A hybrid spatio-temporal forecasting of solar generating resources for grid integration. (15th June 2019)

• Record Type: Journal Article
• Authors: Nam, SeungBeom; Hur, Jin
• Is Part Of: Energy. Volume 177(2019)
• Page Start: 503
• Page End: 510
• Publication Date: 2019-06-15
• Subjects: Solar generating resources -- Hybrid spatio-temporal forecasting -- Kriging -- Naïve Bayes classifier; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.04.127
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: 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 naïve 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. Highlights: New energy policy and trend in South Korea is introduced. A new hybrid Spatial-Temporal forecasting method is proposed. A probabilistic forecasting method base on NBC is implemented. A spatial modelling and analysis based on Kriging techniques is implemented. An empirical data with high resolution is applied to the proposed forecasting model.