Intelligent Analysis and Prediction of User Electricity Consumption Status Based on Big Data

Electricity is a pivotal energy resource in contemporary society, yet its supply stability and energy efficiency encounter obstacles. The advent of big data technology has resulted in the accumulation of vast quantities of user electricity consumption data within the power industry. This presents an opportunity to gain profound insights into users' electricity consumption statuses and behavioral patterns. The focal points of this study encompass the effective gathering and manipulation of user electricity consumption data, the construction of a precise electricity consumption prediction model, the interpretation of model predictions, and the validation and assessment of the model's performance. Findings reveal that the predicted data broadly aligns with the trend observed in the actual electricity consumption data of new users, albeit with some deviations. Furthermore, the calculated RMSE (Root Mean Square Error) for the new user group is 2.080, signifying that the model's average prediction error for this group is approximately 2.080 kilowatt hours. A lower RMSE value indicates that the model can produce more accurate predictions on different user groups, indicating good generalization performance of the model. This is of great significance for fields such as power supply management, energy efficiency optimization, and user behavior analysis.