Solar power prediction in different forecasting horizons using machine learning and time series techniques
Pun, Kesh Bahadur Basnet, Saurav Man Singh Jewell, Ward T.
Pun, Kesh Bahadur
Basnet, Saurav Man Singh
Jewell, Ward T.
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Authors
Pun, Kesh Bahadur
Basnet, Saurav Man Singh
Jewell, Ward T.
Basnet, Saurav Man Singh
Jewell, Ward T.
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Advisors
Original Date
Digitization Date
Issue Date
2021-07-01
Type
Conference paper
Genre
Keywords
Renewable energy sources,Time series analysis,Machine learning,Solar energy,Predictive models,Performance analysis,Solar power generation
Subjects (LCSH)
Citation
Pun, K., Basnet, S. M. S., & Jewell, W. (2021). Solar power prediction in different forecasting horizons using machine learning and time series techniques. Paper presented at the 2021 IEEE Conference on Technologies for Sustainability, SusTech 2021, doi:10.1109/SusTech51236.2021.9467464
Abstract
Solar power generation is highly intermittent, nonlinear, and variable in nature. The increase in penetration level of solar energy resources poses technical challenges. An accurate forecasting model is crucial to minimizing these technical issues. Therefore, choosing the right forecasting technique for the right forecasting horizon is vital. In this study, the performance analysis of machine learning and time series forecasting techniques for various forecasting horizons has been investigated. Its accuracy, root mean square error (RMSE), and mean absolute error (MAE) have been compared to other techniques.
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Publisher
IEEE
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Series
2021 IEEE Conference on Technologies for Sustainability (SusTech);