TY - JOUR
T1 - A hybrid spatio-temporal forecasting of solar generating resources for grid integration
AU - Nam, Seung Beom
AU - Hur, Jin
N1 - Funding Information:
This research was supported by a 2018 Research Grant from Sangmyung University .
Publisher Copyright:
© 2019 Elsevier Ltd
PY - 2019/6/15
Y1 - 2019/6/15
N2 - 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.
AB - 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.
KW - Hybrid spatio-temporal forecasting
KW - Kriging
KW - Naïve Bayes classifier
KW - Solar generating resources
UR - http://www.scopus.com/inward/record.url?scp=85064977005&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2019.04.127
DO - 10.1016/j.energy.2019.04.127
M3 - Article
AN - SCOPUS:85064977005
SN - 0360-5442
VL - 177
SP - 503
EP - 510
JO - Energy
JF - Energy
ER -