Advanced Load Forecasting in Power Systems with Bayesian Networks and Reinforcement Learning Models

Power system planning, management, and utility operation require power load forecasting as renewable energy sources and external power facilities integrate. Accurate forecasting matches power generation to demand, boosting reliability and efficiency. Estimating changing consumer demand energy needs requires short-term load forecasting (STLF). Electrical load unpredictability makes power storage challenging, therefore accurate forecasting is needed. The research introduces BNRL, an intelligent machine learning method for power system STLF. Data breakdown, preprocessing, feature selection, prediction, and parameter adjustment are BNRL's steps. OAFSA extracts optimal attributes to increase prediction accuracy. The model is validated by extensive benchmark simulations. The BNRL model outperforms other state-of-the-art approaches in predicting power loads, as evidenced by the simulation results. Bayesian reinforcement learning and intelligent feature selection improve short-term load forecast accuracy and robustness. The model's good performance shows it can handle complex electrical loads. The results imply that the BNRL model could improve power system management by better power load prediction. Integrating OAFSA for optimal feature selection improves the model. This study improves estimates for power grid energy utilisation and system optimisation.