Power load and electricity price collaborative prediction method and system based on deep learning

By collecting and processing power status data in real time in the power system, calculating the voltage lag feedback and synchronization response excitation index, and constructing a comprehensive collaborative prediction index, the problem of insufficient identification of the coupling relationship in the collaborative prediction of power load and electricity price is solved, and efficient and intelligent prediction and scheduling of the power system is realized.

CN121031906AInactive Publication Date: 2025-11-28SHENZHEN POWER TECH GRP CO LTD
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Patent Information

Application Number
CN202511309857.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-09-15
Publication Date
2025-11-28
Estimated Expiration
Not applicable · inactive patent

AI Technical Summary

Technical Problem

Existing technologies lack a dynamic identification and quantification mechanism for the coupling relationship between electricity price disturbances and load response in the co-prediction of power load and electricity price. They are unable to capture the sudden change trend under extreme load fluctuations or high-frequency electricity price disturbances, and lack a dynamic index system for responding to asymmetric coupling characteristics, resulting in insufficient real-time performance, robustness and system disturbance perception capabilities.

Method used

By deploying voltage sensors, thermistor temperature sensors, and three-phase power meters in the power system to collect power status data in real time, and then transmitting the data to the power data platform via API interface for data processing, the voltage hysteresis feedback index dyx and the synchronization response excitation index tbj are calculated, and a comprehensive collaborative prediction index xty is constructed to achieve in-depth analysis and prediction of the collaborative relationship between power load and electricity price.

Benefits of technology

It significantly improves the power system's ability to perceive and predict power disturbances, enables quantitative assessment of the synergistic relationship between load trends and electricity price fluctuations, supports advance price adjustment simulation, load control, and backup power dispatch, and enhances the foresight of dispatch decisions and the stability of system operation.

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Abstract

The invention discloses an electric power load and electricity price collaborative prediction method and system based on deep learning, and relates to the technical field of electric power systems.The method comprises the steps that electric power state data are collected in real time through a sensor set arranged in electric power distribution equipment, and data processing is carried out; acquiring a power disturbance data set, calculating a voltage lag feedback index dyx, performing power supply disturbance evaluation, executing a coupling response trend prediction instruction, and acquiring a disturbance characteristic data set and calculating a synchronous response excitation index tbj according to real-time extraction and dimensionless processing of a special API (Application Program Interface); cooperative disturbance evaluation is carried out by calculating a comprehensive cooperative prediction index xty, and when it is detected that the load and the electricity price have a cooperative disturbance trend, early warning is automatically generated, and price adjustment simulation, a negative control strategy and a standby power supply scheduling mechanism are linked. The method can effectively improve the prediction precision and the regulation and control response capability of the power grid under multi-factor disturbance, and improves the operation stability and economical efficiency of the smart power grid.
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