A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms

被引:39
作者
Rahimi, Negar [1 ]
Park, Sejun [1 ]
Choi, Wonseok [1 ]
Oh, Byoungryul [1 ]
Kim, Sookyung [1 ]
Cho, Young-ho [1 ]
Ahn, Sunghyun [1 ]
Chong, Chulho [1 ]
Kim, Daewon [1 ]
Jin, Cheong [2 ]
Lee, Duehee [1 ]
机构
[1] Konkuk Univ, Deptartment Elect & Elect Engn, Seoul, South Korea
[2] EINS S&C, Seoul, South Korea
关键词
Ensemble methods; Solar forecasting; Cooperative ensemble forecasting; EMPIRICAL MODE DECOMPOSITION; ARTIFICIAL NEURAL-NETWORKS; MACHINE LEARNING-METHODS; SUPPORT VECTOR MACHINE; WIND-SPEED; RADIATION; GENERATION; IRRADIANCE; OPTIMIZATION; PREDICTION;
D O I
10.1007/s42835-023-01378-2
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:719 / 733
页数:15
相关论文
共 102 条
[1]  
Acharya SK, 2018, P S KOREAN I COMMUNI, V8, P1310
[2]   Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression [J].
Ahmad, Muhammad Waseem ;
Mourshed, Monjur ;
Rezgui, Yacine .
ENERGY, 2018, 164 :465-474
[3]   A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization [J].
Ahmed, R. ;
Sreeram, V ;
Mishra, Y. ;
Arif, M. D. .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2020, 124
[4]   Variational mode decomposition based random forest model for solar radiation forecasting: New emerging machine learning technology [J].
Ali, Mumtaz ;
Prasad, Ramendra ;
Xiang, Yong ;
Khan, Mohsin ;
Farooque, Aitazaz Ahsan ;
Zong, Tianrui ;
Yaseen, Zaher Mundher .
ENERGY REPORTS, 2021, 7 :6700-6717
[5]   A systematic analysis of meteorological variables for PV output power estimation [J].
AlSkaif, Tarek ;
Dev, Soumyabrata ;
Visser, Lennard ;
Hossari, Murhaf ;
van Sark, Wilfried .
RENEWABLE ENERGY, 2020, 153 :12-22
[6]  
Arora Isha, 2020, Proceedings of Second International Conference on Inventive Research in Computing Applications (ICIRCA 2020), P675, DOI 10.1109/ICIRCA48905.2020.9182876
[7]   A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm [J].
Basaran, Kivanc ;
Ozcift, Akin ;
Kilinc, Deniz .
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2019, 44 (08) :7159-7171
[8]   Solar photovoltaic power forecasting using optimized modified extreme learning machine technique [J].
Behera, Manoja Kumar ;
Majumder, Irani ;
Nayak, Niranjan .
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2018, 21 (03) :428-438
[9]   A hybrid ARIMA-ANN method to forecast daily global solar radiation in three different cities in Morocco [J].
Belmahdi, Brahim ;
Louzazni, Mohamed ;
El Bouardi, Abdelmajid .
EUROPEAN PHYSICAL JOURNAL PLUS, 2020, 135 (11)
[10]   A gradient boosting approach to the Kaggle load forecasting competition [J].
Ben Taieb, Souhaib ;
Hyndman, Rob J. .
INTERNATIONAL JOURNAL OF FORECASTING, 2014, 30 (02) :382-394