Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/65331
Title: Probabilistic forecasting of photovoltaic generation : an efficient statistical approach
Authors: Wan, C
Lin, J
Song, Y
Xu, Z 
Yang, G
Keywords: Extreme learning machine
Prediction intervals
Probabilistic forecasting
PV power
Quantile regression
Issue Date: 2017
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on power systems, 2017, v. 32, no. 3, p. 2471-2472 How to cite?
Journal: IEEE transactions on power systems 
Abstract: A novel efficient probabilistic forecasting approach is proposed to accurately quantify the variability and uncertainty of the power production from photovoltaic (PV) systems. Distinguished from most existing models, a linear programming based prediction interval construction model for PV power generation is constructed based on extreme learning machine and quantile regression, featuring high reliability and computational efficiency. The proposed approach is validated through the numerical studies on PV data from Denmark.
URI: http://hdl.handle.net/10397/65331
ISSN: 0885-8950
EISSN: 1558-0679
DOI: 10.1109/TPWRS.2016.2608740
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