SolarisNet: A Deep Regression Network for Solar Radiation Prediction

Kyoto Protocol (KP) like strategic agreements on energy resources reflects the need for long run forecasting of renewable energy time series fluctuations and mitigate the problems of environment degradation due to emission exhausts from nonrenewable resources [1]. Photovoltaic systems for industrial and domestic uses require the distribution of grid connected power systems with solar radiation as the main energy source. However direct conversion of solar to electrical energy is costly and has relatively low efficiency [2]. Coupled with grid stability issues concerning scheduling and assets optimization for short-term (monthly)and long-term (yearly) forecasting requires guaranteed knowledge of solar radiation instabilities at local weather stations. All this information is based on satellite observations and data from ground stations, with uncertainty in geographic and time availability of data, and data sampling rate posing significant forecast granularity. To assess the PV plant operation dependability on global solar radiation (GSR), good measurement of GSR using a high class radiometer and correct controlling of the instrument through correct maintenance policy is essential.