Short-term load forecasting with high levels of distributed renewable generation

Judith Foster; Xueqin Liu; Sean McLoone

doi:10.1109/UPEC.2017.8231976

Short-term load forecasting with high levels of distributed renewable generation

Judith Foster, Xueqin Liu, Sean McLoone

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Citations (Scopus)

333 Downloads (Pure)

Abstract

Traditionally, load forecasting tools include weather variables as model inputs. Northern Ireland has seen a major increase in weather dependent, renewable generation over the last number of years creating a double impact by weather parameters in the load profile. The new generation is not visible to, or controllable by, the system operator and is presenting major challenges to traditional load forecasting tools which are struggling to account for the fluctuations in demand. In this paper a simple linear method, using the previous week demand value and a correction for the weather and renewable generation is investigated within a sliding window parameter updating framework. Selection of variables are based on correlation analysis of supplied and derived meteorological variables. Model performance is evaluated on load data for the period 2011-16, split into several periods due to different levels of installed renewable energy. A 4-input model with parameters updated on the basis of a 44-day sliding window of historical data, is shown to give a mean absolute percentage error of 2.6% overall.

Original language	English
Title of host publication	52nd International Universities Power Engineering Conference (UPEC 2017): Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	6
ISBN (Electronic)	978-1-5386-2344-2
ISBN (Print)	978-1-5386-2345-9
DOIs	https://doi.org/10.1109/UPEC.2017.8231976
Publication status	Published - 21 Dec 2017
Event	52nd International Universities Power Engineering Conference (UPEC) - Heraklion, Greece Duration: 28 Aug 2017 → 31 Aug 2017 Conference number: 52

Conference

Conference	52nd International Universities Power Engineering Conference (UPEC)
Abbreviated title	UPEC
Country/Territory	Greece
City	Heraklion
Period	28/08/2017 → 31/08/2017

UN SDGs

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

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10.1109/UPEC.2017.8231976Licence: Unspecified

Brexit and Northern Ireland
© 2018 IEEE. This work is made available online in accordance with the publisher’s policies. Please refer to any applicable terms of use of the publisher.
Final published version, 294 KBLicence: Unspecified

Electric load forecasting with increased embedded renewable generation
Foster, J. (Author), Liu, X. (Supervisor) & McLoone, S. (Supervisor), Dec 2020
Student thesis: Doctoral Thesis › Doctor of Philosophy

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title = "Short-term load forecasting with high levels of distributed renewable generation",

abstract = "Traditionally, load forecasting tools include weather variables as model inputs. Northern Ireland has seen a major increase in weather dependent, renewable generation over the last number of years creating a double impact by weather parameters in the load profile. The new generation is not visible to, or controllable by, the system operator and is presenting major challenges to traditional load forecasting tools which are struggling to account for the fluctuations in demand. In this paper a simple linear method, using the previous week demand value and a correction for the weather and renewable generation is investigated within a sliding window parameter updating framework. Selection of variables are based on correlation analysis of supplied and derived meteorological variables. Model performance is evaluated on load data for the period 2011-16, split into several periods due to different levels of installed renewable energy. A 4-input model with parameters updated on the basis of a 44-day sliding window of historical data, is shown to give a mean absolute percentage error of 2.6% overall.",

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Foster, J, Liu, X & McLoone, S 2017, Short-term load forecasting with high levels of distributed renewable generation. in 52nd International Universities Power Engineering Conference (UPEC 2017): Proceedings . Institute of Electrical and Electronics Engineers Inc., 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Greece, 28/08/2017. https://doi.org/10.1109/UPEC.2017.8231976

TY - GEN

T1 - Short-term load forecasting with high levels of distributed renewable generation

AU - Foster, Judith

AU - Liu, Xueqin

AU - McLoone, Sean

N1 - Conference code: 52

PY - 2017/12/21

Y1 - 2017/12/21

N2 - Traditionally, load forecasting tools include weather variables as model inputs. Northern Ireland has seen a major increase in weather dependent, renewable generation over the last number of years creating a double impact by weather parameters in the load profile. The new generation is not visible to, or controllable by, the system operator and is presenting major challenges to traditional load forecasting tools which are struggling to account for the fluctuations in demand. In this paper a simple linear method, using the previous week demand value and a correction for the weather and renewable generation is investigated within a sliding window parameter updating framework. Selection of variables are based on correlation analysis of supplied and derived meteorological variables. Model performance is evaluated on load data for the period 2011-16, split into several periods due to different levels of installed renewable energy. A 4-input model with parameters updated on the basis of a 44-day sliding window of historical data, is shown to give a mean absolute percentage error of 2.6% overall.

AB - Traditionally, load forecasting tools include weather variables as model inputs. Northern Ireland has seen a major increase in weather dependent, renewable generation over the last number of years creating a double impact by weather parameters in the load profile. The new generation is not visible to, or controllable by, the system operator and is presenting major challenges to traditional load forecasting tools which are struggling to account for the fluctuations in demand. In this paper a simple linear method, using the previous week demand value and a correction for the weather and renewable generation is investigated within a sliding window parameter updating framework. Selection of variables are based on correlation analysis of supplied and derived meteorological variables. Model performance is evaluated on load data for the period 2011-16, split into several periods due to different levels of installed renewable energy. A 4-input model with parameters updated on the basis of a 44-day sliding window of historical data, is shown to give a mean absolute percentage error of 2.6% overall.

U2 - 10.1109/UPEC.2017.8231976

DO - 10.1109/UPEC.2017.8231976

M3 - Conference contribution

SN - 978-1-5386-2345-9

BT - 52nd International Universities Power Engineering Conference (UPEC 2017): Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 52nd International Universities Power Engineering Conference (UPEC)

Y2 - 28 August 2017 through 31 August 2017

ER -

Short-term load forecasting with high levels of distributed renewable generation

Abstract

Conference

UN SDGs

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Student theses

Electric load forecasting with increased embedded renewable generation

Cite this