Practical approach for sub-hourly and hourly prediction of PV power output

This paper proposes a practical and reliable approach for the prediction of photovoltaic power generation using solar irradiance as the input. Solar irradiance is modeled as the sum of a deterministic component and a Gaussian noise signal. The solar irradiance on a partly cloudy day is forecasted by Kalman filtering. The shaping filter for the Gaussian noise is calculated using spectral analysis and an autoregressive moving average (ARMA) model. The results of the two approaches are compared with the measured irradiance at a PV generating facility within an electric utility company. The results show that better estimates are obtained using spectral analysis than those obtained with the ARMA model, particularly for lower sampling rates.