Spatial forecasting of solar radiation using ARIMA model

Abstract

This paper is an attempt to forecast monthly solar radiation using remote sensing data on a region. Seasonal ARIMA (SARIMA) models are used for simulating and forecasting time series of insolation data from NASA's POWER (Prediction of Worldwide Energy Resources) data archive. Remotely sensed modelled insolation data for 34 years (i.e. January 1984 to December 2017) has been retrieved and analysed for forecasting. Monthly average insolation forecasts of the region around India's capital Delhi have been generated for next four years (i.e. January 2018 to December 2021) and presented in the form of contours obtained using marching square algorithm. The overall accuracy of forecasts in terms of R2 (0.9293), Root Mean Square Error (0.3529), Mean Absolute Error (0.2659) and Mean Absolute Percentage Error (6.556) was obtained. The ARIMA model forecasted the maximum insolation values in the months of May (6.52–6.76 KwH/m2/day in year 2018, 6.56–6.8 KwH/m2/day in 2019, 6.6–6.8 KwH/m2/day in 2020 and 6.6–6.84 KwH/m2/day in 2021) and Minimum in the months of January and December (3.2–3.7 KwH/m2/day in January 2018, 3.28–3.52 KwH/m2/day in December 2018). Insolation contours were analysed for identification of potential regions receiving maximum insolation as well as high average annual values of insolation for implementing efficient solar power generation projects. Parts of Haryana and Rajasthan region in study area were found most suitable for such projects.