A taxonomy of short-term solar power forecasting: Classifications focused on climatic conditions and input data

Abstract

A review of the state-of-the-art in short-term Solar Power Forecasting (SPF) methodologies is presented in this paper. Over the last few years, developing and improving solar forecasting models has been the main focus of researchers, considering the need to efficiently increase their forecasting accuracy. Forecasting models aim to be used as an efficient tool to help with the stability and control of energy systems and electricity markets. Intending to further comprehend the factors affecting the quality of SPF models, this paper focuses on short-term solar forecasting methodologies since they pose a crucial role in the daily operation and scheduling of power systems, since they focus on forecasting horizons typically ranging from 1 h to 1 day. The reviewed works are classified according to the climatic conditions, technical characteristics, and the forecasting errors of the different methodologies, providing readers with information over various different cases of SPF. Considering the need to improve the SPF efficiency, such classifications allow for important comparative conclusions to be drawn, depending on the location of each case and the meteorological data available. Future directions in the field of short-term solar power forecasting are proposed considering the increasing development of SPF models' architecture and their field of focus.