Multitime-Scale Data-Driven Spatio-Temporal Forecast of Photovoltaic Generation


The increasing penetration of stochastic photovoltaic (PV) generation in electric power systems poses vital challenges to system operators. To ensure reliable operation of power systems, correct forecasting of PV power production is essential. During this paper, we tend to propose a unique multitime-scale knowledge-driven forecast model to enhance the accuracy of short-term PV power production. This model leverages each spatial and temporal correlations among neighboring solar sites, and is shown to own improved performance compared to the conventional persistence (PSS) model. The tradeoff between computation cost and improved forecast quality is studied using real datasets from PV sites in California and Colorado.

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