Lanre Olatomiwa

Potential of support vector regression for solar radiation prediction in Nigeria

In this paper, the accuracy of soft computing technique in solar radiation prediction based on series of measured meteorological data (monthly mean sunshine duration, monthly mean maximum and minimum temperature) taking from Iseyin meteorological station in Nigeria was examined. The process, which simulates the solar radiation with support vector regression (SVR), was constructed. The inputs were monthly mean maximum temperature (Tmax), monthly mean minimum temperature (Tmin) and monthly mean sunshine duration (

Techno-economic feasibility of hybrid renewable energy system for rural health centre (RHC): The wayward for quality health delivery

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A support vector machine–firefly algorithm-based model for global solar radiation prediction

n¯). Polynomial and radial basis functions (RBF) are applied as the SVR kernel function to estimate solar radiation. According to the results, a greater improvement in estimation accuracy can be achieved through the SVR with polynomial basis function compared to RBF. The SVR coefficient of determination R2 with the polynomial function was 0.7395 and with the radial basis function, the R2 was 0.5877.

Energy management strategies in hybrid renewable energy systems: A review

Hybrid energy systems are becoming attractive for providing electricity in remote areas due to excessive expenditure of grid extension, increase in oil price and advances in renewable energy technology. Optimal sizing of components can reduce the cost of hybrid systems. This article illustrates the size optimization of solar-wind-diesel generator-battery hybrid system designed for a remote location mobile telecom base transceiver station in Nigeria. Different energy combinations have been analyzed using HOMER 2.81 (Hybrid Optimization Model for Electric Renewables) in order to determine an optimal model. Simulation results show that the hybrid energy systems can minimize the power generation cost significantly and can decrease CO2 emissions as compared to the traditional diesel generator only.

Economic evaluation of hybrid energy systems for rural electrification in six geo-political zones of Nigeria

Role of off-grid renewable energy in extending basic healthcare services to rural villages where there is no grid extension or unreliable power supply cannot be over emphasized. This paper thus evaluated the technical and economic benefit of powering off-grid rural health clinic with renewable energy resources. The rural clinic selected as a case study in this paper is situated at Fatika rural village in northern Nigeria. Techno-economic analysis was carried out with HOMER software and the analysis found PV/diesel/battery best optimal configuration among other considered configurations. The results obtained proved greater potential of hybrid renewable energy system configuration consisting PV, diesel and battery in providing electricity to the rural health clinics far away from grid centers.

Adaptive neuro-fuzzy approach for solar radiation prediction in Nigeria

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.