Feasibility of Hybrid Neuro-Fuzzy (ANFIS) Machine Learning Model with Classical Multi-Linear Regression (MLR) For the Simulation of Solar Radiation: A Case Study Abuja, Nigeria

Abstract

The extremely variable nature of solar radiation makes it difficult for solar power plants to keep up with predicted power output and demand curves. As a result, solar radiation simulation is crucial to the efficient design, administration, and operation of any solar power plant. With only partially satisfactory results, empirical models have been routinely employed in Nigeria to predict solar radiation from easily measurable environmental characteristics like temperature, humidity, cloud cover, etc. Only a few machine learning models have been used to predict sun radiation in Nigeria, despite the global trend toward machine learning. With almost no published work utilizing Abuja as a case study, machine learning algorithms for simulating sun radiation in Nigeria have not been sufficiently studied. By contrasting the performance of the conventional Multi-Linear Regression (MLR) model with the cutting-edge machine learning model, ANFIS, this study seeks to close this gap and establish which model is more suited and accurate for forecasting solar radiation in Abuja, Nigeria. Data for daily measured climatic variables, such as maximum and minimum temperatures, relative humidity, precipitation, maximum and minimum wind speeds, sunshine hours, and solar radiation were retrieved for this study over ten years from the National Space Research and Development Agency, (NASDRA) Abuja. R, R2, RMSE, and MSE were used to simulate and assess the performance of various model combinations throughout both the training and testing stages. When compared to the best MLR model simulation, ANFIS model 8 was shown to generate accurate results.