A Hybrid SCO–BiLSTM–DRL Framework for Intelligent Smart Grid Energy Management and Energy Demand Forecasting

Abstract

Smart grid technologies have evolved rapidly to enable intelligent monitoring, forecasting, and control of modern energy systems. However, the increasing integration of renewable energy sources, variable demand, and dynamic grid conditions poses significant challenges for effective energy management. Traditional methods are often not well-suited to handling the nonlinear and time-varying nature of smart grid data, which can lead to reduced prediction accuracy and scheduling efficiency. To address these challenges, this paper proposes a hybrid Sine Cosine Optimization (SCO)–Bidirectional Long Short-Term Memory (BiLSTM)–Deep Reinforcement Learning (DRL) framework for smart grid energy management. The BiLSTM model is employed to accurately forecast energy demand, whereas the SCO algorithm optimizes model parameters to enhance prediction performance. Experimental results show that the proposed model achieves lower Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values and higher prediction accuracy than conventional models, including Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and BiLSTM. The proposed framework improves energy utilization efficiency, reduces operating costs, and enhances electrical grid stability. Overall, the proposed approach provides a scalable and efficient solution for next-generation smart grid energy management.