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Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information. / Schirmer, Pascal; Geiger, Christian; Mporas, Iosif.

2020. Paper presented at 28th European Signal Processing Conference, EUSIPCO 2020.

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Schirmer, P, Geiger, C & Mporas, I 2020, 'Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information', Paper presented at 28th European Signal Processing Conference, EUSIPCO 2020, 18/01/21. https://doi.org/10.23919/Eusipco47968.2020.9287395

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Schirmer, Pascal ; Geiger, Christian ; Mporas, Iosif. / Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information. Paper presented at 28th European Signal Processing Conference, EUSIPCO 2020.

Bibtex

@conference{7dc050ea6252464ab6f40cf1cf5aaa8e,
title = "Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information",
abstract = "Previous studies have shown that residential energy consumption prediction accuracy can be improved when households energy data are fused with residents' socioeconomic information. In this article we propose an architecture for the prediction of residential energy consumption using past energy consumption from other/neighboring households in combination with socioeconomic information of the corresponding residents. The architecture is based on a Long Short Term Memory model and was evaluated using a large-scale dataset monitoring households of London. The proposed approach significantly improves the accuracy of the energy consumption predictor reducing the mean absolute error up to 25.2% with prediction error rate equal to 5.4%.",
author = "Pascal Schirmer and Christian Geiger and Iosif Mporas",
year = "2020",
month = dec,
day = "18",
doi = "10.23919/Eusipco47968.2020.9287395",
language = "English",
note = "28th European Signal Processing Conference, EUSIPCO 2020 ; Conference date: 18-01-2021",
url = "https://eusipco2020.org/",

}

RIS

TY - CONF

T1 - Residential Energy Consumption Prediction Using Inter-Household Energy Data and Socioeconomic Information

AU - Schirmer, Pascal

AU - Geiger, Christian

AU - Mporas, Iosif

PY - 2020/12/18

Y1 - 2020/12/18

N2 - Previous studies have shown that residential energy consumption prediction accuracy can be improved when households energy data are fused with residents' socioeconomic information. In this article we propose an architecture for the prediction of residential energy consumption using past energy consumption from other/neighboring households in combination with socioeconomic information of the corresponding residents. The architecture is based on a Long Short Term Memory model and was evaluated using a large-scale dataset monitoring households of London. The proposed approach significantly improves the accuracy of the energy consumption predictor reducing the mean absolute error up to 25.2% with prediction error rate equal to 5.4%.

AB - Previous studies have shown that residential energy consumption prediction accuracy can be improved when households energy data are fused with residents' socioeconomic information. In this article we propose an architecture for the prediction of residential energy consumption using past energy consumption from other/neighboring households in combination with socioeconomic information of the corresponding residents. The architecture is based on a Long Short Term Memory model and was evaluated using a large-scale dataset monitoring households of London. The proposed approach significantly improves the accuracy of the energy consumption predictor reducing the mean absolute error up to 25.2% with prediction error rate equal to 5.4%.

U2 - 10.23919/Eusipco47968.2020.9287395

DO - 10.23919/Eusipco47968.2020.9287395

M3 - Paper

T2 - 28th European Signal Processing Conference, EUSIPCO 2020

Y2 - 18 January 2021

ER -