Journal
ENERGY
Volume 177, Issue -, Pages 503-510Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2019.04.127
Keywords
Solar generating resources; Hybrid spatio-temporal forecasting; Kriging; Naive Bayes classifier
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Funding
- Sangmyung University
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Recently, the installed solar generating resources have been increasing rapidly. Consequently, forecasting for solar generating resources are becoming an important work to integrate utility-scale solar generating resources into power systems. As solar generating resources are variable, uncontrollable, and uncertain, accurate and reliable forecasting enables higher penetrations of solar generating resources to be deployed on the electrical power grid. Accurate forecasting of solar resources contributes to evaluation of system reserves over large geographic area and to transmission system planning. To increase the penetration of solar generating resources on the electric power grid, the accurate power forecasting of geographically distributed solar generating resources is needed. In this paper, we propose a hybrid spatio-temporal forecasting of solar generating resources based on the naive Bayesian classifier approach and spatial modelling approach. To validate our forecasting model, we use the empirical data from the practical solar farms in South Korea. (C) 2019 Elsevier Ltd. All rights reserved.
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