0145] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the virtual power plant electric heating resource regulation method in the above-mentioned embodiment.

[0146] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0147] The above-mentioned computer-readable storage medium may be included in the virtual power plant electric heating resource regulation device; or it may exist independently without being assembled into the virtual power plant electric heating resource regulation device.

[0148] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the virtual power plant electric heating resource regulation device, the virtual power plant electric heating resource regulation device: obtains current environmental parameters, current time parameters and current user behavior; analyzes the current environmental parameters, current time parameters and current user behavior to obtain predicted user heating demand; obtains electric heating resource regulation parameters according to the predicted user heating demand and the current heating resource load; and adjusts the power consumption of the current electric heating equipment according to the electric heating resource regulation parameters to achieve regulation of the electric heating resources.

[0149] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0150] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0151] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0152] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned virtual power plant electric heating resource adjustment method, which can solve the technical problems that the current control of electric heating resources is fixed and requires manual control and adjustment, cannot adapt to the actual heating needs of users, wastes resources and affects the user experience. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the virtual power plant electric heating resource adjustment method provided by the above-mentioned embodiment, and will not be repeated here.

[0153] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned virtual power plant electric heating resource regulation method.

[0154] The computer program product provided by this application can solve the technical problem that the current control of electric heating resources is fixed and requires manual control and adjustment, cannot adapt to the actual heating needs of users, wastes resources and affects the user experience. Compared with the prior art, the beneficial effects of the computer program product provided by this application are the same as the beneficial effects of the virtual power plant electric heating resource adjustment method provided in the above embodiment, which will not be repeated here.

[0155] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for regulating electric heating resources in a virtual power plant, characterized in that: The virtual power plant electric heating resource adjustment method comprises: Get current environment parameters, current time parameters and current user behavior; Analyze the current environmental parameters, the current time parameters, and the current user behavior to obtain a predicted user heating demand; Obtaining electric heating resource adjustment parameters according to the predicted user heating demand and current heating resource load; The power consumption of the current electric heating equipment is adjusted according to the electric heating resource adjustment parameters to achieve adjustment of the electric heating resources.

2. The method for regulating electric heating resources of a virtual power plant according to claim 1, characterized in that: The analyzing the current environmental parameter, the current time parameter and the current user behavior to obtain a predicted user heating demand includes: Preprocessing the current environment parameter, the current time parameter and the current user behavior to obtain environment characteristics, time characteristics and user behavior characteristics; Obtaining a predicted temperature and a predicted user behavior according to the time characteristics, the environmental characteristics, and the user behavior characteristics; A multi-factor coupling analysis is performed on the environmental characteristics, the predicted temperature, the predicted user behavior and the historical power consumption according to the user heating demand prediction model to obtain the predicted user heating demand.

3. The method for regulating electric heating resources of a virtual power plant according to claim 1, characterized in that: Before performing a multi-factor coupling analysis on the environmental characteristics, the predicted temperature, the predicted user behavior and the historical power consumption according to the user heating demand prediction model to obtain the predicted user heating demand, the method further includes: Obtaining the initial user heating demand prediction model and training data set; Optimizing the parameters of the user heating demand prediction model according to a preset algorithm and the training data set to obtain the number of reference trees and reference leaf nodes; The parameters of the initial user heating demand prediction model are adjusted based on the number of reference trees and the reference leaf nodes to obtain a user heating demand prediction model.

4. The method for regulating electric heating resources of a virtual power plant according to claim 1, characterized in that: The obtaining of electric heating resource adjustment parameters according to the predicted user heating demand and the current heating resource load includes: Obtaining a predicted heating resource load based on the current heating resource load, the current environmental parameters and the current time parameters according to a preset load prediction model; Constructing a multi-objective optimization function according to the predicted heating resource load, the predicted user heating demand, the user comfort range and the time-of-use electricity price ladder; The optimization solution is performed according to the multi-objective optimization function to obtain the electric heating resource adjustment parameters.

5. The method for regulating electric heating resources of a virtual power plant according to claim 3, characterized in that: The multi-objective optimization function is constructed according to the predicted heating resource load, the predicted user heating demand, the user comfort range and the time-of-use electricity price ladder, including: Obtaining an energy consumption function, a comfort function, a user heating demand function, and an electricity expense function according to the predicted heating resource load, the predicted user heating demand, the user comfort range, and the time-of-use electricity price ladder; Obtaining a multi-objective optimization function according to the energy consumption function, the comfort function, the user heating demand function and the electricity expense function; The optimization solution is performed according to the multi-objective optimization function to obtain the electric heating resource adjustment parameters, including: Obtaining comprehensive constraints, solving the multi-objective optimization function according to a preset optimization algorithm and the comprehensive constraints, and obtaining target adjustment parameters; The electric heating resource adjustment parameter is obtained according to the target adjustment parameter.

6. The method for regulating electric heating resources of a virtual power plant according to claim 3, characterized in that: The method of obtaining a predicted heating resource load based on the current heating resource load, the current environmental parameter and the current time parameter according to a preset load prediction model includes: Obtaining a time series based on the preset load prediction model according to the current heating resource load, the current environmental parameters and the current time parameters; Decomposing the time series multiple times to obtain multiple decomposition sequences, and obtaining residual components according to the decomposition sequences; A residual predicted heating resource load is obtained according to each residual component, and a predicted heating resource load is obtained according to a plurality of the residual predicted heating resource loads.

7. The method for regulating electric heating resources of a virtual power plant according to claim 6, characterized in that: The method of obtaining a residual predicted heating resource load according to each residual component and obtaining a predicted heating resource load according to a plurality of the residual predicted heating resource loads includes: Get the atrous causal convolution,filter, expansion factor, and kernel size; Sampling the residual component according to the atrous causal convolution, the filter, the expansion coefficient, and the convolution kernel size to obtain a sampling feature; The sampling features are processed across levels through an identity mapping function to obtain a residual prediction of heating resource load; The predicted heating resource load is obtained by predicting the heating resource load according to the multiple residuals.

8. A virtual power plant electric heating resource adjustment device, characterized in that: The virtual power plant electric heating resource adjustment device comprises: Parameter acquisition module, used to obtain current environment parameters, current time parameters and current user behavior; A heating demand prediction module, used to analyze the current environmental parameters, the current time parameters and the current user behavior to obtain a predicted user heating demand; An adjustment module, used for obtaining electric heating resource adjustment parameters according to the predicted user heating demand and the current heating resource load; The adjustment module is also used to adjust the power consumption of the current electric heating equipment according to the electric heating resource adjustment parameters to achieve the adjustment of the electric heating resources.

9. A virtual power plant electric heating resource adjustment device, characterized in that: The device includes: a memory, a processor, and a virtual power plant electric heating resource adjustment program stored in the memory and executable on the processor, wherein the virtual power plant electric heating resource adjustment program is configured to implement the virtual power plant electric heating resource adjustment method as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a virtual power plant electric heating resource adjustment program, and when the virtual power plant electric heating resource adjustment program is executed by the processor, the virtual power plant electric heating resource adjustment method as described in any one of claims 1 to 7 is implemented.

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