Optimal Sizing of an Off-Grid Hybrid Energy System with Metaheuristics and Meteorological Forecasting Based on Wavelet Transform and Long Short-Term Memory Networks

Yamilet González Cusa, José Hidalgo Suárez (), Jorge Laureano Moya Rodríguez, Tulio Hernández Ramírez, Silvio A. B. Vieira de Melo and Ednildo Andrade Torres
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Yamilet González Cusa: Industrial Engineering Program, Polytechnic School, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
José Hidalgo Suárez: Electrical Engineering Program, SENAI CIMATEC University, Salvador 41650-010, Brazil
Jorge Laureano Moya Rodríguez: Industrial Engineering Program, Polytechnic School, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Tulio Hernández Ramírez: Electrical Engineering Program, Polytechnic School, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Silvio A. B. Vieira de Melo: Industrial Engineering Program, Polytechnic School, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil
Ednildo Andrade Torres: Industrial Engineering Program, Polytechnic School, Federal University of Bahia (UFBA), Salvador 40210-630, Brazil

Energies, 2025, vol. 18, issue 20, 1-34

Abstract: This study proposes an integrated framework for the optimal sizing of off-grid hybrid energy systems, combining photovoltaic panels, wind turbines, battery storage, a diesel generator, and an inverter. The methodology uniquely integrates long-term meteorological forecasting through a hybrid approach based on the Discrete Wavelet Transform and Long Short-Term Memory networks, together with metaheuristic optimization techniques (Particle Swarm Optimization and Genetic Algorithm), to minimize the system’s total annual cost. A case study was conducted in Guanambi, Brazil, using ten years (2012–2021) of hourly data on wind speed, solar irradiance, and ambient temperature. Forecasting results show that the hybrid Discrete Wavelet Transform–Long Short-Term Memory model outperforms the conventional Long Short-Term Memory approach, reducing error metrics and improving predictive accuracy. In the optimization stage, Particle Swarm Optimization consistently achieved lower costs and more stable convergence compared to the Genetic Algorithm. The optimal configuration comprised 450 photovoltaic panels, 10 wind turbines, 66 lithium iron phosphate battery, and 1 diesel generator, yielding a total annual cost of $105,381.17, a cost of energy of $0.1243/kWh, and minimal diesel dependence ($8825.89 annually). The proposed framework demonstrates robustness, economic viability, and applicability for providing sustainable and reliable electricity in isolated regions with high renewable energy potential.

Keywords: genetic algorithm; hybrid energy system; long short-term memory networks; optimal sizing; particle swarm optimization; discrete wavelet transform (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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