Short-term Solar Irradiance Forecasting Using Exponential Smoothing State Space Model

We forecast high resolution solar irradiance time series using an exponential smoothing state space (ESSS) model. To stationarize the irradiance data before applying linear time series models, we propose a novel Fourier trend model and compare the performance with other popular trend models using residual analysis and the Kwiatkowski-Phillips-Schmidt-Shin (KPSS) stationarity test. Using the optimized Fourier trend, an ESSS model is implemented to forecast the stationary residual series of datasets from Singapore and Colorado, USA. To compare the performance with other time series models, autoregressive integrated moving average (ARIMA), linear exponential smoothing (LES), simple exponential smoothing (SES) and random walk (RW) models are tested using the same data. The simulation results show that the ESSS model has generally better performance than other time series forecasting models. To assess the reliability of the forecasting model in real-time applications, a complementary study of the forecasting 95% confidence interval and forecasting horizon of the ESSS model is proposed.