Data-Driven Short-Term Solar Irradiance Forecasting Based on Information of Neighboring Sites
By Chao Huang, Long Wang, Loi Lei Lai. 2019
75 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Pollution, Renewable Energy, Sustainability and the Environment
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Chau Yuen
- Yuren Zhou
- Meng Zhang
- Billy Pik Lik Lau
- U-Xuan Tan
- Marakkalage Sumudu Hasala
- Naveed Ul L. Hassan
In Pollution
Citations in the corpus
- A survey of data fusion in smart city applications (2019, methodology): “Huang et al. [141] have proposed to use data driven algorithms such as ABB, SVM, BRT, and Lasso, in which the information from neighboring solar plants are combined to accurately predict the solar irradiance.”
- Short-Term Photovoltaic Power Forecasting Based on Long Short Term Memory Neural Network and Attention Mechanism (2019, background): “[15] proposed a data-driven framework based on fusing spatial and temporal information of the target PV station and its neighboring PV stations.”