Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

Songjian Chai; Ming Niu; Zhao Xu; Loi Lei Lai; Kit Po Wong

doi:10.1109/PESGM.2016.7741953

Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

Songjian Chai
, Ming Niu
, Zhao Xu
, Loi Lei Lai
, Kit Po Wong

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

21 Citations (Scopus)

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.

Original language	English
Title of host publication	IEEE Power and Energy Society General Meeting
Editors	Paula Traynor
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Volume	2016-November
ISBN (Electronic)	9781509041688
DOIs	https://doi.org/10.1109/PESGM.2016.7741953
Publication status	Published - 10 Nov 2016
Event	2016 IEEE Power and Energy Society General Meeting, PESGM 2016 - Boston, United States Duration: 17 Jul 2016 → 21 Jul 2016

Conference

Conference	2016 IEEE Power and Energy Society General Meeting, PESGM 2016
Country/Territory	United States
City	Boston
Period	17/07/16 → 21/07/16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.1109/PESGM.2016.7741953

Cite this

Chai, S., Niu, M., Xu, Z., Lai, L. L., & Wong, K. P. (2016). Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence. In P. Traynor (Ed.), IEEE Power and Energy Society General Meeting (Vol. 2016-November). Article 7741953 IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/PESGM.2016.7741953

@inproceedings{e3b830fcaf8f4b019b70744ecb836c27,

title = "Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence",

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.",

keywords = "Conditional forecast, Copula, Kernel density estimation, PV power forecast, Temporal dependence",

author = "Songjian Chai and Ming Niu and Zhao Xu and Lai, \{Loi Lei\} and Wong, \{Kit Po\}",

year = "2016",

month = nov,

day = "10",

doi = "10.1109/PESGM.2016.7741953",

language = "English",

volume = "2016-November",

editor = "Traynor, \{Paula \}",

booktitle = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2016 IEEE Power and Energy Society General Meeting, PESGM 2016 ; Conference date: 17-07-2016 Through 21-07-2016",

}

Chai, S, Niu, M, Xu, Z, Lai, LL & Wong, KP 2016, Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence. in P Traynor (ed.), IEEE Power and Energy Society General Meeting. vol. 2016-November, 7741953, IEEE, Institute of Electrical and Electronics Engineers, 2016 IEEE Power and Energy Society General Meeting, PESGM 2016, Boston, United States, 17/07/16. https://doi.org/10.1109/PESGM.2016.7741953

Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence. / Chai, Songjian; Niu, Ming; Xu, Zhao et al.
IEEE Power and Energy Society General Meeting. ed. / Paula Traynor. Vol. 2016-November IEEE, Institute of Electrical and Electronics Engineers, 2016. 7741953.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

AU - Chai, Songjian

AU - Niu, Ming

AU - Xu, Zhao

AU - Lai, Loi Lei

AU - Wong, Kit Po

PY - 2016/11/10

Y1 - 2016/11/10

N2 - 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.

AB - 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.

KW - Conditional forecast

KW - Copula

KW - Kernel density estimation

KW - PV power forecast

KW - Temporal dependence

UR - https://www.scopus.com/pages/publications/85002273517

UR - http://www.pes-gm.org/2016/call-for-papers-2016-ieee-power-energy-society-general-meeting

U2 - 10.1109/PESGM.2016.7741953

DO - 10.1109/PESGM.2016.7741953

M3 - Conference paper

AN - SCOPUS:85002273517

VL - 2016-November

BT - IEEE Power and Energy Society General Meeting

A2 - Traynor, Paula

PB - IEEE, Institute of Electrical and Electronics Engineers

T2 - 2016 IEEE Power and Energy Society General Meeting, PESGM 2016

Y2 - 17 July 2016 through 21 July 2016

ER -

Nonparametric conditional interval forecasts for PV power generation considering the temporal dependence

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UN SDGs

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