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The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant