Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects

被引:100
作者
Benti, Natei Ermias [1 ]
Chaka, Mesfin Diro [1 ]
Semie, Addisu Gezahegn [1 ]
机构
[1] Addis Ababa Univ, Coll Nat & Computat Sci, Computat Data Sci Program, POB 1176, Addis Ababa, Ethiopia
关键词
accurate predictions; deep learning; energy management; machine learning; renewable energy forecasting; GLOBAL SOLAR-RADIATION; SUPPORT VECTOR MACHINE; WAVELET NEURAL-NETWORK; WIND-SPEED PREDICTION; POWER PREDICTION; SUNSHINE DURATION; MODEL; REGRESSION; SYSTEM; SVM;
D O I
10.3390/su15097087
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This article presents a review of current advances and prospects in the field of forecasting renewable energy generation using machine learning (ML) and deep learning (DL) techniques. With the increasing penetration of renewable energy sources (RES) into the electricity grid, accurate forecasting of their generation becomes crucial for efficient grid operation and energy management. Traditional forecasting methods have limitations, and thus ML and DL algorithms have gained popularity due to their ability to learn complex relationships from data and provide accurate predictions. This paper reviews the different approaches and models that have been used for renewable energy forecasting and discusses their strengths and limitations. It also highlights the challenges and future research directions in the field, such as dealing with uncertainty and variability in renewable energy generation, data availability, and model interpretability. Finally, this paper emphasizes the importance of developing robust and accurate renewable energy forecasting models to enable the integration of RES into the electricity grid and facilitate the transition towards a sustainable energy future.
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页数:33
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