Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea

A. Hyun Jung, Dong Hyun Lee, Jin Young Kim, Chang Ki Kim, Hyun Goo Kim, Yung Seop Lee

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Renewable energy forecasting is a key for efficient resource use in terms of power generation and safe grid control. In this study, we investigated a short-term statistical forecasting model with 1 to 3 h horizons using photovoltaic operation data from 215 power plants throughout South Korea. A vector autoregression (VAR) model-based regional photovoltaic power forecasting system is proposed for seven clusters of power plants in South Korea. This method showed better predictability than the autoregressive integrated moving average (ARIMA) model. The normalized root-mean-square errors of hourly photovoltaic generation predictions obtained from VAR (ARIMA) were 8.5–10.9% (9.8–13.0%) and 18.5–22.8% (21.3–26.3%) for 1 h and 3 h horizon, respectively, at 215 power plants. The coefficient of determination, R2 was higher for VAR, at 4–5%, than ARIMA. The VAR model had greater accuracy than ARIMA. This will be useful for economical and efficient grid management.

Original languageEnglish
Article number7853
JournalEnergies
Volume15
Issue number21
DOIs
StatePublished - Nov 2022

Keywords

  • ARIMA
  • cluster analysis
  • photovoltaic power
  • regional prediction
  • solar irradiance
  • VAR

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