CN-121981766-A - Electric power market trading system and method based on multi-mode fusion and causal inference

CN121981766ACN 121981766 ACN121981766 ACN 121981766ACN-121981766-A

Abstract

The invention discloses a system and a method for electric power market transaction based on multi-modal fusion and causal inference, which relate to the technical field of electric power market transaction and comprise the steps of automatically acquiring multi-source heterogeneous data related to an electric power market; establishing a unified data warehouse and providing a unified data base, quantifying the change degree and identifying the change type, generating a power price prediction change result, generating early warning information based on the power price prediction change result and pushing the early warning information to a transactor, generating transaction strategy suggestions through resistance reinforcement learning, a multi-time scale element learning mechanism and risk assessment, and providing a human-computer interaction interface and a data visualization function. The invention solves the technical problems of insufficient integration of multi-source heterogeneous data, limited electricity price prediction precision and lack of effective risk early warning and management and control mechanisms in the prior art, achieves the technical effects of realizing multi-source data deep fusion and electricity price accurate prediction, and improves transaction decision accuracy and risk management and control capability.

Inventors

LIU JIANG
YAO MIN
LIU JIAZHEN
ZHANG JIE
GUO YING
CAO BO
LEI YANRONG
SONG YIN
WANG HUABO
BAI ZHIJUN
LI XINWEI

Assignees

大唐陕西发电有限公司延安热电厂
中国大唐集团科技创新有限公司

Dates

Publication Date: 20260505
Application Date: 20251215

Claims (8)

1. An electric market trading system based on multimodal fusion and causal inference, the system comprising: The multi-source heterogeneous data acquisition module is used for automatically acquiring multi-source heterogeneous data related to the electric power market, including transaction center data, meteorological data, unit equipment state data, fuel price data and policy news information; The data management and fusion module is used for cleaning, managing and fusing the multi-source heterogeneous data, establishing a unified data warehouse and providing a unified data base; The variable change detection module is used for automatically detecting the remarkable change of the key variable relative to the data in the preset time period, quantifying the change degree and identifying the change type; the influence analysis evaluation module is used for quantifying the causal influence of the variable change on the electricity price and generating an electricity price prediction change result; the intelligent early warning pushing module is used for generating early warning information based on the electricity price prediction change result and pushing the early warning information to a transactor; The transaction strategy suggestion module is used for generating a transaction strategy suggestion through resistance reinforcement learning, a multi-time scale element learning mechanism and risk assessment; and the man-machine interaction and visualization module is used for providing a man-machine interaction interface and a data visualization function.
2. The multimodal fusion and causal inference based power market trading system of claim 1, the data governance and fusion module comprising: the data cleaning sub-module is used for executing abnormal value detection, missing value processing and data standardization operation of the multi-source heterogeneous data; the data fusion sub-module is used for carrying out unified formatting and structuring treatment on data from different sources based on characteristic engineering and a data fusion algorithm; And the data storage sub-module is used for establishing a unified data warehouse and executing storage and management of structured data and unstructured data.
3. The multi-modal fusion and causal inference based power market trading system of claim 1, wherein the variable change detection module automatically detects significant changes in key variables relative to data within a predetermined time period, comprising: Identifying the significant change of the variable by using a variable detection algorithm through statistical process control, a variable point detection algorithm and an anomaly detection algorithm; Performing a change degree quantization management by calculating quantization indexes of a change amplitude, a change speed, and a change duration; Performing a change type identification by distinguishing a change type of a periodic change, a trend change, a sudden change; and performing auxiliary identification of variable change detection by utilizing a federal change detection mechanism and jointly training a change detection model.
4. The multi-modal fusion and causal inference based power market trading system of claim 1, wherein the impact analysis assessment module comprises: The multi-mode time sequence diagram neural network fusion model is used for constructing an electric power market abnormal pattern, nodes comprise a unit, a bus and a load center, edges represent electric connection or transaction relations, time-space dependence characteristics of market data and a network topological structure are captured simultaneously by using the time sequence diagram neural network, and electricity price prediction is executed; the causal inference analysis submodule distinguishes causal effect and pseudo correlation between the variable and the electricity price through a causal inference method of double difference, causal forest or transfer entropy, and quantifies actual influence paths and intensities of exogenous impact of policy release and unit burst faults on the electricity price; and dynamically updating and correcting the multi-mode time sequence diagram neural network fusion model by using the causal analysis result.
5. The multi-modal fusion and causal inference based power market trading system of claim 1, wherein the intelligent pre-warning push module comprises: the early warning rule engine is used for setting a multi-stage early warning threshold value and a triggering condition; The early warning information generation sub-module is used for carrying out early warning trigger recognition on the electricity price prediction change result according to the multi-stage early warning threshold value and the trigger condition, and automatically generating an early warning report containing change variables, influence degrees and prediction changes; and the personalized pushing sub-module is used for pushing the early warning report to the customized pushing of the pushing content and the pushing mode according to the preference and responsibility of the trader.
6. The multi-modal fusion and causal inference based power market trading system of claim 1, wherein the trading strategy advice module comprises: The system comprises an antagonism reinforcement learning strategy generation engine, a main agent generation engine and a simulation engine, wherein the antagonism reinforcement learning strategy generation engine is used for constructing an environment simulating a real market game, introducing an antagonism agent into the environment to simulate the behaviors or extreme market situations of other participants in the market, training the main agent in the simulation environment through reinforcement learning, and generating a robust strategy resisting market manipulation and sudden disturbance; the multi-time scale element learning mechanism is used for introducing element learning algorithm, carrying out market mode change adaptation training of different time scales from D+2 to D+7 on the main intelligent agent, and executing strategy fast self-adaptive adjustment; And the interpretable policy recommendation and risk assessment sub-module is used for performing attribution analysis on the generated policy by utilizing SHAP and LIME interpretable AI technology and identifying key variables, decision logic and potential risk sources on which the policy depends.
7. The multi-modal fusion and causal inference based power market trading system of claim 1, wherein the human-machine interaction and visualization module comprises: The real-time monitoring instrument panel is used for displaying key indexes, early warning information and market dynamics; The interactive analysis tool supports the user to perform custom analysis and inquiry; and the strategy simulation and return detection sub-module is used for providing strategy effect verification and historical return detection functions.
8. A method of electric market trading based on multimodal fusion and causal inference, characterized in that it is implemented by the electric market trading system based on multimodal fusion and causal inference of any of claims 1 to 7, said method comprising: Automatically acquiring multi-source heterogeneous data related to an electric power market, wherein the multi-source heterogeneous data comprise transaction center data, meteorological data, unit equipment state data, fuel price data and policy news information; cleaning, managing and fusing the multi-source heterogeneous data, establishing a unified data warehouse and providing a unified data base; automatically detecting the remarkable change of the key variable relative to the data in the preset time period, quantifying the change degree and identifying the change type; Quantifying the causal influence of the variable change on the electricity price, and generating an electricity price prediction change result; Generating early warning information based on the electricity price prediction change result and pushing the early warning information to a trader; generating a transaction strategy suggestion through reinforcement learning of resistance, a multi-time scale element learning mechanism and risk assessment; and providing a man-machine interaction interface and a data visualization function.

