SolarisNet: A Deep Regression Network for Solar Radiation Prediction

Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversion rate estimation. In addition, a decent GSR estimate requires costly high-tech radiometer and expert dependent instrument handling and measurements, which are subjective in nature. As such, a computer aided monitoring (CAM) system to evaluate PV plant operation feasibility by employing smart grid past data analytics and machine learning is developed. Our algorithm, SolarisNet is a 6-layer deep neural network, which is trained and tested on data collected at two weather stations located near Kalyani metrological site, West Bengal, India. The daily GSR prediction performance using SolarisNet outperforms the existing state of the art, while its efficacy in inferring past GSR data insights to comprehend daily and seasonal GSR variability along with its competence for short term forecasting is discussed. Keywords— Deep learning; Gaussian process regression (GPR); Global solar radiation (GSR); forecasting; time series;