A Power Trading Trend Prediction and Decision Support System and Method

Abstract

The present invention discloses a power trading trend prediction and decision support system and method, which relates to the technical field of power trading decision-making. A spatio-temporal data set in the power system is obtained through a data acquisition module; a spatio-temporal adaptive causal network module is used to model these data through a causal inference algorithm, and the edge weights in the causal relationship graph are dynamically adjusted; based on the constructed causal graph, the power trading trend prediction and decision support module combines a spatio-temporal convolutional neural network for multi-level prediction, provides the change trends of power load and price, and generates corresponding power trading strategies; the prediction results are transmitted back to the causal network through a feedback and optimization module to further optimize the causal graph and the spatio-temporal modeling process; through an iterative optimization module, the accuracy of prediction and decision support is gradually improved by using multiple feedback closed-loop optimization processes. The present invention can effectively improve the decision-making quality of power market trading and provide more accurate and timely decision-making basis for power system managers.

Description

Technical Field

[0001] The present invention relates to the technical field of power trading decision-making, and particularly relates to a power trading trend prediction and decision support system and method. Background Art

[0002] The dynamic complexity and variability of the power market make the prediction and decision-making of power trading a challenging task. Existing technologies usually rely on historical data and traditional modeling methods to predict power load, price changes and their trends. However, with the continuous development of power systems and market environments, these traditional methods often struggle to handle the complexity of spatio-temporal changes, resulting in low prediction accuracy and decision-making efficiency.

[0003] In traditional power trading prediction methods, most models only rely on static data or simple regression analysis, and fail to fully consider the dynamic changes of spatio-temporal data. In addition, the variability of the power market requires in-depth understanding and modeling of the causal relationships between different variables. Although some machine learning-based algorithms, such as deep neural networks and regression models, can make a certain degree of prediction for power trading, they often ignore the influence of spatio-temporal dimensions and the complex causal relationships between variables.

[0004] Therefore, this application proposes a power trading trend prediction and decision support system and method. By constructing a spatio-temporal adaptive causal network and combining multi-stage feedback closed-loop optimization, the causal relationship map is adjusted in real time to improve the prediction accuracy and decision-making efficiency of the power market. Summary of the Invention

[0005] The purpose of the present invention is to solve the problems that existing methods often rely on static data and simple linear regression models, cannot accurately capture the dynamic changes of power load and price, resulting in large deviations in prediction results, and existing systems cannot update model parameters and decision-making strategies in real time when facing rapid fluctuations in the power market, resulting in lagging decision support and inability to respond to market changes in a timely manner. A power trading trend prediction and decision support system and method are proposed.

[0006] To achieve the above purpose, the present invention adopts the following technical solutions:

[0007] A power trading trend prediction and decision support system, comprising:

[0008] A data acquisition module: used to obtain spatio-temporal data sets in the power system, and the spatio-temporal data sets include multiple power load data, power price data, equipment status data and sensor data;

[0009] Space-time Adaptive Causal Network Module: It is used to model the variables and their mutual influence relationships in the space-time dataset through a causal inference algorithm. The causal inference algorithm constructs causal relationships based on historical data and dynamically adjusts the weights of the edges to reflect changes in causal relationships.

[0010] Power Trading Trend Prediction and Decision Support Module: It is used to generate multi-level prediction and decision support for the power system based on the joint modeling results of the space-time adaptive causal network and the space-time convolutional neural network.

[0011] Feedback and Optimization Module: It is used to take the power trading trend prediction and equipment scheduling decision as feedback and input them into the space-time adaptive causal network graph, and dynamically adjust and optimize the space-time causal graph.

[0012] Iterative Optimization Module: It is used to optimize the power trading trend prediction and equipment scheduling decision through multiple iterations and feedback closed-loop.

[0013] A power trading trend prediction and decision support method using the above power trading trend prediction and decision support system, including:

[0014] S1: Obtain the space-time dataset in the power system. The space-time dataset includes multiple power load data, power price data, equipment status data, and sensor data. Each data item in the space-time dataset has a timestamp and a spatial identifier, which are used to characterize the state changes of the power system at multiple time points and geographical locations.

[0015] S2: Based on the historical data in the space-time dataset, use the causal inference algorithm to determine the causal relationships between various variables in the power system, combine the space-time convolutional neural network to model the dynamic changes in the spatial dimension and time dimension, and weight the space-time data through the attention mechanism, so as to dynamically adjust the weights of the edges in the causal graph according to the feedback information, generate a causal relationship graph, and form a space-time adaptive causal network.

[0016] S3: Use the causal graph generated by the space-time adaptive causal network and the extracted space-time features to perform trend prediction through a neural network; according to the output of the space-time data model, predict the future changes in power load and price, and provide support for the trading strategies in the power market; combine the predicted power trading trend, and use the decision-making module of the space-time adaptive causal network to generate corresponding power trading strategies.

[0017] S4: Input the power trading trend prediction and decision support generated in step S3 as feedback information into the spatio-temporal adaptive causal network to dynamically optimize the causal graph and spatio-temporal modeling process, including: taking the power trading trend prediction results and decision support strategies as new inputs and transmitting them into the spatio-temporal adaptive causal network graph, and analyzing their impacts on other power system variables through the causal reasoning module; based on the feedback information, dynamically adjust the causal graph in the spatio-temporal adaptive causal network, update the edge weights and structures of the causal relationships, and ensure that the causal relationships can be adaptively adjusted with the evolution of time and the changes in the system state;

[0018] S5: Multi-stage feedback closed-loop optimization prediction and decision-making: Based on multiple feedbacks and optimizations, improve the accuracy of power trading trend prediction and decision support, including: taking the causal graph described in step S2 as the first causal relationship graph, and constructing the second causal relationship graph based on the feedback information described in step S4. After each feedback, the spatio-temporal adaptive causal network updates the causal graph by combining the spatio-temporal convolutional neural network and the adaptive attention mechanism, and retrains the prediction model. Compare the first causal relationship graph with the second causal relationship graph to identify the differences in the causal relationships between the two, and adjust the weights of the corresponding edges in the causal graph based on the items that meet the predetermined statistical criteria in the differences; the feedback information is used to drive the causal reasoning module to dynamically adjust the edge weights and structures of the causal graph in the spatio-temporal adaptive causal network to ensure that the causal relationships can be adaptively adjusted with the changes in the power system state, optimize the modeling process of spatio-temporal data through the spatio-temporal convolutional neural network and the adaptive attention mechanism, thereby improving the prediction accuracy; after each feedback, adjust the causal graph by analyzing the feedback data, and retrain the prediction model in the spatio-temporal adaptive causal network. Through multiple feedback iterations, optimize the power trading trend prediction and decision support strategies until the predetermined prediction accuracy and decision-making efficiency goals are achieved.

[0019] Preferably, step S2 includes: using the Granger causality test method to determine the causal relationship between power load and power price:

[0020] Use the AIC criterion to select the delay number to select the time delay p, and the selected delay is 1;

[0021] The Granger test uses a regression model: test whether the past values of Y(t) can significantly predict the future values of X(t):

[0022]

[0023]

[0024] where p and q are the lag orders, , , is the regression coefficient. If is significantly non - zero, it indicates that there is a Granger causal relationship between X(t) and Y(t);

[0025] If the Granger test shows that the electricity load has a significant impact on the price, then in the causal graph, add a directed edge of "electricity load → electricity price";

[0026] Extract spatial features through 3×3 spatial convolution, and then extract features in the time dimension through 1D convolution. After each convolution operation, generate corresponding spatio - temporal feature maps;

[0027] Apply the adaptive attention mechanism to calculate the attention weights based on spatial and temporal features:

[0028]

[0029] Among them, is the weight score at time t and space s, is the normalized attention value;

[0030] Combine the spatio - temporal convolution features with the attention weights to generate the final spatio - temporal feature map for subsequent decision - making support.

[0031] Preferably, the step S2 further includes: based on each timestamp and spatial identifier in the multi - source spatio - temporal dataset, use the inverse information entropy algorithm for causal inference to identify potential causal relationships between variables in the power system; assign preliminary weights to each edge in the causal graph, and the weights represent the strength of the causal relationship between nodes; according to the changes in spatio - temporal data, dynamically adjust the causal graph through a feedback mechanism, so that the graph can adapt to the changing state in the power system and improve the prediction accuracy of power trading trends.

[0032] Preferably, the spatio - temporal convolutional neural network includes: a spatial convolutional layer for extracting spatial features of device and sensor data in the power system and capturing the spatial dependence between devices; a temporal convolutional layer for extracting time - series features of electricity load and electricity price variables and capturing the changing trends of various variables in the power system over time; a spatio - temporal fusion layer for fusing the features extracted by the spatial convolutional layer and the temporal convolutional layer to obtain a more comprehensive spatio - temporal feature representation for power trading trend prediction.

[0033] Preferably, the step S4 specifically includes: taking the power trading trend prediction result and the decision - making support strategy as new inputs and passing them into the spatio - temporal adaptive causal network graph, analyzing their impacts on other power system variables through the causal inference module; based on the feedback information, dynamically adjust the causal graph in the spatio - temporal adaptive causal network, and update the edge weights and structure of the causal relationship.

[0034] Preferably, step S5 includes: in the first stage, based on the output of the initial spatio-temporal causal graph and the spatio-temporal convolutional neural network, generate a power trading trend prediction, and generate decision support information according to the prediction result; in the second stage, use the power trading trend prediction result of the first stage as feedback and input it into the spatio-temporal causal graph to update the strength and structure of the causal relationship to enhance the prediction accuracy of the next step; in the third stage, optimize the prediction ability of the spatio-temporal convolutional neural network according to the feedback result, and further adjust the power trading trend prediction result to form an adaptive optimization model to provide decision support.

[0035] Preferably, the dynamic adjustment process of the spatio-temporal adaptive causal network graph includes the following steps: according to the feedback information after the prediction period of the power system, adjust the causal relationship and edge weight in the graph in real time, and the feedback information includes the actual transaction data of the power market, load fluctuation conditions and other relevant events, and the feedback information is used to optimize the edge weight and causal relationship strength in the causal graph; in the feedback adjustment process, the spatio-temporal adaptive causal network dynamically adjusts the structure of the causal graph by analyzing the evolution of spatio-temporal data. With the iteration of each prediction period, the spatio-temporal adaptive causal network optimizes the causal graph using the prediction result and feedback information of the previous stage in each stage, so as to achieve higher prediction accuracy when the power system faces complex dynamic changes.

