Gravitational Search Algorithm with Deep Learning Enabled Short-Term Load Forecasting Scheme for Smart Grid Environment

The Short-Term Load Forecasting System for Smart Grid (SG) Environment is a predictive modelling technique developed for anticipating the energy consumption over a comparably small time horizon within the Smart Grid framework. Leveraging advanced predicting methods, this system integrates different data sources like weather conditions, real-time grid data, and historical consumption patterns for generating exact prediction of short-term load requirement. The system empower grid operators and utilities to enhance resource allocation, improve grid stability, and proactively manage energy distribution by using sophisticated techniques, statistical, and machine learning algorithms. This predicting scheme plays a major role in enabling effective energy utilization and planning, which contribute to the overall sustainability and reliability of Smart Grid system. This manuscript offers the design of Gravitational Search Algorithm with Deep Learning Enabled Short-Term Load Forecasting (GSADL-STLF) Scheme for Smart Grid Environment. The GSADL-STLF technique make use of deep belief network (DBN) for modelling and forecasting the short-term electricity utilization pattern. Through the assimilation of different and adaptive data sources like historical load profile, weather condition, and grid operational parameter, the DBN model properly predicts the load in the SG environment. For fine tuning the parameters related to the DBN model, the GSA has been applied. By extensive performance analysis of the GSADL-STLF technique, the performance is validated on real world dataset. The experimental values inferred that the GSADL-STLF technique exhibit significant performance to predict the short term load demand.