A Hybrid PSO–LSTM Framework For Intelligent Demand-Side Management In Smart Grids With Renewable Energy Integration

Abstract

The growing penetration of renewable energy resources, electric vehicles (EVs) and distributed energy systems, demand-side management (DSM) in modern smart grids has become increasingly complex. Conventional DSM methods usually show lower predictive capability and ineffective load scheduling in a tight operating environment, which leads to increased operational costs, hindered grid stability. This paper presents a hybrid PSO–RNN framework for intelligent freedom of management for power systems. The proposed framework combines an RNN for precise short-term load forecasting with PSO for effective optimal load scheduling, EV charging coordination, and energy distribution by utilizing data on historical electricity consumption, outdoor weather conditions, renewable energy generation variables, electricity pricing data of local suppliers in Singapore,, average grid load from the previous day (24 hours) as well as #datetimeindex. The experimental evaluation shows that the proposed PSO–RNN model can reach 98.24% forecasting accuracy, reduce RMSE to 0.081 and MAE to 0.061 value, while achieving over 24.76 % of peak-load reduction; saving about 21.35 % of electricity cost; using approximately by up to 91.42 % renewable energy as well, compared with ANN, SVM, DRL and RNN standalone models. These features increase reliability, lower operation costs and assist in sustainable energy recovery. The designed framework can effectively serve as a scalable, adaptive and computationally efficient next-generation intelligent smart grid and real-time electricity demand side management solution.