The prediction accuracy of support vector regression (SVR) is highly influenced by a kernel function. However, its performance suffers on large datasets, and this could be attributed to the computational limitations of kernel learning. To tackle this problem, this paper combines SVR with the emerging Harris hawks optimization (HHO) and particle swarm optimization (PSO) algorithms to form two hybrid SVR algorithms, SVR-HHO and SVR-PSO. Both the two proposed algorithms and traditional SVR were applied to load forecasting in four different states of Nigeria. The correlation coefficient (R), coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute percentage error (MAPE) were used as indicators to evaluate the prediction accuracy of the algorithms. The results reveal that there is an increase in performance for both SVR-HHO and SVR-PSO over traditional SVR. SVR-HHO has the highest R2 values of 0.9951, 0.8963, 0.9951, and 0.9313, the lowest MSE values of 0.0002, 0.0070, 0.0002, and 0.0080, and the lowest MAPE values of 0.1311, 0.1452, 0.0599, and 0.1817, respectively, for Kano, Abuja, Niger, and Lagos State. The results of SVR-HHO also prove more advantageous over SVR-PSO in all the states concerning load forecasting skills. This paper also designed a hybrid renewable energy system (HRES) that consists of solar photovoltaic (PV) panels, wind turbines, and batteries. As inputs, the system used solar radiation, temperature, wind speed, and the predicted load demands by SVR-HHO in all the states. The system was optimized by using the PSO algorithm to obtain the optimal configuration of the HRES that will satisfy all constraints at the minimum cost.

Hybrid Renewable Energy Systems (HRESs) have been touted as an appropriate way for supplying electricity to remote and off-grid areas in developing countries, especially in sub-Saharan Africa (SSA), where rural electrification challenges are the most pronounced. This study proposes a two-step methodology for optimizing and analyzing a stand-alone photovoltaic/wind/battery/diesel hybrid system to meet the electricity needs of Fanisua, an off-grid and remote village of northern Nigeria. In the first step, the MATLAB environment was used to run simulations and optimize the system via the genetic algorithm. Then, techno-economic and emissions analysis was carried out in the second step to compare the proposed system to the existing traditional modes of rural electrification in sub-Saharan Africa, namely, the grid-extension and diesel generator. The break-even distance parameter was adopted in the comparison with grid-extension. Besides, the hypothetical project of replacing the diesel generator by the optimal system was analyzed using the Simple Payback Period (SPP) and Net Present Value (NPV) parameters. The resulting optimal design architecture included an 89.271-kW photovoltaic array, a 100.31-W diesel generator, and 148 batteries with a total annualized cost (TAC) and cost of energy (COE) of USD 43,807 and USD 0.25/kWh, respectively. The break-even distance found was 16.2 km, while the NPV and SPP of the hypothetical project were USD 572,382 and 2.8 years, respectively. The savings in carbon dioxide (CO2) emissions of the proposed system compared to the grid extension and the diesel generator were found to be 85,401.08 kg/year and 122,062.85 kg/year, respectively. This study highlighted the role that solar PV-based HRESs could play in the sustainable electricity supply in rural areas of sub-Saharan Africa.

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It is commonly recommended to incorporate diesel generators into distributed hybrid renewable energy systems (HRESs) to lower the system's total cost and make the generated electricity affordable. Due to the environmental and economic issues associated with fossil fuel use, biomass power technologies (BPT) appear to be an attractive alternative to diesel generators. The HOMER software was used to model, simulate, and optimise two photovoltaic/wind/battery systems integrated with different BPT (anaerobic digestion or gasification) to satisfy the electrical needs of Babadam, a remote community in northern Cameroon. The results indicated that the anaerobic digester integrated system's overall optimal architecture included a 98.1 kW photovoltaic array, a 30 kW biogas generator, and 200 batteries, with a cost of energy (COE) of $0.347/kWh. On the other hand, the gasifier integrated option is made up of an 81.8 kW photovoltaic array, a 15 kW syngas generator, and 200 batteries and has a COE of $0.319/kW. Additionally, compared to the PV/Wind/Battery system, integrating the biogas (resp. syngas) generator showed a potential COE decrease of 29% (resp. 40%). The sensitivity analysis highlighted the validity of this COE reduction potential everywhere in sub-Saharan Africa, leading to the conclusion that integrating BPT into HRESs can effectively contribute to Sustainable Development Goal 7.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

The average seasonal variations of surface radio refractivity pressure, temperature and relative humidity made across Yola (9 N, 6 o 45' E), Lagos (6 o 27' N, 5 o 12' E), and Port measurement was made using wireless automatic weather station installed at the surface of each station under study. The study utilized three years of meteorological data measured from January 2010 to December 2013 to calculate the surface radio refractivity using empirical form recommended by the International Telecommunication Union (ITU that the average value of surface radio refractivity showed seasonal variations with high values during the rainy season and low values during the dry season surface radio refractivity of 242N-Lagos station. The result also shows that climatic condition is the force behind the seasonal variations of surface radio refractivity in all study stations.