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

能源与人工智能(英文) 2021年第2期4卷 136-160页

作者： Ghadah Alkhayat Rashid Mehmood Department of Computer Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah 21589Saudi Arabia High Performance Computing Centre King Abdulaziz UniversityJeddah 21589Saudi Arabia

renewable energy is essential for planet *** energy output forecasting has a significant impact on making decisions related to operating and managing power *** prediction of renewable energy output is vital to ensure grid reliability and permanency and reduce the risk and cost of the energy market and *** 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 *** 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 *** core characteristics of all the reviewed works are summarised in tabular forms to enable methodological *** current challenges in the field and future research directions are *** 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 *** also find that probabilistic and multistep ahead forecasting methods are gaining more ***,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.

关键词： Deep learning renewable energy forecasting Solar energy Wind energy Taxonomy Hybrid methods

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Adaptive meta-learning extreme learning machine with golden eagle optimization and logistic map for forecasting the incomplete data of solar iradiance

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energy and AI 2023年第3期13卷 36-51页

作者： Sarunyoo Boriratrit Pradit Fuangfoo Chitchai Srithapon Rongrit Chatthaworn Department of Electrical Engineering Faculty of EngineeringKhon Kaen UniversityKhon KaenThailand Provincial Electricity Authority of Thailand(PEA) BangkokThailand Department of Electrical Engineering KTH Royal Institute of TechnologyStockholm11428Sweden Center for Alternative energy Research and Development Khon Kaen UniversityThailand

Solar energy has become crucial in producing electrical energy because it is inexhaustible and ***,its uncertain generation causes problems in power system ***,solar irradiance forecasting is significant for suitable controlling power system operation,organizing the transmission expansion planning,and dispatching power system ***,the forecasting performance can be decreased due to the unfitted prediction model and lacked *** deal with mentioned issues,this paper pro-poses Meta-Learning Extreme Learning Machine optimized with Golden Eagle Optimization and Logistic Map(MGEL-ELM)and the Same Datetime Interval Averaged Imputation algorithm(SAME)for improving the fore-casting performance of incomplete solar irradiance time series ***,the proposed method is not only imputing incomplete forecasting data but also achieving forecasting *** experimental result of fore-casting solar irradiance dataset in Thailand indicates that the proposed method can achieve the highest coeffi-cient of determination value up to 0.9307 compared to state-of-the-art ***,the proposed method consumes less forecasting time than the deep learning model.

关键词： Data imputation Golden eagle optimization Logistic maps Meta-learning extreme learning machine renewable energy forecasting

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