Data Analysis and Mining Methods Based on the Actual Operation of the Electricity Market and Energy Consumption

Nowadays, the widespread use of new energy has made the operation of the electricity market more complex, which requires more efficient data analysis and mining methods. This article comprehensively applies methods such as machine learning, time series analysis, and optimal modeling to conduct in-depth analysis of the operation and energy consumption data of the Chinese electricity market. Taking the electricity market as the research object, this article studies its application in electricity demand forecasting, market development trends, and optimized configuration. Based on this, it explores the impact of data mining on the electricity industry’s response to energy environment and policy demands. The main research content includes: using statistical learning methods to discover new trends and anomalies; using optimal algorithms to improve energy efficiency; machine learning-based feature extraction and predictive analysis of complex data. During the observation period from 01–01 to 01–02, the electricity demand decreased from 1420MW at 23:00 on 01–01 to 1200MW at 00:00 on 01–02. Through the research in this article, accurate prediction of electricity demand can be achieved, and based on this, optimal energy allocation can be achieved, reducing operating costs and providing decision-making basis for the behavior of electricity market entities.