A Survey of Machine Learning Models in Renewable Energy Predictions
Source abstract
Abstract
The use of renewable energy to reduce the effects of climate change and global warming has become an increasing trend. In order to improve the prediction ability of renewable energy, various prediction techniques have been developed. The aims of this review are illustrated as follows. First, this survey attempts to provide a review and analysis of machine-learning models in renewable-energy predictions. Secondly, this study depicts procedures, including data pre-processing techniques, parameter selection algorithms, and prediction performance measurements, used in machine-learning models for renewable-energy predictions. Thirdly, the analysis of sources of renewable energy, values of the mean absolute percentage error, and values of the coefficient of determination were conducted. Finally, some possible potential opportunities for future work were provided at end of this survey.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Solar Radiation and Photovoltaics · Energy Load and Power Forecasting · Energy and Environment Impacts
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
PhD Program in Strategy and Development of Emerging Industries, National Chi Nan University, Nantou 54561, Taiwan
Department of Culinary Arts and Hotel Management, HungKuang University, Taichung 43302, Taiwan
Department of Information Management, National Chi Nan University, Nantou 54561, Taiwan
Funding
Ministry of Science and Technology, Taiwan
References (100+)
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