A Hybrid Sailfish Whale Optimization and Deep Long Short-Term Memory (SWO-DLSTM) Model for Energy Efficient Autonomy in India by 2048

Abstract

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. Deep learning models play a vital role in accurately forecasting the energy de-mand. A novel model called Sail Fish Whale Optimization-based Deep Long Short- Term memory (SFWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SFWO is designed by integrating the Sail Fish Optimizer (SFO) with the Whale Optimiza-tion Algorithm (WOA). The Hilbert-Schmidt Independence Criterion Lasso (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm was implemented in MATLAB software package and the study was done using real-time data. The feature selection pro-cess improves the accuracy of the proposed model by training the features using Deep LSTM. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves good accuracy with the average normalized Root Mean Squared Error (RMSE) value of 4.4559. The hybrid approach provides improved accuracy for the prediction of energy demand in India by the year 2047.