Paper Info
Title
Data-Driven Short-Term Solar Irradiance Forecasting Based On Information Of Neighboring Sites
Abstract
Short-term forecasting of solar irradiance at a targeted site with consideration of its time series and measurements at neighboring sites is studied in this paper. A data-driven framework for forecasting solar irradiance based on fusing spatial and temporal information is proposed. In the framework, data-driven approaches including boosted regression trees (BRT), artificial neural network, support vector machine, and least absolute shrinkage and selection operator are applied to model spatial dependence among solar irradiance time series thereby to generate forecasts of solar irradiance at the targeted site. A comprehensive comparison among data-driven forecasting models is performed using 30-min averaged data of recent two years. Moreover, benchmarking models including scaled persistence model, autoregressive model, and autoregressive exogenous model are employed to further validate the effectiveness of data-driven forecasting models. Computational results of multiple-steps ahead forecasting demonstrate that the BRT model gives the best performance with the lowest normalized root mean squared error of 18.4%, 24.3%, 27.9%, and 30.6% for forecasting horizons of 30, 60, 90, and 120-min, respectively.
Year
DOI
Venue
2019
10.1109/TIE.2018.2856199
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Keywords
DocType
Volume
Boosted regression trees, forecasting, solar irradiance, data-driven approaches
Journal
66
Issue
ISSN
Citations 
12
0278-0046
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Chao Huang102.37
Long Wang25112.95
Loi Lei Lai313538.72