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http://dx.doi.org/10.5351/KJAS.2018.31.1.139

A study on solar energy forecasting based on time series models  

Lee, Keunho (Department of Applied Statistics, Chung-Ang University)
Son, Heung-gu (Department of Aviation, The Korea Transport Institute)
Kim, Sahm (Department of Applied Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.1, 2018 , pp. 139-153 More about this Journal
Abstract
This paper investigates solar power forecasting based on several time series models. First, we consider weather variables that influence forecasting procedures as well as compare forecasting accuracies between time series models such as ARIMAX, Holt-Winters and Artificial Neural Network (ANN) models. The results show that ten models forecasting 24hour data have better performance than single models for 24 hours.
Keywords
ARIMAX; Artificial Neural Network; weather variables; solar power;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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