MARS model for prediction of short- and long-term global solar radiation
Edited book (chapter)
Chapter Title | MARS model for prediction of short- and long-term global solar radiation |
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Book Chapter Category | Edited book (chapter) |
ERA Publisher ID | 1821 |
Book Title | Predictive Modelling for Energy Management and Power Systems Engineering |
Authors | Balalla, Dilki T., Nguyen-Huy, Thong and Deo, Ravinesh |
Editors | Deo, Ravinesh, Roy, Sanjiban Sekhar and Samui, Pijush |
Page Range | 391-436 |
Chapter Number | 13 |
Number of Pages | 46 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISBN | 9780128177723 |
Digital Object Identifier (DOI) | https://doi.org/10.1016/B978-0-12-817772-3.00013-6 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/B9780128177723000136 |
Abstract | This chapter applies a machine learning approach based on Multivariate Adaptive Regression Splines (MARS) algorithm for developing solar radiation estimation and forecasting models for regional Queensland. First, a short-term (daily) global solar radiation model is constructed using the MARS algorithm considering the nonlinear behavior of surface-level solar radiation with its plausible list of predictor variables. Second, a long-term (monthly) global solar radiation model is built using the MARS algorithm mainly to test the tool that can later be used for solar energy assessment over a long-term period and considering seasonal climatic cycles. The accuracy of the MARS model with respect to an alternative data-driven framework is evaluated using Autoregressive Integrated Moving Average at both time horizons. The results indicate that the MARS-based solar radiation forecast can be applied in Central Queensland in both short-term and long-term forecasting scenarios and is a valuable tool for future solar energy projects. |
Keywords | autoregressive integrated moving average; decision support tool; machine learning; Solar radiation forecast |
ANZSRC Field of Research 2020 | 460207. Modelling and simulation |
490511. Time series and spatial modelling | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Southern Queensland |
Centre for Applied Climate Sciences | |
Vietnam Academy of Science and Technology, Vietnam |
https://research.usq.edu.au/item/qq9vz/mars-model-for-prediction-of-short-and-long-term-global-solar-radiation
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