[0036] Preferably, step S3 includes the following steps:

[0037] Input the cleaned power load data, price data and equipment status information into the spatio-temporal convolutional neural network for feature extraction; input the extracted spatio-temporal features into the long short-term memory network, and predict the change trend of the load in the next 7 days by analyzing the power load data of the past 30 days: Among them, is the hidden state at the current moment, is the input spatio-temporal feature, and LSTM will predict the future load change according to the past data; according to the output of LSTM, generate a prediction trend graph of future power load and price;

[0038] Based on the prediction result, generate a power trading strategy using the reinforcement learning strategy; when it is predicted that the future power price will rise significantly, the model will recommend buying power at the current lower price and selling it when the future price is high to maximize the profit.

[0039] Preferably, the spatio-temporal convolutional neural network further includes: a skip connection layer, which is used to avoid the problem of gradient disappearance during the learning process of spatio-temporal data, enabling the network to effectively learn long-term dependencies and helping to capture long-term market volatility patterns in power trading trend prediction; a pooling layer, which is used to reduce the dimension of spatio-temporal features, retain important information and improve computational efficiency, thereby accelerating the processing speed of power trading trend prediction.

[0040] The present invention has the following beneficial effects:

[0041] 1. In the present invention, by combining the spatio-temporal adaptive causal network with the spatio-temporal convolutional neural network and the causal inference algorithm, the accuracy of power trading trend prediction can be improved, the causal relationship graph can be adjusted in real-time and adaptively, the causal relationships between variables in the power system can be deeply mined, and through the multi-stage feedback closed-loop optimization mechanism, continuous learning and self-optimization can be carried out, thereby enhancing the stability and reliability of the system.

[0042] 2. The present invention can optimize power trading decisions, improve the operating efficiency of the power system, enhance market response capabilities, support sustainable development, and help power market participants formulate more accurate trading strategies, improve economic benefits, and reduce market risks through accurate prediction and real-time adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is the system architecture and method flow chart of a power trading trend prediction and decision support system and method proposed by the present invention;

[0044] Figure 2 is the multi-stage feedback closed-loop optimization process diagram of the spatio-temporal adaptive causal network in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0046] As Figure 1 - Figure 2 shown, a power trading trend prediction and decision support system proposed by the present invention includes:

[0047] A data acquisition module: used to obtain spatio-temporal data sets in the power system, and the spatio-temporal data sets include multiple power load data, power price data, equipment status data, and sensor data;

[0048] Space-time Adaptive Causal Network Module: It is used to model the variables and their mutual influence relationships in the space-time dataset through a causal inference algorithm. The causal inference algorithm constructs causal relationships based on historical data and dynamically adjusts the weights of the edges to reflect changes in causal relationships.

[0049] Power Trading Trend Prediction and Decision Support Module: It is used to generate multi-level prediction and decision support for the power system based on the joint modeling results of the space-time adaptive causal network and the space-time convolutional neural network;

[0050] Feedback and Optimization Module: It is used to take the power trading trend prediction and equipment scheduling decision as feedback and input them into the space-time adaptive causal network graph, and dynamically adjust and optimize the space-time causal graph;

[0051] Iterative Optimization Module: It is used to optimize the power trading trend prediction and equipment scheduling decision through multiple iterations and feedback closed loops.

[0052] A power trading trend prediction and decision support method, using the above-mentioned power trading trend prediction and decision support system, includes:

[0053] S1: Obtain the space-time dataset in the power system. The space-time dataset includes multiple power load data, power price data, equipment status data, and sensor data. Each data item in the space-time dataset has a timestamp and a spatial identifier, which are used to characterize the state changes of the power system at multiple time points and geographical locations;

[0054] S2: Based on the historical data in the space-time dataset, use the causal inference algorithm to determine the causal relationships between various variables in the power system, combine the space-time convolutional neural network to model the dynamic changes in the spatial dimension and the time dimension, and weight the space-time data through the attention mechanism, so as to dynamically adjust the weights of the edges in the causal graph according to the feedback information, generate the causal relationship graph, and form a space-time adaptive causal network;

[0055] S3: Use the causal graph generated by the space-time adaptive causal network and the extracted space-time features to conduct trend prediction through a neural network; According to the output of the space-time data model, predict the future changes in power load and price, and provide support for the trading strategies in the power market; Combine the predicted power trading trend, and use the decision-making module of the space-time adaptive causal network to generate corresponding power trading strategies;

[0056] S4: Input the power trading trend prediction and decision support generated in step S3 into the spatiotemporal adaptive causal network as feedback information to dynamically optimize the causal graph and spatiotemporal modeling process, including: passing the power trading trend prediction results and decision support strategies as new inputs to the spatiotemporal adaptive causal network graph, and analyzing their impact on other power system variables through the causal reasoning module; based on the feedback information, dynamically adjust the causal graph in the spatiotemporal adaptive causal network, update the edge weights and structure of the causal relationship, and ensure that the causal relationship can be adaptively adjusted with time evolution and system state changes;

[0057] S5: Multi-stage feedback closed-loop optimization prediction and decision-making: Based on multiple feedback and optimization, the accuracy of power trading trend prediction and decision support is improved, including: using the causal graph described in step S2 as the first causal relationship graph, and constructing a second causal relationship graph based on the feedback information described in step S4. After each feedback, the spatiotemporal adaptive causal network updates the causal graph by combining the spatiotemporal convolutional neural network with the adaptive attention mechanism, and retrains the prediction model, compares the first causal relationship graph with the second causal relationship graph, determines whether the causal relationship therein has changed significantly, and adjusts the edges in the causal graph based on the change. Weight; the feedback information is used to drive the causal reasoning module to dynamically adjust the edge weights and structure of the causal graph in the spatiotemporal adaptive causal network to ensure that the causal relationship can be adaptively adjusted as the state of the power system changes, and optimize the modeling process of spatiotemporal data through the spatiotemporal convolutional neural network and the adaptive attention mechanism, thereby improving the prediction accuracy; after each feedback, the causal graph is adjusted by analyzing the feedback data, and the prediction model in the spatiotemporal adaptive causal network is retrained. Through multiple feedback iterations, the optimization of power trading trend prediction and decision support strategy is achieved until the predetermined prediction accuracy and decision efficiency goals are achieved.

