A model for generating household electricity load profiles

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A model for generating household electricity load profiles. / Paatero, Jukka; Lund, Peter.

In: International Journal of Energy Research, Vol. 30, No. 5, 04.2006, p. 273-290.

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@article{dbdca7f252ce443888a96bf88c72afbb,
title = "A model for generating household electricity load profiles",
abstract = "Electricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2{\%} in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42{\%} peak reduction and sudden 3 h loss of load can be compensated with 61{\%} mean load reduction. (c) Copyright 2005 John Wiley & Sons, Ltd.",
keywords = "bottom-up load model, household appliance, demand side management, electric load, ENERGY-CONSUMPTION, DEMAND, HOMES",
author = "Jukka Paatero and Peter Lund",
year = "2006",
month = "4",
doi = "10.1002/er.1136",
language = "English",
volume = "30",
pages = "273--290",
journal = "International Journal of Energy Research",
issn = "0363-907X",
number = "5",

}

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TY - JOUR

T1 - A model for generating household electricity load profiles

AU - Paatero, Jukka

AU - Lund, Peter

PY - 2006/4

Y1 - 2006/4

N2 - Electricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42% peak reduction and sudden 3 h loss of load can be compensated with 61% mean load reduction. (c) Copyright 2005 John Wiley & Sons, Ltd.

AB - Electricity consumption data profiles that include details on the consumption can be generated with a bottom-up load models. In these models the load is constructed from elementary load components that can be households or even their individual appliances. In this work a simplified bottom-up model is presented. The model can be used to generate realistic domestic electricity consumption data on an hourly basis from a few up to thousands of households. The model uses input data that is available in public reports and statistics. Two measured data sets from block houses are also applied for statistical analysis, model training, and verification. Our analysis shows that the generated load profiles correlate well with real data. Furthermore, three case studies with generated load data demonstrate some opportunities for appliance level demand side management (DSM). With a mild DSM scheme using cold loads, the daily peak loads can be reduced 7.2% in average. With more severe DSM schemes the peak load at the yearly peak day can be completely levelled with 42% peak reduction and sudden 3 h loss of load can be compensated with 61% mean load reduction. (c) Copyright 2005 John Wiley & Sons, Ltd.

KW - bottom-up load model

KW - household appliance

KW - demand side management

KW - electric load

KW - ENERGY-CONSUMPTION

KW - DEMAND

KW - HOMES

UR - http://dx.doi.org/10.1002/er.1136

U2 - 10.1002/er.1136

DO - 10.1002/er.1136

M3 - Article

VL - 30

SP - 273

EP - 290

JO - International Journal of Energy Research

JF - International Journal of Energy Research

SN - 0363-907X

IS - 5

ER -

ID: 3208427