A Performance Evaluation of Machine Learning Models for Solar PV Power Forecasting in Bamenda, Cameroon

Facing increased energy demand which surpasses national grid supply capacity due to rapid population growth, urbanization, and economic activities, developing countries such as Cameroon are deploying solar photovoltaic power (SPVP) systems to supplement their energy needs; with these systems heralded for sustainability and environmental friendliness. However, the inherent intermittency of SPVP is a major concern since it cannot reliably fill the supply-demand gap with its associated risk of non-availability. Tackling this issue requires adequate forecasting of SPVP to guarantee better management of the energy shortfall. This study evaluates the performance of twenty-four machine learning models (MLMs) in forecasting SPVP in Bamenda, Cameroon. The study uses data from Photovoltaic Geographical Information System with six input features (direct beam irradiance, diffuse irradiance, reflected irradiance, sun height, ambient temperature, and wind speed) and training-testing split of 80% - 20% to forecast SPVP as output feature. Employing hold-out and re-substitution validation techniques, MLMs performance was evaluated using Coefficient of Determination (R2) and Root Mean Squared (RMSE) metrics. Results reveal wide neural network model as the overall best performer with R2 of 0.999 and RMSE of 9.377, compared to the other models with same or lower R2 and higher RMSE ranging from 9.4522 to 458.97. This model was used to perform short-term SPVP forecast in Bamenda and may be used in the forecast of SPVP in geographically similar areas of Cameroon. This study underscores the role and importance of MLM performance evaluation to identify the best-yield model for SPVP to reliably fill supply-demand gaps.