[0058] In one embodiment, step S1: obtaining a spatiotemporal data set in the power system: first, it is necessary to obtain a spatiotemporal data set related to prediction and decision-making in the power system. The acquisition of the spatiotemporal data set mainly includes two sources: real-time collected data and historical data.

[0059] Real-time data collection: Use sensors, smart devices and communication networks in the power system to collect power load data, power price data, equipment status data and sensor monitoring data in real time. Each data item contains a timestamp and a spatial identifier to describe the time and location of its acquisition. Specifically, power load data is collected through power meters, power price data is obtained through the power market system, equipment status data is provided by the control center or the device itself, and sensor data (such as temperature, current, voltage, etc.) is collected by sensors at different locations.

[0060] Historical data: Obtain the historical operation data of the power system, including power load, price, equipment status, etc. over a past period of time. These data are usually provided by the power dispatching center or the historical database and are obtained through query interfaces. The historical data provides the basis for the training and model construction of the spatio-temporal dataset.

[0061] The obtained spatio-temporal dataset contains data changes of multiple variables (power load, power price, equipment status, sensor data, etc.) at different time points and spatial locations. And data processing is carried out. Specifically, the spatio-temporal dataset includes the following key parts:

[0062] Power load data: Record the power load information of different regions (such as each load point or area), including real-time load and historical load data.

[0063] Power price data: Include the power trading prices at multiple time points and different regions in the power market. These price data reflect the influence of factors such as supply and demand relationship and market regulation mechanism in the power market on price fluctuations.

[0064] Equipment status data: Involve the operation status information of each device (such as transformers, switches, lines, etc.) in the power system. The equipment status data is mainly obtained through the SCADA (Supervisory Control and Data Acquisition) system or intelligent sensors, identifying the working status, fault information, maintenance status, etc. of power equipment.

[0065] Sensor data: The sensors installed in the power system (such as current sensors, voltage sensors, temperature sensors, etc.) are used to monitor the parameter changes of power equipment and the environment in real time. These data can reflect the working status of power equipment and environmental changes, and assist in diagnosing and predicting the operation status of the power system.

[0066] Each piece of data includes a timestamp (the time point when the data is recorded) and a spatial identifier (identifying the data collection location). For example, the power load data may include the timestamp "2024-01-01 10:00:00" and the spatial identifier "Region A", so that the specific data at each time point and each location can be clearly recorded.

[0067] In one embodiment, step S2: Construct a spatio-temporal adaptive causal network graph: Construct a preliminary causal relationship graph through a causal reasoning algorithm based on historical data. Identify the mutual influence relationships between variables in the power system through causal reasoning methods. The specific implementation steps are as follows:

[0068] Use the spatio-temporal dataset obtained from step S1 (including power load, power price, equipment status, and sensor data, etc.) as input data. This dataset contains multi-dimensional data at multiple time periods and spatial locations.

[0069] Select appropriate causal inference algorithms, such as the inverse information entropy algorithm and the Granger causality test, to model the causal relationships between variables in the power system. By analyzing the historical relationships in spatio-temporal data, determine which variables have significant causal dependencies.

[0070] Inverse information entropy method: Determine the causal relationship between variables by calculating the change in information entropy. Information entropy reflects the degree of disorder of the system. If the change of one variable leads to the change of the uncertainty of another variable, it is considered that there is a causal relationship between the two.

[0071] Granger causality test: Test the causal relationship between variables through the lag terms of time series data. If the past values of one variable can significantly predict the future values of another variable, it is considered that there is a causal relationship: test whether the past values of Y(t) can significantly predict the future values of X(t):

[0072]

[0073]

[0074] where p and q are the lag orders, , , are the regression coefficients. If is significantly non-zero, it indicates that there is a Granger causal relationship between X(t) and Y(t).

[0075] Causal relationship graph generation: Through the above causal inference methods, generate a preliminary causal graph, which shows the causal relationships between variables and their preliminary weight values. The edges of the graph represent the causal relationships between variables, and the nodes represent the variables themselves. The weight value of each edge reflects the strength and direction of the causal relationship.

[0076] After the preliminary causal graph is generated, next, by combining the spatio-temporal convolutional neural network (ST-CNN) and the attention mechanism, adjust the weights of the edges in the causal graph according to the dynamic changes of spatio-temporal data, so as to form a spatio-temporal adaptive causal network (STACN). The specific implementation process is as follows:

[0077] Model the spatial and temporal dimensions using a Spatio-Temporal Convolutional Neural Network (ST-CNN). Through convolutional operations, ST-CNN can capture local and global dependencies from spatio-temporal data. ST-CNN extracts temporal change patterns by applying convolutional kernels to time series data. Learn the correlations between different time points, especially in cases where load, price, and device status change over time. Through spatial convolutional operations, ST-CNN can learn dependencies between various spatial locations in the power system and identify associations between different regions or devices.

