Short-term solar power forecasting using satellite images. (27th August 2021)

• Record Type: Journal Article
• Authors: Devi, A. Shobana; Maragatham, G.; Boopathi, K.; Prabu, M.R.
• Is Part Of: International journal of powertrains. Volume 10:Number 2(2021)
• Page Start: 125
• Page End: 142
• Publication Date: 2021-08-27
• Subjects: clear sky models -- cloud index -- solar irradiance -- satellite images -- solar power forecasting; Motor vehicles -- Power trains -- Periodicals; 629.25
• Languages: English
• ISSNs: 1742-4267

Abstract

Solar irradiance forecasting will turn into a major challenge in the future integration of solar energy resources into existing structures of energy supply. There are squeezing requirements for approaches to accurately estimate the movement of the cloud that legitimately impacts solar power output stability. As a degree of cloudiness is concerned, cloud index images are calculated from the satellite images to derive radiation from satellite data. Solar power is predicted using a forecasting model from the predicted global horizontal irradiance (GHI). This paper focuses on forecasting the solar power of every one and half an hour using the long short-term memory (LSTM) technique. The forecasting results are compared with the actual measured 250 MW solar plant power. The experimental findings considerably increase the assessment quality of cloud movement within 15 to 90 mins that is satisfactory for grid operators to make necessary action to improve the unpredictability of solar power.