A Survey of Machine Learning Models in Renewable Energy Predictions

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 su...

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Published inApplied sciences Vol. 10; no. 17; p. 5975
Main Authors Lai, Jung-Pin, Chang, Yu-Ming, Chen, Chieh-Huang, Pai, Ping-Feng
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.09.2020
Subjects
Accuracy
Alternative energy sources
Artificial intelligence
Climate change
Deep learning
Energy resources
Environmental impact
Forecasting techniques
Fossil fuels
Investigations
Machine learning
Neural networks
prediction
renewable energy
Renewable resources
Solar energy
Wind power
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Summary: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.
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ISSN:2076-3417
2076-3417
DOI:10.3390/app10175975