[0078] Use an attention mechanism to weight the spatio-temporal data. The attention mechanism can dynamically adjust weights according to the importance of the data, highlighting key spatio-temporal features and weakening unimportant parts. The specific implementation includes:

[0079] For each time step of the time series data, the attention mechanism dynamically weights the influence of different time points according to the historical performance of the data. By learning the weight coefficients, the model can pay more attention to those time periods with significant changes over time. The attention mechanism is used to weight the data at different spatial locations, so that the model pays more attention to those spatial regions that contribute more to the change of the system state.

[0080] Based on spatio-temporal convolution and the attention mechanism, the model dynamically adjusts the weights of the edges in the causal graph. Specifically, through real-time feedback and evolution of spatio-temporal data, the causal inference model can update the weights of each edge in the graph according to the current spatio-temporal state, forming an adaptive causal network. This dynamic adjustment can reflect the changes in causal relationships in the power system in real time.

[0081] By continuously obtaining new spatio-temporal data inputs, the model adjusts the causal graph according to the latest feedback information. The feedback information includes the change trend of power load, the change of device status, the change of external environment, etc. These information are input into the causal network to adjust the weights of each edge in the network to reflect the change of causal relationship. The generated Spatio-Temporal Adaptive Causal Network (STACN) contains the causal relationships between various variables in the power system and adaptively adjusts the weights of the edges as the spatio-temporal data changes dynamically. This enables the STACN graph to accurately reflect the real-time state of the power system and provide support for subsequent prediction and decision-making:

[0082]

[0083] Where:

[0084] is the weight of the previous update.

[0085] is the learning rate, which controls the update speed.

[0086] is the change in causal impact calculated based on new data.

[0087] The generated Spatio-Temporal Adaptive Causal Network (STACN) is used for the following aspects:

[0088] Electric load forecasting: Utilize the causal relationships in the STACN graph to predict the electric loads of different regions and devices in the future for a period of time.

[0089] Equipment fault diagnosis: Based on the causal relationships in the STACN graph, detect abnormal changes in the operating state of the equipment and timely discover potential fault risks.

[0090] Power market decision support: By analyzing the causal relationships between price changes and load fluctuations in the STACN graph, provide support for price forecasting and decision-making in the power market.

[0091] In one embodiment, step S3: Generate power trading trend prediction and decision support based on the STACN output: According to the outputs of the Spatio-Temporal Adaptive Causal Network (STACN) and the Spatio-Temporal Convolutional Neural Network (ST-CNN), generate a power trading trend prediction, and generate an optimized decision support strategy according to the prediction result. The specific implementation is as follows:

[0092] Obtain the output of the spatio-temporal data model processed by the Spatio-Temporal Convolutional Neural Network (ST-CNN) and the adaptive attention mechanism from step S3. This data includes spatio-temporal features of variables such as electric load, price, and equipment status; the spatio-temporal features include, but are not limited to: power system operation data in different time dimensions (such as hours, days, months, etc.) and space dimensions (such as each region, each subsystem, etc.).

[0093] Spatio-temporal data modeling and causal relationship reasoning: The causal reasoning module of STACN analyzes the causal relationships between key variables such as electric load and price based on the spatio-temporal data model generated in step S3, and constructs a causal graph between variables in the power system.

[0094] According to the causal graph, analyze the mutual influence between different factors in the future period, and obtain the evolution trend of the power system state; the spatio-temporal data at the current moment , the output of STACN is the spatio-temporal prediction data at the future moment :

[0095]

[0096] Among them, G is the causal relationship graph.

[0097] Trend prediction model: A deep neural network (such as Long Short-Term Memory Network LSTM or other time series prediction models) is used to predict the spatio-temporal data of the power system. This model utilizes the results of causal reasoning and spatio-temporal features to predict key variables such as future power load and price.

[0098] The prediction results include the change trends of key variables such as power load and price in the future time period. Specifically, it includes:

[0099] The change trend of power demand (such as peak load, valley load, etc.).

[0100] The predicted fluctuations of power price (such as the upward or downward trend of market price).

[0101] The evolution of the state of power equipment (such as equipment overload, equipment response caused by load changes, etc.).

[0102] Input of the decision-making model: Through the prediction of power trading trends, trend data of the future power market is obtained, including load demand prediction, price fluctuation trend, etc. These prediction results are used as inputs and provided to the decision support module to support the optimization of power trading decisions based on the Spatio-Temporal Causal Network (STACN). In this module, specific power trading strategies are generated based on the predicted trend data and the causal reasoning output of STACN. The strategies include:

[0103] Strategy for selecting power trading time periods: According to load prediction and price prediction, determine the best timing for power trading in specific time periods.

[0104] Power purchase and sales decisions: Based on the market price trend, optimize the decisions of purchasing power or selling power to reduce costs or maximize benefits.

[0105] Demand response management: According to load prediction, formulate demand response strategies for the power system to ensure that the system load is within a controllable range and avoid overload or power shortage situations.

[0106] Optimizing the operation of the power system: The generated power trading strategies will optimize the operation state of the power system. For example, by optimizing power load distribution, scheduling transactions, etc., ensure that the power system reaches the optimal operation state in different time periods.

[0107] Output optimized power trading decisions, specifically including:

[0108] Trading suggestions for each time period: For example, when to purchase power and when to sell power.

[0109] Specific strategies for load management: including power scheduling, demand response, etc.

[0110] Buying and selling decisions made based on power market price prediction.

