Off-grid multi-region energy system design based on energy load demand estimation using hybrid nature-inspired optimization algorithms

• Published in: Energy conversion and management 2024-09, Vol.315, p.118766, Article 118766
• Main Authors: Hussain Alhamami, Ali, Abba, Sani I., Musa, Bashir, Aminu Dodo, Yakubu, Abiodun Salami, Babatunde, Alhaji Dodo, Usman, Alyami, Saleh H.
• Format: Article
• Language: English
• Subjects: administrative management; algorithms; Artificial intelligence; batteries; energy conversion; Energy demand; GA-based optimal sizing; Metaheuristic algorithms; Niger; Nigeria; prediction; regression analysis; solar radiation; Standalone kernels; sustainable development; systems engineering; temperature; wind speed
• ISSN: 0196-8904
• DOI: 10.1016/j.enconman.2024.118766

•Sustainable Development Goal 7 was addressed in the off-grid multi-region energy study.•A multi-region design load system was proposed to reduce the total annual cost.•Several hybrid approaches were derived for the modelling of energy demand.•Hybrid ML algorithms were utilized and compared with the traditional ML method.•The outcomes depicted the reliable accuracy of hybrid models. Reliable energy demand estimation and optimal sizing of a stand-alone photovoltaic/wind/battery hybrid energy system are critical for achieving sustainable development goals. This study presents a hybridized design of an off-grid multi-region energy system to reduce the total annual cost of the system based on a genetic algorithm. A stand-alone kernel support vector regression was used to predict the short-term load demand variability of four major states in Nigeria, namely, Kano, Abuja, Niger, and Lagos. To calibrate the models, primary data was obtained from the four states: load demand (W), temperature (oC), wind speed (m/s), and solar radiation (Wh/m2). The model was evaluated in calibration and verification phases using four key metrics: Nash-Sutcliffe Efficiency, Mean Squared Error, Root Mean Squared Error, and Correlation Coefficient. The model's accuracy was visualized using a newly emerged fan plot and a two-dimensional Taylor diagram. Three kernel support vector regression-C models (C1, C2, and C3) were derived based on the input combination. The outcomes proved that the kernel support vector regression-C3 model tends to perform better in prediction accuracy for all the cities, followed by kernel support vector regression-C2 and-C1. Compared to other cities, Lagos (kernel support vector regression-C3) has the lowest prediction accuracy, while Niger (kernel support vector regression-C3) has the highest prediction accuracy. To enhance the stand-alone approach, hybrid adaptive neuro-fuzzy-based particle swarm optimization, and biogeography-based optimization algorithms were employed. The optimization results revealed that the adaptive neuro-fuzzy-based particle swarm optimization model outperformed the biogeography-based optimization approach in the calibration and verification phases. Niger State demonstrated the highest performance metric values, with Kano, Lagos, and Abuja following closely behind. These findings proved that adaptive neuro-fuzzy-based particle swam optimization is a promising model for load demand forecasting in Nigeria and holds potential for use in