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

•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...

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Vydáno v:Energy conversion and management Ročník 315; s. 118766
Hlavní autoři: Hussain Alhamami, Ali, Abba, Sani I., Musa, Bashir, Aminu Dodo, Yakubu, Abiodun Salami, Babatunde, Alhaji Dodo, Usman, Alyami, Saleh H.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.09.2024
Témata:
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
Nigeria
Niger
Energy demand
Standalone kernels
Artificial intelligence
GA-based optimal sizing
Metaheuristic algorithms
ISSN:0196-8904
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Shrnutí:•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 other developing countries.
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ISSN:0196-8904
DOI:10.1016/j.enconman.2024.118766