Deep Learning Models for PV Power Forecasting: Review

Junfeng Yu, Xiaodong Li, Lei Yang, Linze Li, Zhichao Huang, Keyan Shen, Xu Yang, Xu Yang, Zhikang Xu, Dongying Zhang () and Shuai Du
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Junfeng Yu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Xiaodong Li: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Lei Yang: CTG Wuhan Science and Technology Innovation Park, China Three Gorges Corporation, Wuhan 430074, China
Linze Li: CTG Wuhan Science and Technology Innovation Park, China Three Gorges Corporation, Wuhan 430074, China
Zhichao Huang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Keyan Shen: Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
Xu Yang: Hubei Key Laboratory of Intelligent Yangtze and Hydroelectric Science, China Yangtze Power Co., Ltd., Yichang 443000, China
Xu Yang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zhikang Xu: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Dongying Zhang: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Shuai Du: School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2024, vol. 17, issue 16, 1-35

Abstract: Accurate forecasting of photovoltaic (PV) power is essential for grid scheduling and energy management. In recent years, deep learning technology has made significant progress in time-series forecasting, offering new solutions for PV power forecasting. This study provides a systematic review of deep learning models for PV power forecasting, concentrating on comparisons of the features, advantages, and limitations of different model architectures. First, we analyze the commonly used datasets for PV power forecasting. Additionally, we provide an overview of mainstream deep learning model architectures, including multilayer perceptron (MLP), recurrent neural networks (RNN), convolutional neural networks (CNN), and graph neural networks (GNN), and explain their fundamental principles and technical features. Moreover, we systematically organize the research progress of deep learning models based on different architectures for PV power forecasting. This study indicates that different deep learning model architectures have their own advantages in PV power forecasting. MLP models have strong nonlinear fitting capabilities, RNN models can capture long-term dependencies, CNN models can automatically extract local features, and GNN models have unique advantages for modeling spatiotemporal characteristics. This manuscript provides a comprehensive research survey for PV power forecasting using deep learning models, helping researchers and practitioners to gain a deeper understanding of the current applications, challenges, and opportunities of deep learning technology in this area.

Keywords: PV power forecasting; deep learning; MLP; CNN; RNN; GNN (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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