Power Load Forecasting and Accurate Early Warning Model Simulation of Power Theft Based on Data Mining Algorithm

Given the challenges posed to power system operation by the increasing global energy demand, traditional load forecasting methods struggle to meet contemporary requirements for accuracy and real-time performance. This article uses Long Short Term Memory (LSTM) as the basic model, combined with ensemble learning strategy, to construct an efficient power load forecasting model. By preprocessing data, extracting features, and conducting correlation analysis, significant features influencing power load are identified to enhance the model’s prediction accuracy. Experimental findings demonstrate that the ensemble learning LSTM model outperforms the single LSTM model significantly in terms of mean square error (MSE) and root mean square error (RMSE), validating the effectiveness of ensemble learning in improving prediction accuracy. At the same time, this paper also constructs an accurate early warning model of power theft based on LSTM, which can effectively identify power theft by mining abnormal patterns in historical power consumption data of users and combining external characteristics such as weather and holidays. The weighted average method is used to integrate the prediction results of several LSTM models, which improves the accuracy and robustness of early warning. In the simulation experiment, by artificially injecting the data of electricity stealing behavior, it is verified that the model can accurately identify and warn the electricity stealing behavior, and the accuracy and recall rate reach a high level. This study provides a new and more accurate load forecasting and early warning method for power industry. This helps ensure the stable functioning of the power grid, improves resource allocation efficiency, and minimizes economic losses.