Energy Supply, Its Demand and Security Issues for Developed and Emerging Economies

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Solar Energy 112 (2015) 68–77 Short-term reforecasting of power output from a 48 MWe solar PV plant Yinghao Chu, Bryan Urquhart, Seyyed M.I. Gohari, Hugo T.C. Pedro, Jan Kleissl,
Carlos F.M. Coimbra ⇑
Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Renewable Resource Integration, University of
California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093, USA
Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, Center for Energy Research, University of California, San Diego,
9500 Gilman Drive, La Jolla, CA 92093, USA
Received 31 March 2014; received in revised form 14 November 2014; accepted 17 November 2014

Communicated by: Associate Editor Frank Vignola

A smart, real-time reforecast method is applied to the intra-hour prediction of power generated by a 48 MWe photovoltaic (PV) plant.
This reforecasting method is developed based on artificial neural network (ANN) optimization schemes and is employed to improve the performance of three baseline prediction models: (1) a physical deterministic model based on cloud tracking techniques; (2) an autoregressive moving average (ARMA) model; and (3) a k-th Nearest Neighbor (kNN) model. Using the measured power data from the
PV plant, the performance of all forecasts is assessed in terms of common error statistics (mean bias, mean absolute error and root mean square error) and forecast skill over the reference persistence model. With the reforecasting method, the forecast skills of the three baseline models are significantly increased for time horizons of 5, 10, and 15 min. This study demonstrates the effectiveness of the optimized reforecasting method in reducing learnable errors produced by a diverse set of forecast methodologies.

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