A control method and system of a power selling device
By combining the dynamic electricity price calculation module and the load characteristic acquisition module with online sequence extreme learning machine algorithm and artificial bee colony algorithm, the problem of real-time analysis of market transaction data and user load characteristics in the existing power sales device control system is solved. This enables efficient allocation of power resources and accurate optimization of electricity pricing strategies, and improves the stability of power metering equipment and the reliability of feedback information.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 山西金投电力发展有限公司
- Filing Date
- 2026-05-18
- Publication Date
- 2026-06-12
AI Technical Summary
The control systems of existing electricity sales devices lack real-time analysis of market transaction data and accurate understanding of user load characteristics, resulting in low efficiency in power resource allocation, inability to meet the needs of peak shaving and valley filling of the power grid, and insufficient refinement of user response data processing. Noise interference leads to inaccurate feedback information, making it difficult to effectively use for electricity price adjustment and strategy optimization.
The system employs a dynamic electricity price calculation module, a load characteristic acquisition module, an optimization decision-making module, an execution control module, and a feedback adjustment module. It combines online sequence extreme learning machine algorithm, artificial bee colony algorithm, tabu search algorithm, wavelet denoising technology, and chaotic optimization algorithm to achieve real-time processing of electricity market transaction data and accurate analysis of user electricity consumption behavior, generate dynamic electricity price adjustment instructions, and optimize equipment control parameters.
It enables intelligent and refined management of the electricity sales process, with dynamic electricity prices closely aligned with market supply and demand changes, improving the efficiency of electricity resource allocation, ensuring the stable operation of electricity metering equipment, reducing noise interference, providing high-quality feedback information, forming a closed-loop optimization system, and promoting the continuous optimization of electricity pricing and sales strategies.
Smart Images
Figure CN122203263A_ABST