Efficient predictive demand response using laguerre functions

Paul Mc Namara; Sean McLoone

doi:10.1109/PESMG.2013.6672963

Efficient predictive demand response using laguerre functions

Paul Mc Namara, Sean McLoone

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

3 Citations (Scopus)

Abstract

Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation.

Original language	English
Title of host publication	IEEE Power and Energy Society General Meeting
DOIs	https://doi.org/10.1109/PESMG.2013.6672963
Publication status	Published - 01 Dec 2013
Event	2013 IEEE Power and Energy Society General Meeting, PES 2013 - BC, Vancouver, Canada Duration: 21 Jul 2013 → 25 Jul 2013

Conference

Conference	2013 IEEE Power and Energy Society General Meeting, PES 2013
Country/Territory	Canada
City	Vancouver
Period	21/07/2013 → 25/07/2013

ASJC Scopus subject areas

Energy Engineering and Power Technology
Nuclear Energy and Engineering
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

UN SDGs

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

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10.1109/PESMG.2013.6672963

Cite this

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title = "Efficient predictive demand response using laguerre functions",

abstract = "Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation.",

author = "{Mc Namara}, Paul and Sean McLoone",

year = "2013",

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doi = "10.1109/PESMG.2013.6672963",

language = "English",

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AU - McLoone, Sean

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AB - Predictive Demand Response (DR) algorithms allow schedulable loads in power systems to be shifted to off-peak times. However, the size of the optimisation problems associated with predictive DR can grow very large and so efficient implementations of algorithms are desirable. In this paper Laguerre functions are used to significantly reduce the size of the optimisation needed to implement predictive DR, thus significantly increasing the efficiency of the implementation.

UR - http://www.scopus.com/inward/record.url?scp=84893161175&partnerID=8YFLogxK

U2 - 10.1109/PESMG.2013.6672963

DO - 10.1109/PESMG.2013.6672963

M3 - Conference contribution

AN - SCOPUS:84893161175

SN - 9781479913039

BT - IEEE Power and Energy Society General Meeting

T2 - 2013 IEEE Power and Energy Society General Meeting, PES 2013

Y2 - 21 July 2013 through 25 July 2013

ER -

Efficient predictive demand response using laguerre functions

Abstract

Conference

ASJC Scopus subject areas

UN SDGs

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