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Energies 2016, 9(10), 829; doi:10.3390/en9100829

Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power

1
Centre for Translational Data Science, University of Sydney, Sydney, NSW 2006, Australia
2
School of Information Technologies, University of Sydney, Sydney, NSW 2006, Australia
*
Author to whom correspondence should be addressed.
Academic Editors: José C. Riquelme, Alicia Troncoso and Francisco Martínez-Álvarez
Received: 31 July 2016 / Revised: 28 September 2016 / Accepted: 30 September 2016 / Published: 17 October 2016
(This article belongs to the Special Issue Energy Time Series Forecasting)
View Full-Text   |   Download PDF [5337 KB, uploaded 21 October 2016]   |  

Abstract

Solar energy generated from PhotoVoltaic (PV) systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the electricity generated by PV systems. In this paper we consider 2D-interval forecasts, where the goal is to predict summary statistics for the distribution of the PV power values in a future time interval. 2D-interval forecasts have been recently introduced, and they are more suitable than point forecasts for applications where the predicted variable has a high variability. We propose a method called NNE2D that combines variable selection based on mutual information and an ensemble of neural networks, to compute 2D-interval forecasts, where the two interval boundaries are expressed in terms of percentiles. NNE2D was evaluated for univariate prediction of Australian solar PV power data for two years. The results show that it is a promising method, outperforming persistence baselines and other methods used for comparison in terms of accuracy and coverage probability. View Full-Text
Keywords: solar power prediction; interval forecasts; 2D-interval forecasts; ensembles of neural networks; mutual information; support vector regression solar power prediction; interval forecasts; 2D-interval forecasts; ensembles of neural networks; mutual information; support vector regression
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Rana, M.; Koprinska, I. Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power. Energies 2016, 9, 829.

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