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

By B. Elsinga, W. van Sark. 2017

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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 1st of March through the 31st 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 8min 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.

Subfields: Artificial Intelligence, Pollution, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/df0219a61c201c9f95eba3fb1d9d4ac995320980