Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models

Abstract

One of the key enabling technologies for integrating solar energy into the grid is short-range forecasting. Two issues have emerged in the literature. The first has to do with the relative merits of physics-based versus time series models. The second is how to parameterize short-term variability. One promising approach is time-varying parameter models. Time series models can be updated using moving windows. Meteorological models can be adjusted to match the data more closely. This study evaluates several types of models over forecast horizons ranging from 15 min to 4 h, using data from two locations in the United States. The Weather Research Forecast (WRF) model is a state-of-the art numerical weather prediction system. The Dynamic Integrated Forecast (DICast) system combines meteorological models with statistical adjustments. The primary time series model is the ARIMA. Several other techniques are also tested, cloud advection, smart persistence forecasts and regression trees. Each type of model is found to have particular strengths and weaknesses. Among time series models, ARIMAs with time-varying coefficients are superior to fixed coefficient methods. In a direct comparison of meteorological and time series models, the ARIMA is more accurate at short horizons, while the numerical weather prediction models are more accurate as the horizon extends. The convergence point, at which the two methods achieve similar degrees of accuracy, is in the range of 1–3 h. Adjusting meteorological model output using statistical corrections at regular intervals, as in the DICast, consistently outperforms the alternatives at horizons of 2–4 h, and is highly competitive at 1 h.