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Modelling Energy Consumption Profiles with Data Mining Methods based on Smart Meter Survey Data with Additional User Characteristics

Rüppel, Uwe and Schwöbel, Christian
Li, Haijiang and de Wilde, Pieter and Rafiq, Yaqub (eds.) (2014):
Modelling Energy Consumption Profiles with Data Mining Methods based on Smart Meter Survey Data with Additional User Characteristics.
In: Proceedings of the 21st International Workshop on Intelligent Computing in Engineering, In: 21st EG-ICE International Workshop, Cardiff, England, 16.-18. Juli 2014, ISBN 978-0-9930807-0-8,
[Online-Edition: http://egice2014.engineering.cf.ac.uk/images/BIN/papers/18.p...],
[Conference or Workshop Item]

Abstract

Among the many measures in the field of energy efficiency, predicting energy consumption and a better understanding of how and when energy is consumed are key factors. Possible ways to predict energy consumption would be to apply simulation models or to make use of statistical methods. However, most of these methods do not include user and building properties in their models. In this paper, a data mining method is shown which uses the questionnaire of a smart metering study and its metering values as an input. First, clustering is applied on a subset of the survey questions to form user groups and then a link to each cluster’s appropriate consumption characteristics is established. In this work two clustering processes are shown as examples. In the first example, clusters are formed using characteristics about the residents. Then the clusters are being linked to their electricity consumption. In the second example, building characteristics are used to form the clusters and these are being connected to their gas consumption.

Item Type: Conference or Workshop Item
Erschienen: 2014
Editors: Li, Haijiang and de Wilde, Pieter and Rafiq, Yaqub
Creators: Rüppel, Uwe and Schwöbel, Christian
Title: Modelling Energy Consumption Profiles with Data Mining Methods based on Smart Meter Survey Data with Additional User Characteristics
Language: English
Abstract:

Among the many measures in the field of energy efficiency, predicting energy consumption and a better understanding of how and when energy is consumed are key factors. Possible ways to predict energy consumption would be to apply simulation models or to make use of statistical methods. However, most of these methods do not include user and building properties in their models. In this paper, a data mining method is shown which uses the questionnaire of a smart metering study and its metering values as an input. First, clustering is applied on a subset of the survey questions to form user groups and then a link to each cluster’s appropriate consumption characteristics is established. In this work two clustering processes are shown as examples. In the first example, clusters are formed using characteristics about the residents. Then the clusters are being linked to their electricity consumption. In the second example, building characteristics are used to form the clusters and these are being connected to their gas consumption.

Title of Book: Proceedings of the 21st International Workshop on Intelligent Computing in Engineering
ISBN: 978-0-9930807-0-8
Divisions: 13 Department of Civil and Environmental Engineering Sciences
13 Department of Civil and Environmental Engineering Sciences > Institute of Numerical Methods and Informatics in Civil Engineering
Event Title: 21st EG-ICE International Workshop
Event Location: Cardiff, England
Event Dates: 16.-18. Juli 2014
Date Deposited: 22 Jan 2015 10:26
Official URL: http://egice2014.engineering.cf.ac.uk/images/BIN/papers/18.p...
Additional Information:

ISBN: 978-0-9930807-0-8

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