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Energies 2019, 12(6), 1011; https://doi.org/10.3390/en12061011

Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method

1
Department of Engineering, University of Napoli Parthenope, 80143 Naples, Italy
2
Department of Electrical Engineering and Information Technologies, University of Napoli Federico II, 80125 Naples, Italy
*
Author to whom correspondence should be addressed.
Received: 10 February 2019 / Revised: 4 March 2019 / Accepted: 11 March 2019 / Published: 15 March 2019
(This article belongs to the Special Issue Ensemble Forecasting Applied to Power Systems)
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Abstract

Accurate probabilistic forecasts of renewable generation are drivers for operational and management excellence in modern power systems and for the sustainable integration of green energy. The combination of forecasts provided by different individual models may allow increasing the accuracy of predictions; however, in contrast to point forecast combination, for which the simple weighted averaging is often a plausible solution, combining probabilistic forecasts is a much more challenging task. This paper aims at developing a new ensemble method for photovoltaic (PV) power forecasting, which combines the outcomes of three underlying probabilistic models (quantile k-nearest neighbors, quantile regression forests, and quantile regression) through a weighted quantile combination. Due to the challenges in combining probabilistic forecasts, the paper presents different combination strategies; the competing strategies are based on unconstrained, constrained, and regularized optimization problems for estimating the weights. The competing strategies are compared to individual forecasts and to several benchmarks, using the data published during the Global Energy Forecasting Competition 2014. Numerical experiments were run in MATLAB and R environments; the results suggest that unconstrained and Least Absolute Shrinkage and Selection Operator (LASSO)-regularized strategies exhibit the best performances for the datasets under study, outperforming the best competitors by 2.5 to 9% of the Pinball Score. View Full-Text
Keywords: forecast combination; photovoltaic power; probabilistic forecasting forecast combination; photovoltaic power; probabilistic forecasting
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Bracale, A.; Carpinelli, G.; De Falco, P. Developing and Comparing Different Strategies for Combining Probabilistic Photovoltaic Power Forecasts in an Ensemble Method. Energies 2019, 12, 1011.

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