A review and taxonomy of wind and solar energy forecasting methods based on deep learning

作     者：Ghadah Alkhayat Rashid Mehmood 

作者机构：Department of Computer Information SystemsFaculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia High Performance Computing CentreKing Abdulaziz UniversityJeddah 21589Saudi Arabia 

出 版 物：《能源与人工智能(英文)》 (Energy and AI)

年 卷 期：2021年第4卷第2期

页      面：136-160页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 

主　　题：Deep learning Renewable energy forecasting Solar energy Wind energy Taxonomy Hybrid methods 

摘      要：Renewable energy is essential for planet sustainability.Renewable energy output forecasting has a significant impact on making decisions related to operating and managing power systems.Accurate prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and systems.Deep learning’s recent success in many applications has attracted researchers to this field and its promising potential is manifested in the richness of the proposed methods and the increasing number of publications.To facilitate further research and development in this area,this paper provides a review of deep learning-based solar and wind energy forecasting research published during the last five years discussing extensively the data and datasets used in the reviewed works,the data pre-processing methods,deterministic and probabilistic methods,and evaluation and comparison methods.The core characteristics of all the reviewed works are summarised in tabular forms to enable methodological comparisons.The current challenges in the field and future research directions are given.The trends show that hybrid forecasting models are the most used in this field followed by Recurrent Neural Network models including Long Short-Term Memory and Gated Recurrent Unit,and in the third place Convolutional Neural Networks.We also find that probabilistic and multistep ahead forecasting methods are gaining more attention.Moreover,we devise a broad taxonomy of the research using the key insights gained from this extensive review,the taxonomy we believe will be vital in understanding the cutting-edge and accelerating innovation in this field.