Description

Electric power market trading system and method based on multi-mode fusion and causal inference Technical Field The invention relates to the technical field of electric power market trading, in particular to an electric power market trading system and method based on multi-modal fusion and causal inference. Background With the deep advancement of the electric power marketing reform, medium-and-long-term electric power trading from D+2 to D+7 becomes a core business of market participants, but the current trading strategy formulation still faces a plurality of bottlenecks, the prior art is highly dependent on personal experiences of traders, needs to manually collect and analyze massive multi-source heterogeneous information such as trading center data, meteorological data, unit state data and the like, has large workload and low efficiency, is difficult to realize effective integration of the data, and causes incomplete decision information, the traditional electricity price prediction model is mainly based on historical data to carry out correlation analysis, has insufficient response capability to exogenous impacts such as policy variation, unit burst faults and the like, has limited prediction precision, and meanwhile lacks an automatic monitoring and intelligent early warning mechanism of key variable change, so that a manual analysis mode cannot timely capture market dynamics and quick response, and is easy to miss a trade opportunity or face unnecessary risks. In the prior art, the multisource heterogeneous data is not integrated enough, the electricity price prediction precision is limited, and the technical problems of effective risk early warning and management and control mechanisms are lacked. Disclosure of Invention The application provides an electric power market trading system and method based on multi-mode fusion and causal inference, which are used for solving the technical problems of insufficient multi-source heterogeneous data integration, limited electricity price prediction precision and lack of effective risk early warning and management and control mechanisms in the prior art. In view of the above, the present application provides a system and method for electric power market trading based on multimodal fusion and causal inference. In a first aspect of the application, there is provided a multi-modal fusion and causal inference based power market trading system, the system comprising: The system comprises a multi-source heterogeneous data acquisition module, a variable change detection module, an influence analysis evaluation module, an intelligent early warning pushing module, a transaction strategy suggestion module and a man-machine interaction and visualization module, wherein the multi-source heterogeneous data acquisition module is used for automatically acquiring multi-source heterogeneous data related to an electric power market and comprises transaction center data, meteorological data, unit equipment state data, fuel price data and policy news information, the data management and fusion module is used for cleaning, managing and fusing the multi-source heterogeneous data, establishing a unified data warehouse and providing a unified data base, the variable change detection module is used for automatically detecting the significant change of a key variable relative to data in a preset time period, quantifying the change degree and identifying the change type, the influence analysis evaluation module is used for quantifying the causal influence of the variable change on the electric price and generating an electric price prediction change result, the intelligent early warning pushing module is used for generating early warning information and pushing the early warning information to a transactor, the transaction strategy suggestion module is used for generating a man-machine interaction and visualization module is used for providing a man-machine interaction interface and a data visualization function through resistance reinforcement learning mechanism and multi-time scale element learning mechanism. In a second aspect of the present application, there is provided a method of electric market trading based on multimodal fusion and causal inference, the method comprising: The method comprises the steps of automatically collecting multi-source heterogeneous data related to an electric power market, including transaction center data, meteorological data, unit equipment state data, fuel price data and policy news information, cleaning, managing and fusing the multi-source heterogeneous data, establishing a unified data warehouse and providing a unified data base, automatically detecting significant changes of key variables relative to data in a preset time period, quantifying the change degree and identifying the change type, quantifying the causal influence of the variable changes on the electric price, generating an electric price prediction change result, generating early warnin