Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.525309
Title: Reducing domestic energy consumption through behaviour modification
Author: Ford, Rebecca
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2009
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Abstract:
This thesis presents the development of techniques which enable appliance recognition in an Advanced Electricity Meter (AEM) to aid individuals reduce their domestic electricity consumption. The key aspect is to provide immediate and disaggregated information, down to appliance level, from a single point of measurement. Three sets of features including the short term time domain, time dependent finite state machine behaviour and time of day are identified by monitoring step changes in the power consumption of the home. Associated with each feature set is a membership which depicts the amount to which that feature set is representative of a particular appliance. These memberships are combined in a novel framework to effectively identify individual appliance state changes and hence appliance energy consumption. An innovative mechanism is developed for generating short term time domain memberships. Hierarchical and nearest neighbour clustering is used to train the AEM by generating appliance prototypes which contain an indication of typical parameters. From these prototypes probabilistic fuzzy memberships and possibilistic fuzzy typicalities are calculated for new data points which correspond to appliance state changes. These values are combined in a weighted geometric mean to produce novel memberships which are determined to be appropriate for the domestic model. A voltage independent feature space in the short term time domain is developed based on a model of the appliance’s electrical interface. The components within that interface are calculated and these, along with an indication of the appropriate model, form a novel feature set which is used to represent appliances. The techniques developed are verified with real data and are 99.8% accurate in a laboratory based classification in the short term time domain. The work presented in this thesis demonstrates the ability of the AEM to accurately track the energy consumption of individual appliances.
Supervisor: McCulloch, Malcolm Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.525309  DOI: Not available
Keywords: Engineering & allied sciences ; energy ; smart-meter ; disaggregated feedback ; non intrusive appliance load monitoring ; fuzzy clustering ; membership generation
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