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Title: Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence
Authors: Chai, S
Niu, M
Xu, Z 
Lai, LL
Wong, KP
Keywords: Conditional forecast
Kernel density estimation
PV power forecast
Temporal dependence
Issue Date: 2016
Publisher: IEEE Computer Society
Source: IEEE Power and Energy Society General Meeting, 2016, v. 2016-November, 7741953 How to cite?
Abstract: The high penetration of solar PV generations brings about significant challenges for decision-makers of power system operation due to high volatility and uncertainty it involves. In recent years, it has been demonstrated by many researchers that the probabilistic interval forecast could significantly facilitate some decision-making cases, such as storage optimization, market bidding, reserves setting, as it can provide the uncertainty information associated with the point estimations. This paper proposes a nonparametric conditional interval forecast method for PV power generation which can capture the interdependence among the real power output and their point forecasts within all forecasting horizons of interests. The proposed model is tested using the dataset of PV generation power measurements and day-ahead point forecasts in Belgium. The results based on reliability and interval score performance metrics illustrate the effectiveness of the proposed model.
Description: 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, US, 17-21 July 2016
ISBN: 9781509041688
ISSN: 1944-9925
DOI: 10.1109/PESGM.2016.7741953
Appears in Collections:Conference Paper

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