[0111] Decision support generation is based on the prediction results and combines STACN causal reasoning analysis to generate specific power trading decision strategies. By dynamically adjusting power trading timing, load distribution, and market price management strategies, it ensures that the power system can still achieve optimal economic benefits and operating effects under various uncertainties.

[0112] In one embodiment, step S4: Optimize the power trading trend prediction and decision support as feedback input to STACN. Take the power trading trend prediction and decision support results generated in S3 as feedback input and transmit them into the spatio-temporal adaptive causal network (STACN). Then, through the dynamic adjustment of causal reasoning and spatio-temporal modeling, optimize the prediction results and decision support strategies. The specific implementation is as follows:

[0113] Take the power trading trend prediction data and decision support strategies generated in step S3 as new input data and transmit them to the causal reasoning module in the spatio-temporal adaptive causal network (STACN). These input data are fused with the previous spatio-temporal data as feedback information and transmitted into the network through the input layer of STACN. The feedback information is organized in a time series and contains historical and predicted data of multiple key factors such as power load, market price, and equipment status. It also includes relevant decision results generated by the decision support strategy.

[0114] Causal reasoning analysis: The causal reasoning module in STACN determines the causal relationships between variables by analyzing the relationship between feedback information and the existing spatio-temporal data model. For example, the possible influence between the predicted power load and power price, and the potential impact of load fluctuations on equipment status. Adopt technologies such as graph neural network (GNN) to infer the causal relationships in different time periods through the known causal graph and identify the influence paths of each variable in the power system.

[0115] According to the new prediction data and decision results in the feedback information, the causal reasoning module dynamically adjusts the causal graph in STACN. Specifically, it includes: Edge weight update: Update the edge weights of the causal relationships according to the feedback prediction results and adjust the relative strength between the influencing variables. For example, when the accuracy of load prediction improves, the system will automatically increase the weight of the causal relationship related to load.

[0116] Self-adaptability of the causal graph: The adaptive adjustment process of the causal graph is based on the evolutionary mechanism of time and space and can be adjusted in real time according to the changes in the power system and feedback information. For example, when there is a large error in power load prediction, STACN will adaptively adjust the causal graph structure to reduce errors in future predictions.

[0117] By taking the prediction of power trading trends and decision support as feedback information and inputting it into STACN, the system can dynamically adjust the structure and edge weights of the causal graph according to the actual feedback, thereby adaptively optimizing the spatio-temporal data modeling process. This improves the operating efficiency and decision-making accuracy of the power system.

[0118] Step S4 realizes the in-depth mining and adjustment of the causal relationships between variables in the power system through the combination of causal reasoning and spatio-temporal adaptive mechanisms. By adaptively optimizing the causal graph structure, the system can continuously optimize the prediction results based on feedback information and improve the accuracy of decision support. Each feedback will lead to the optimization of the causal graph and the prediction model.

[0119] In one embodiment, step S5: multi-stage feedback closed-loop optimization of prediction and decision-making: Through a multi-stage feedback closed-loop mechanism, continuously optimize the accuracy and effectiveness of power trading trend prediction and decision support. Using the spatio-temporal convolutional neural network (ST-CNN) and adaptive attention mechanism in the spatio-temporal adaptive causal network (STACN), combined with the prediction results and decision support strategies, conduct repeated optimization and iteration. The specific implementation is as follows:

[0120] Obtain feedback information and construct the second causal graph: After each prediction and decision-making, collect feedback information on actual operation data from relevant fields such as the power trading market and power load dispatching, including but not limited to: actual power trading results, power demand fluctuations, power equipment status, efficiency of system emergency response, etc.; Based on this feedback information, construct the second causal graph. By analyzing the new spatio-temporal data and feedback information, capture the new causal relationships between variables in the power system and perform preliminary updates.

[0121] In step S2, the causal graph generated by combining the causal reasoning algorithm and the spatio-temporal convolutional neural network (ST-CNN) with historical spatio-temporal data is used as the first causal graph, which represents the preliminary causal relationships and dynamic changes between various variables in the power system. Based on the first causal graph, the system will gradually optimize the structure and edge weights of the causal graph based on feedback information (from the actual operation data of the power market).

[0122] Compare the significance of the first causal graph and the second causal graph: Use statistical methods (such as t-tests, hypothesis tests, etc.) to compare the significance between the first causal graph and the second causal graph. Through significance analysis, identify which causal relationships become more important at different times or market states, thereby providing a basis for model optimization. For example, during a certain period, the relationship between power load and power price is more significant, and the weight of this relationship is adjusted preferentially.

[0123] The generated second causal relationship graph is input into the Spatiotemporal Adaptive Causal Network (STACN) as feedback information and passed to the module as new input data. The causal reasoning module will update and reason about causal relationships based on the latest feedback data and historical data. At this time, the causal reasoning module in STACN can automatically adjust the edge structure and edge weights of the graph according to the feedback information, further improving the causal relationship model of the system.

[0124] According to the new data in the feedback information, the causal reasoning module in STACN will adaptively adjust the structure and edge weights of the causal graph. The specific adjustments include: dynamically adjusting the relationship weights between variables in the prediction model according to the actual market conditions and changes in electricity demand. For example, if the electricity load changes significantly during a certain period, this module will enhance the causal relationship between the load and the equipment status, thereby optimizing the prediction accuracy. Automatically adjusting the causal graph structure based on the pattern changes in the feedback information, deleting irrelevant relationships or adding new associations to ensure that the causal graph always remains consistent with the actual changes in the electricity market and load.

