Short-term peer-to-peer solar forecasting in a network of photovoltaic systems

Abstract Solar forecasting is a necessary component of economical realization of high penetration levels of photovoltaic (PV) systems. This paper presents a short term, intra-hour solar forecasting method. This “peer-to-peer” (P2P) forecasting method is based on the cross-correlation time lag between clear-sky index time series of pairs of PV-systems that are influenced by the (assumed) same cloud sequentially, with the feature that the forecast horizon (FH) can be set at a fixed value. The P2P forecasting algorithm was evaluated for 11 central PV-systems (out of 202) over a half year period from the 1 st of March through the 31 st of August 2015 using the forecast skill (FS) metric. Positive FS means improvement over reference clear-sky index persistence forecasting. The P2P forecasting method was evaluated over a subset of days with either high, all or low irradiance variability. The average forecast skill (avgFS) concerning forecast horizons between 5 and 8 min was 5.99%, −1.61% and −16.0% over these periods respectively, indicating the superior performance of the P2P method over persistence during the highly variable days, which are most interesting from the perspective of electricity grid management.