Emerging Harris Hawks Optimization based load demand forecasting and optimal sizing of stand-alone hybrid renewable energy systems– A case study of Kano and Abuja, Nigeria

Abstract This paper presents load forecasting and optimal sizing for minimizing the Annualized Cost of the System (ACS) of a stand-alone photovoltaic (PV)/wind/battery hybrid renewable energy system. To achieve load forecasting, the Support Vector Regression (SVR) was integrated with the emerging Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) algorithms to form two hybrid SVR algorithms (SVR-HHO and SVR-PSO). The single SVR and the two obtained hybrid SVR algorithms were used to predict the load demand variability of remote areas in Kano and Abuja, Nigeria. For optimal sizing, a PSO algorithm was used. The prediction accuracy of the algorithms was evaluated using Correlation Coefficient (R), Coefficient of Determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). The results show that both hybrid SVR algorithms outperformed the single SVR in terms of prediction accuracy. Furthermore, SVR-HHO has the highest goodness of fit and lowest prediction error. Besides, the SVR-HHO proved merit over SVR-PSO despite its reliability. These results concluded that metaheuristic algorithms are more promising in forecasting load demand and hence can serve as a reliable tool for decision making.