[0125] The updated causal relationship graph will be input into the Spatiotemporal Convolutional Neural Network (ST-CNN) to perform spatiotemporal data modeling in combination with the adaptive attention mechanism, further optimizing the prediction accuracy of variables such as electricity load and price. ST-CNN can handle the complexity of spatiotemporal data, extract features in the spatial and temporal dimensions through spatiotemporal convolution, and strengthen the model's learning of important spatiotemporal features.

[0126] After each feedback, STACN will retrain the prediction model based on the updated causal graph and the output of the spatiotemporal convolutional neural network to further improve the prediction accuracy. The results after each iteration will have an optimizing impact on the prediction results of electricity trading trends and decision-making support strategies. Specifically, it includes: regenerating the prediction of electricity trading trends according to the latest feedback data and optimized model. Generating a more accurate electricity trading decision-making strategy according to the optimized prediction results, improving the timeliness and accuracy of decision-making.

[0127] Each round of feedback data and optimization results will form a new closed loop, continuously promoting the improvement of the prediction model and decision-making support strategy. Specifically reflected in:

[0128] The accuracy of electricity trading trend prediction has been greatly improved: reducing prediction bias and enhancing the model's adaptability to market changes.

[0129] The electricity trading decision-making strategy is more accurate and reasonable: improving the timing grasp and resource allocation efficiency of trading, and optimizing the formulation of electricity trading strategies.

[0130] The closed-loop mechanism continues until the accuracy of power trading trend prediction and decision support reaches the set expected standard. As the market and system states change, the system can adaptively adjust the models and strategies to adapt to the dynamic changes in the power market, ensuring long-term stable and efficient operation.

[0131] Through this multi-stage feedback closed-loop optimization process, the overall performance of the system will be significantly improved, ensuring that power trading trend prediction and decision support can achieve the best results in different market environments.

[0132] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for predicting and supporting power trading trends, characterized in that: include: S1: Acquire a spatiotemporal data set in a power system, wherein the spatiotemporal data set includes a plurality of power load data, power price data, equipment status data and sensor data, wherein each data item in the spatiotemporal data set has a timestamp and a spatial identifier; S2: Based on the historical data in the spatiotemporal dataset, a causal inference algorithm is used to determine the causal relationship between various variables in the power system. The spatiotemporal convolutional neural network is combined to model the dynamic changes in the spatial and temporal dimensions, and the spatiotemporal data is weighted through the attention mechanism. The weights of the edges in the causal graph are dynamically adjusted according to the feedback information, a causal relationship graph is generated, and a spatiotemporal adaptive causal network is formed. S3: Using the causal graph generated by the spatiotemporal adaptive causal network and the extracted spatiotemporal features, trend prediction is performed through neural networks; Based on the output of the spatiotemporal data model, the future trend of power load and price changes is predicted, and support is provided for the trading strategy of the power market; combined with the predicted power trading trend, the corresponding power trading strategy is generated using the spatiotemporal adaptive causal network; S4: input the power trading trend prediction and decision support generated in step S3 as feedback information into the spatiotemporal adaptive causal network to dynamically optimize the causal graph and spatiotemporal modeling process; S5: Multi-stage feedback closed-loop optimization prediction and decision-making: Based on multiple feedback and optimization, the accuracy of power trading trend prediction and decision support is improved, including: using the causal map described in step S2 as the first causal relationship map, and constructing a second causal relationship map based on the feedback information described in step S4. After each feedback, the spatiotemporal adaptive causal network updates the causal map by combining the spatiotemporal convolutional neural network with the adaptive attention mechanism, and retrains the prediction model to compare the first causal relationship map with the second causal relationship map to identify the difference in causal relationship between the two, and adjust the causal map based on the items in the difference that meet the predetermined statistical standards. The feedback information is used to drive the causal reasoning module to dynamically adjust the edge weights and structure of the causal graph in the spatiotemporal adaptive causal network to ensure that the causal relationship can be adaptively adjusted as the state of the power system changes, and optimize the modeling process of spatiotemporal data through the spatiotemporal convolutional neural network and the adaptive attention mechanism, so as to improve the prediction accuracy; after each feedback, the causal graph is adjusted by analyzing the feedback data, and the prediction model in the spatiotemporal adaptive causal network is retrained. Through multiple feedback iterations, the optimization of power trading trend prediction and decision support strategy is realized until the predetermined prediction accuracy and decision efficiency goals are achieved.

2. A method for predicting and supporting power trading trends according to claim 1, characterized in that: The step S2 includes: using the Granger causality test method to determine the causal relationship between power load and power price: Use the AIC criterion to select the number of delays. Select the time delay p, and the selected delay is 1; The Granger test uses a regression model to test whether the past values ​​of Y(t) can significantly predict the future values ​​of X(t): ; Where p and q are the lag orders, , , is the regression coefficient, if If it is significantly different from zero, it indicates that there is a Granger causal relationship between X(t) and Y(t); If the Granger test shows that power load has a significant impact on price, then add a directed edge of "power load → power price" to the causal graph; The spatial features are extracted through 3×3 spatial convolution, and then the features of the time dimension are extracted through 1D convolution. After each convolution operation, the corresponding spatiotemporal feature map is generated; Apply an adaptive attention mechanism to calculate attention weights based on spatial and temporal features: in, is the weight score of time t and space s, is the normalized attention value; The spatiotemporal convolutional features are combined with the attention weights to generate the final spatiotemporal feature map for subsequent decision support.

