Evaluation and Prediction of Peak Energy Demand in the Electricity Grid Using Machine Learning

Abstract

The precise prediction of peak energy demand in electricity networks has grown more crucial as power systems become more complicated and renewable energy sources are included. This paper introduces a novel method that uses a Convolutional Neural Network (CNN) model, enhanced with the Black Widow Optimizer (BWO), to tackle the difficulties associated with predicting peak energy demand in power networks. Utilizing the "Peak Energy Demand in the Electricity Energy Dataset BanE-16," this study comprehensively examines many elements that impact energy use trends in contemporary electrical grids. The CNN-BWO model has outstanding prediction accuracy, as seen by its low Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values, as well as its high Coefficient of Determination (R²). These measurements demonstrate the model's efficacy in detecting intricate patterns in energy use, which is crucial for accurate demand prediction. The conclusions of this research are vital for improving grid management, namely in maximizing the provision of electricity, reducing the likelihood of power failures, and effectively integrating renewable energy sources. Furthermore, the approach and results are adaptable, providing opportunities for use in various fields of predictive modeling, thereby extending the impact of advanced machine learning in predictive analytics. This study makes a substantial contribution to the field by showing how advanced machine learning models may enhance the accuracy and control of energy demand forecasting within electrical grids.