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Research Article |

Sailfish-Optimized Feed Forward Neural Network for Accurate Forecasting and Economic Optimization of Microgrid Energy Systems

Author(s): Anish Vora1*, and Rajendragiri Aparnathi2

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 14, Issue 1

Publisher : FOREX Publication

Published : 30 March 2026

e-ISSN : 2347-470X

Page(s) : 186-199

DOI: https://doi.org/10.37391/IJEER.140120

Abstract

The integration of renewable energy sources (RES) such as solar and wind into microgrids introduces challenges in energy balance, voltage stability, and economic dispatch due to their intermittent and variable nature. This paper proposes a novel Sailfish-optimized Feed Forward Neural Network (SbFNN) framework to address these challenges by combining intelligent forecasting with optimization-driven energy management. The FFNN predicts microgrid load, solar, and wind generation, while the Sailfish Optimization algorithm adaptively tunes network weights and minimizes operational costs. The proposed SbFNN improves load balancing, reduces energy losses, and maintains reliable voltage and frequency under varying load conditions. Extensive simulation using a one-year hourly microgrid dataset demonstrates that SbFNN achieves superior forecasting accuracy (R² = 0.993, MAPE = 0.0093) and statistically significant improvements over benchmark methods. Economic dispatch results show reduced total operating costs and efficient utilization of renewable resources. The SbFNN framework thus offers an effective, adaptive, and scalable solution for future sustainable microgrid management.

Keywords: Microgrid, Sailfish, Cost, Economic Dispatch, Forecasting, Solar, Wind, Load, Feed Forward Neural Network.




Anish Vora, Ph. D. Research Scholar, Department of Electrical Engineering, Faculty of Engineering & Technology, Gokul Global University, Gujarat, India; Email: voraanish@yahoo.com

Rajendragiri Aparnathi, Professor (Associate), Department of Electrical Engineering, Faculty of Engineering & Technology, Gokul Global University, Gujarat, India; Email: rajendraaparnathi@gmail.com

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Anish Vora, Rajendragiri Aparnathi (2026), Sailfish-Optimized Feed Forward Neural Network for Accurate Forecasting and Economic Optimization of Microgrid Energy Systems. IJEER 14(1), 186-199. DOI: 10.37391/IJEER.140120.