Forecasting-Driven Demand Response Management in Smart Grids Using Neural Networks

Demand Responses Management plays a pivotal role on the advancement of smart grid by promoting efficient energy utilization and optimizing overall system performance. A critical aspect of DRM is the accurate forecasting of household electricity demand, which is essential for effective power system planning, operational efficiency, and informed decision-making. Precise demand predictions contribute to cost reduction, risk mitigation, and strategic energy management. This study proposes an neural network-based forecasting Focus specifically designed for load side management in smart grid. It explores application of advanced Artificial Neural Network (ANN) architectures to model and predict electricity consumption patterns. The performance of the proposed ANN models is rigorously evaluated and benchmarked against three conventional statistical methods.