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

Hemachandira V Saraswathi,Padmanathan Kasinathan,Gokulalakshmi Arunachalam,Rajvikram Madurai Elavarasan,Jayanthi Devaraj,Lucian Mihet-Popa,Tarek M Mostafa,Rajasekaran Rajamoorthy

doi:10.3390/su14031355

Hemachandira V Saraswathi, Padmanathan Kasinathan + Show 6 more

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https://doi.org/10.3390/su14031355

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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.

Highlights

Energy autonomy is built on the various dimensions and targets to enable local energy generation and use and to attain balance between demand and supply in an economically viable and sustainable manner
The existing methods used to compare the performance of the proposed model are Bootstrap-based Extreme Learning Machine (ELM) approach (BELM) [21], Direct Quantile Regression (DQR) [42], Temporally Local Gaussian Process (TLGP) [24], Deep Echo State Network (Deep ESN) [47] and
In order to prove the efficiency of the proposed model, the results of the Sailfish Whale Optimization (SWO)-Deep Long Short-Term Memory (LSTM) are compared with the various existing models like bootstrap-based ELM, Deep Echo state network, TLGP, DQR and Deep LSTM

Summary

Introduction

Energy autonomy is built on the various dimensions and targets to enable local energy generation and use and to attain balance between demand and supply in an economically viable and sustainable manner. In addition to the population and economic growth, global energy consumption has increased, which poses challenges among the researchers to propose various optimistic solutions. To solve these issues, an accurate and efficient forecasting model must be designed. India tends to generate surplus power but there is an inadequate infrastructure to distribute the electricity. To address this issue, the Indian Government launched the “Power for All” program in the year 2016. The International Energy Agency (IEA) prediction shows that before 2050, the nation will increase its production and the electricity production will be between 600 GW to 1200 GW [2].

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