Solar Power Generation Forecast Model Using Seasonal ARIMA

SARIMA 모형을 이용한 태양광 발전량 예보 모형 구축

Abstract

New and renewable energy forecasts are key technology to reduce the annual operating cost of new and renewable facilities, and accuracy of forecasts is paramount. In this study, we intend to build a model for the prediction of short-term solar power generation for 1 hour to 3 hours. To this end, this study applied two time series technique, ARIMA model without considering seasonality and SARIMA model with considering seasonality, comparing which technique has better predictive accuracy. Comparing predicted errors by MAE measures of solar power generation for 1 hour to 3 hours at four locations, the solar power forecast model using ARIMA was better in terms of predictive accuracy than the solar power forecast model using SARIMA. On the other hand, a comparison of predicted error by RMSE measures resulted in a solar power forecast model using SARIMA being better in terms of predictive accuracy than a solar power forecast model using ARIMA.

Fig. 1 Overview of training data set and test data set for solar power data

Fig. 2 The actual value and the predicted value(from 1 hour to 3 hour) of solar power generation at station 4 using ARIMA model

Fig. 3 The actual value and the predicted value (from 1 hour to 3 hour) of solar power generation at station 4 using SARIMA model

Table 1 ARIMA model for 4 stations

Table 2 SARIMA model for 4 stations

Table 3 Comparison of MAE between ARIMA model and SARIMA model

Table 4 Comparison of RMSE between ARIMA model and SARIMA model