Missing-data imputation for solar irradiance forecasting in Thailand

This paper considers a solar irradiance forecasting using seasonal autoregressive integrated moving average models (SARIMA) which finds an application in solar power prediction for energy management. A time series prediction typically requires a complete set of historical data while the global horizontal irradiance (GHI) data in Thailand are frequently missing for consecutive days. This paper proposes a data-missing imputation technique using the mean value of GHI averaged over the data corresponding to the same weather type. The required weather classification consists of two steps: a seasonal segmentation based on detecting changes of monotonic properties of temperature and humidity time series, and a nonlinear support vector machine (SVM) that uses weather labels from the previous seasonal segmentation. Experimental results on the GHI data in Bangkok, Thailand shows that the proposed method significantly provides decreased imputation errors and leads to a better solar forecasting performance compared to other imputation techniques.