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

Rajasekaran Rajamoorthy, Hemachandira V. Saraswathi, Jayanthi Devaraj, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan, Gokulalakshmi Arunachalam, Tarek M. Mostafa and Lucian Mihet-Popa
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Rajasekaran Rajamoorthy: Department of Electrical and Electronics Engineering, SNS College of Technology, Coimbatore 641035, India
Hemachandira V. Saraswathi: Department of Mathematics, Kongu Engineering College, Erode 638060, India
Jayanthi Devaraj: Department of Information Technology, Sri Venkateswara College of Engineering, Chennai 602117, India
Padmanathan Kasinathan: Department of Electrical and Electronics Engineering and Center for Research and Development, Agni College of Technology, Thalambur, Chennai 600130, India
Rajvikram Madurai Elavarasan: Department of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, India
Gokulalakshmi Arunachalam: Department of Biomedical Engineering, SNS College of Technology, Coimbatore 641035, India
Tarek M. Mostafa: Computer, Electrical and Mathematical Science and Engineering (CEMSE), Ali I.AI-Naimi Petroleum Engineering Research Center (ANPERC), King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Saudi Arabia
Lucian Mihet-Popa: Faculty of Information Technology, Engineering and Economics, Ostfold University College, NO-1757 Halden, Norway

Sustainability, 2022, vol. 14, issue 3, 1-35

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. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. 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. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (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 is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. 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 percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

Keywords: energy forecasting; deep long short-term memory (deep LSTM); Sailfish Optimizer (SO); Whale Optimization Algorithm (WOA) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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