SolarisNet: A Deep Regression Network for Solar Radiation Prediction
This work addresses the problem of accurate and cost-effective solar radiation forecasting for photovoltaic plant operators, though it appears incremental as it applies a deep learning method to a specific domain.
The paper tackles the challenge of predicting global solar radiation (GSR) for photovoltaic plant utilization by developing SolarisNet, a 6-layer deep neural network, which outperforms existing state-of-the-art methods in daily GSR prediction.
Effective utilization of photovoltaic (PV) plants requires weather variability robust global solar radiation (GSR) forecasting models. Random weather turbulence phenomena coupled with assumptions of clear sky model as suggested by Hottel pose significant challenges to parametric & non-parametric models in GSR conversion rate estimation. Also, a decent GSR estimate requires costly high-tech radiometer and expert dependent instrument handling and measurements, which are subjective. As such, a computer aided monitoring (CAM) system to evaluate PV plant operation feasibility by employing smart grid past data analytics and deep learning is developed. Our algorithm, SolarisNet is a 6-layer deep neural network trained 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 art and 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.