Semantic Scholar Open Access 2020 161 sitasi

A Survey of Machine Learning Models in Renewable Energy Predictions

Jung-Pin Lai Yu-Ming Chang Chieh-Huang Chen Ping-Feng Pai

Lihat Sumber DOI

Abstrak

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.

Topik & Kata Kunci

Computer Science

Penulis (4)

Jung-Pin Lai

Yu-Ming Chang

Chieh-Huang Chen

Ping-Feng Pai

Format Sitasi

APA MLA BibTeX

Lai, J., Chang, Y., Chen, C., Pai, P. (2020). A Survey of Machine Learning Models in Renewable Energy Predictions. https://doi.org/10.3390/app10175975

Akses Cepat

Lihat di Sumber doi.org/10.3390/app10175975

Informasi Jurnal

Tahun Terbit: 2020
Bahasa: en
Total Sitasi: 161×
Sumber Database: Semantic Scholar
DOI: 10.3390/app10175975
Akses: Open Access ✓