A review of deep learning for renewable energy forecasting

In this section, you can access to the latest technical information related to the FUTURE project topic.

As renewable energy becomes increasingly popular in the global electric energy grid, improving the accuracy of renewable energy forecasting is critical to power system planning, management, and operations. However, this is a challenging task due to the intermittent and chaotic nature of renewable energy data. To date, various methods have been developed, including physical models, statistical methods, artificial intelligence techniques, and their hybrids to improve the forecasting accuracy of renewable energy. Among them, deep learning, as a promising type of machine learning capable for discovering the inherent nonlinear features and high-level invariant structures in data, has been frequently reported in the literature. This paper provides a comprehensive and extensive review of renewable energy forecasting methods based on deep learning to explore its effectiveness, efficiency and application potential. We divide the existing deterministic and probabilistic forecasting methods based on deep learning into four groups, namely deep belief network, stack auto-encoder, deep recurrent neural network and others. We also dissect the feasible data preprocessing techniques and error post-correction methods to improve the forecasting accuracy. Extensive analysis and discussion of various deep learning based forecasting methods are given. Finally, we explore the current research activities, challenges and potential future research directions in this topic.

» Author: Huaizhi Wang, Zhenxing Lei, Xian Zhang, Bin Zhou, Jianchun Peng

» Publication Date: 15/10/2019

» More Information

« Go to Technological Watch

AIMPLAS Instituto Tecnológico del Plástico

C/ Gustave Eiffel, 4
(València Parc Tecnològic) - 46980
PATERNA (Valencia) - SPAIN

PHONE

(+34) 96 136 60 40

EMAIL

Project Management department - Sustainability and Industrial Recovery
life-future-project@aimplas.es