Regional Photovoltaic Power Forecasting Using Vector Autoregression Model in South Korea

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