A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms
Negar Rahimi, Sejun Park, Wonseok Choi, Byoungryul Oh, Sookyung Kim, Young-ho Cho, Sunghyun Ahn, Chulho Chong, Daewon Kim, Cheong Jin, Duehee Lee
Abstract
With increasing demand for energy, the penetration of alternative sources such as renewable energy in power grids has increased. Solar energy is one of the most common and well-known sources of energy in existing networks. But because of its non-stationary and non-linear characteristics, it needs to predict solar irradiance to provide more reliable Photovoltaic (PV) plants and manage the power of supply and demand. Although there are various methods to predict the solar irradiance. This paper gives the overview of recent studies with focus on solar irradiance forecasting with ensemble methods which are divided into two main categories: competitive and cooperative ensemble forecasting. In addition, parameter diversity and data diversity are considered as competitive ensemble forecasting and also preprocessing and post-processing are as cooperative ensemble forecasting. All these ensemble forecasting methods are investigated in this study. In the end, the conclusion has been drawn and the recommendations for future studies have been discussed.