Prediction of Daily Global Solar Radiation on a Horizontal Plane Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract

In recent years, the world has seen an exponential increase in energy demand, prompting scientists to look for innovative ways to exploit the power sun’s power. Solar energy technologies use the sun's energy and light to provide heating, lighting, hot water, electricity and even cooling for homes, businesses, and industries. Therefore, ground-level solar radiation data is important for these applications. Thus, our work aims to use a mathematical modeling tool to predict solar irradiation. For this purpose, we are interested in the application of the Adaptive Neuro Fuzzy Inference System. Through this type of artificial neural system, 10 models were developed, based on meteorological data such as the Day number (Nj), Ambient temperature (T), Relative Humidity (Hr), Wind speed (WS), Wind direction (WD), Declination (δ), Irradiation outside the atmosphere (Goh), Maximum temperature (Tmax), Minimum temperature (Tmin). These models have been tested by different static indicators to choose the most suitable one for the estimation of the daily global solar radiation. This study led us to choose the M8 model, which takes Nj, T, Hr, δ, Ws, Wd, G0, and S0 as input variables because it presents the best performance either in the learning phase (R² = 0.981, RMSE = 0.107 kW/m², MAE = 0.089 kW/m²) or in the validation phase (R² = 0.979, RMSE = 0.117 kW/m², MAE = 0.101 kW/m²).