Improving Prediction Intervals Using Measured Solar Power with a Multi-Objective Approach

Ricardo Aler, Javier Huertas-Tato, José M. Valls and Inés M. Galván
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Ricardo Aler: Computer Science Department, Universidad Carlos III de Madrid, 30 Avenida Universidad, Leganes, 28911 Madrid, Spain
Javier Huertas-Tato: Computer Science Department, Universidad Carlos III de Madrid, 30 Avenida Universidad, Leganes, 28911 Madrid, Spain
José M. Valls: Computer Science Department, Universidad Carlos III de Madrid, 30 Avenida Universidad, Leganes, 28911 Madrid, Spain
Inés M. Galván: Computer Science Department, Universidad Carlos III de Madrid, 30 Avenida Universidad, Leganes, 28911 Madrid, Spain

Energies, 2019, vol. 12, issue 24, 1-19

Abstract: Prediction Intervals are pairs of lower and upper bounds on point forecasts and are useful to take into account the uncertainty on predictions. This article studies the influence of using measured solar power, available at prediction time, on the quality of prediction intervals. While previous studies have suggested that using measured variables can improve point forecasts, not much research has been done on the usefulness of that additional information, so that prediction intervals with less uncertainty can be obtained. With this aim, a multi-objective particle swarm optimization method was used to train neural networks whose outputs are the interval bounds. The inputs to the network used measured solar power in addition to hourly meteorological forecasts. This study was carried out on data from three different locations and for five forecast horizons, from 1 to 5 h. The results were compared with two benchmark methods (quantile regression and quantile regression forests). The Wilcoxon test was used to assess statistical significance. The results show that using measured power reduces the uncertainty associated to the prediction intervals, but mainly for the short forecasting horizons.

Keywords: uncertainty solar energy forecasting; prediction intervals; neural networks; multi-objective particle swarm optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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