3. A method for predicting and supporting power trading trends according to claim 1, characterized in that: The step S2 further includes: based on each timestamp and spatial identifier in the multi-source spatiotemporal data set, using an inverse information entropy algorithm to perform causal reasoning to identify potential causal relationships between variables in the power system; assigning a preliminary weight to each edge in the causal graph, wherein the weight represents the strength of the causal relationship between nodes; and dynamically adjusting the causal graph through a feedback mechanism according to changes in the spatiotemporal data, so that the graph can adapt to the ever-changing state in the power system and improve the prediction accuracy of power trading trends.

4. A method for predicting and supporting power trading trends according to claim 1, characterized in that: The spatiotemporal convolutional neural network includes: a spatial convolution layer, which is used to extract the spatial characteristics of equipment and sensor data in the power system and capture the spatial dependencies between devices; a temporal convolution layer, which is used to extract the time series characteristics of power load and power price variables and capture the changing trends of various variables in the power system over time; and a spatiotemporal fusion layer, which fuses the features extracted by the spatial convolution layer and the temporal convolution layer to obtain a more comprehensive spatiotemporal feature representation for use in power trading trend prediction.

5. The method for predicting and supporting power trading trends according to claim 1, characterized in that: The step S4 specifically includes: taking the power trading trend prediction results and decision support strategies as new inputs, passing them to the spatiotemporal adaptive causal network graph, and analyzing their impact on other power system variables through the causal reasoning module; based on feedback information, dynamically adjusting the causal graph in the spatiotemporal adaptive causal network, and updating the edge weights and structure of the causal relationship.

6. A method for predicting and supporting power trading trends according to claim 1, characterized in that: The step S5 includes: in the first stage, based on the initial spatiotemporal causal graph and the output of the spatiotemporal convolutional neural network, generating a power trading trend forecast, and generating decision support information based on the forecast results; in the second stage, inputting the power trading trend forecast results of the first stage into the spatiotemporal causal graph as feedback, updating the strength and structure of the causal relationship to enhance the prediction accuracy of the next step; in the third stage, optimizing the prediction ability of the spatiotemporal convolutional neural network based on the feedback results, and further adjusting the power trading trend forecast results to form an adaptive optimization model to provide decision support.

7. A method for predicting and supporting power trading trends according to claim 1, characterized in that: The dynamic adjustment process of the spatiotemporal adaptive causal network graph includes the following steps: according to the feedback information of the power system after the prediction cycle, the causal relationship and edge weight in the graph are adjusted in real time, the feedback information includes the actual transaction data of the power market, load fluctuation and other related events, and the feedback information is used to optimize the edge weight and causal relationship strength in the causal graph; in the feedback adjustment process, the spatiotemporal adaptive causal network dynamically adjusts the structure of the causal graph by analyzing the evolution of spatiotemporal data. With the iteration of each prediction cycle, the spatiotemporal adaptive causal network optimizes the causal graph at each stage using the prediction results and feedback information of the previous stage, thereby achieving higher prediction accuracy of the power system when facing complex dynamic changes.

8. The method for predicting and supporting power trading trends according to claim 1, characterized in that: Step S3 includes the following steps: The cleaned power load data, price data and equipment status information are input into the spatiotemporal convolutional neural network for feature extraction. The extracted spatiotemporal features are input into the long short-term memory network to predict the load change trend in the next 7 days by analyzing the power load data of the past 30 days: in, is the hidden state at the current moment, Based on the input spatiotemporal features, LSTM predicts future load changes based on past data; based on the output of LSTM, a forecast trend chart of future power load and price is generated; Based on the prediction results, a reinforcement learning strategy is used to generate an electricity trading strategy; when it is predicted that electricity prices will rise sharply in the future, the model will recommend buying electricity when the current price is low and selling it when the price is high in the future to maximize profits.

9. The method for predicting and supporting power trading trends according to claim 1, characterized in that: The spatiotemporal convolutional neural network further includes: a skip connection layer, which is used to avoid the gradient vanishing problem in the learning process of spatiotemporal data, so that the network can effectively learn long-term dependencies and help capture long-term market fluctuation patterns in power trading trend prediction; a pooling layer, which is used to reduce the dimension of spatiotemporal features, retain important information and improve computing efficiency, thereby speeding up the processing speed of power trading trend prediction.

10. A power trading trend prediction and decision support system, using the power trading trend prediction and decision support method according to any one of claims 1 to 9, characterized in that: include: Data acquisition module: used to obtain spatiotemporal data sets in the power system, which include power load data, power price data, equipment status data and sensor data; Spatiotemporal adaptive causal network module: used to model the variables in the spatiotemporal data set and their mutual influence relationship through a causal reasoning algorithm, wherein the causal reasoning algorithm constructs causal relationships based on historical data and dynamically adjusts the weights of edges to reflect changes in causal relationships; Power trading trend forecasting and decision support module: used to generate multi-level forecasting and decision support for power systems based on the joint modeling results of spatiotemporal adaptive causal networks and spatiotemporal convolutional neural networks; Feedback and optimization module: used to input power trading trend prediction and equipment dispatch decision into the spatiotemporal adaptive causal network map as feedback, and dynamically adjust and optimize the spatiotemporal causal map; Iterative Optimization Module: It is used to optimize power trading trend prediction and equipment scheduling decisions through multiple iterations and feedback loops.

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