Solar power prediction in different forecasting horizons using machine learning and time series techniques
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Authors
Pun, Kesh Bahadur
Basnet, Saurav Man Singh
Jewell, Ward T.
Advisors
Issue Date
2021-07-01
Type
Conference paper
Keywords
Renewable energy sources , Time series analysis , Machine learning , Solar energy , Predictive models , Performance analysis , Solar power generation
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.
Table of Contents
Description
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Publisher
IEEE
Journal
Book Title
Series
2021 IEEE Conference on Technologies for Sustainability (SusTech);