Rahimi Negar, Park Sejun, Choi Wonseok, Oh Byoungryul, Kim Sookyung, Cho Young-Ho, Ahn Sunghyun, Chong Chulho, Kim Daewon, Jin Cheong, Lee Duehee
Deptartment of Electrical and Electronic Engineering, Konkuk University, Seoul, South Korea.
EINS S&C, Seoul, South Korea.
J Electr Eng Technol. 2023;18(2):719-733. doi: 10.1007/s42835-023-01378-2. Epub 2023 Jan 12.
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.
随着能源需求的不断增加，可再生能源等替代能源在电网中的渗透率也在提高。太阳能是现有网络中最常见且广为人知的能源之一。但由于其非平稳和非线性特性，需要预测太阳辐照度，以提供更可靠的光伏电站，并管理供需电力。尽管有多种预测太阳辐照度的方法。本文概述了近期的研究，重点是使用集成方法进行太阳辐照度预测，这些方法主要分为两类：竞争性和协作性集成预测。此外，参数多样性和数据多样性被视为竞争性集成预测，预处理和后处理则被视为协作性集成预测。本研究对所有这些集成预测方法进行了调查。最后得出了结论，并讨论了对未来研究的建议。
