A review of deep learning for renewable energy forecasting

By Huaizhi Wang, Zhenxing Lei, Xian Zhang, Bin Zhou, Jianchun Peng. Energy Conversion and Management, 2019

1,009 citations recorded by the source corpus.

Abstract 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.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f3a7ad95a0992361b6937946dd6e3487489ea83b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. C. D. de Silva
2. Min Xia
3. Xiandong Ma
4. Haidong Shao

In Electrical and Electronic Engineering

1. C. D. de Silva
2. Min Xia
3. Xiandong Ma
4. Haidong Shao

Citations in the corpus

• A Stacked GRU-RNN-Based Approach for Predicting Renewable Energy and Electricity Load for Smart Grid Operation (2021, background): “More recently, deep learning, with its superior capability in discovering the inherent nonlinear features instead of using handcraft features, has achieved remarkable performance in many prediction applications including energy production and load forecasting [17].”
• Deep Learning for Time Series Forecasting: A Survey (2020, neutral): “156,157,195 Deep-learning techniques are also widely applied to architecture, as can be seen in the in-depth study conducted by the authors in Ref.”