Solar Radiation Prediction Using an Improved Adaptive Neuro-Fuzzy Inference System (ANFIS) Optimization Ensemble

Abstract

A dependable design and monitoring of solar energy-based systems necessitates precise data on available solar radiation. However, measuring solar radiation is challenging due to the expensive equipment required for measurement, along with the costs of calibration and maintenance, especially in developing countries like Nigeria. As a result, data-driven techniques are often employed to predict solar radiation in such regions. However, the existing predictive models frequently yield unsatisfactory outcomes. To address this issue, this study proposes the creation of intelligent models to forecast solar radiation in Kano state, Nigeria. The model is developed using an ensemble machine learning approach that combines two Adaptive Neuro-Fuzzy Inference Systems with sub-clustering optimization and grid-partitioning optimization. The meteorological data used for model development include maximum temperature, minimum temperature, mean temperature, and solar radiation from the previous 2 days as predictors. To evaluate the model’s performance, various metrics like correlation coefficient, determination coefficient, mean-squared error, root-mean-squared error, and mean-absolute error are employed. The simulation results demonstrate that the ANFIS ensemble outperforms the individual ANFIS models. Notably, the ANFIS-ENS exhibits the highest accuracy. Consequently, the developed models provide a reliable alternative for estimating solar radiation in Kano and can be instrumental in enhancing the design and management of solar energy systems in the region.