Evaluation of clustering techniques for generating household energy consumption patterns in a developing country

dc.contributor.advisorMoodley, Deshen
dc.contributor.advisorMeyer, Thomas
dc.contributor.authorToussaint, Wiebke
dc.date.accessioned2020-02-07T09:36:55Z
dc.date.available2020-02-07T09:36:55Z
dc.date.issued2019
dc.date.updated2020-01-24T09:35:12Z
dc.description.abstractThis work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily load patterns of a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more expressive clusters than the zero-one scaler, which is the most common method of normalisation used in the domain. While pre-binning improves clustering results for the dataset, the choice of pre-binning method does not significantly impact the quality of clusters produced. Data representation and especially the inclusion or removal of zero-valued profiles is an important consideration in relation to the pre-binning approach selected. Like several previous studies, the k-means algorithm produces the best results. Introducing a qualitative evaluation framework facilitated the evaluation process and helped identify a top clustering structure that is significantly more useable than those that would have been selected based on quantitative metrics alone. The approach demonstrates how explicitly defined qualitative evaluation measures can aid in selecting a clustering structure that is more likely to have real world application. To our knowledge this is the first work that uses cluster analysis to generate customer archetypes from representative daily load profiles in a highly variable, developing country context
dc.identifier.apacitationToussaint, W. (2019). <i>Evaluation of clustering techniques for generating household energy consumption patterns in a developing country</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/30905en_ZA
dc.identifier.chicagocitationToussaint, Wiebke. <i>"Evaluation of clustering techniques for generating household energy consumption patterns in a developing country."</i> ., ,Faculty of Science ,Department of Computer Science, 2019. http://hdl.handle.net/11427/30905en_ZA
dc.identifier.citationToussaint, W. 2019. Evaluation of clustering techniques for generating household energy consumption patterns in a developing country.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Toussaint, Wiebke AB - This work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily load patterns of a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more expressive clusters than the zero-one scaler, which is the most common method of normalisation used in the domain. While pre-binning improves clustering results for the dataset, the choice of pre-binning method does not significantly impact the quality of clusters produced. Data representation and especially the inclusion or removal of zero-valued profiles is an important consideration in relation to the pre-binning approach selected. Like several previous studies, the k-means algorithm produces the best results. Introducing a qualitative evaluation framework facilitated the evaluation process and helped identify a top clustering structure that is significantly more useable than those that would have been selected based on quantitative metrics alone. The approach demonstrates how explicitly defined qualitative evaluation measures can aid in selecting a clustering structure that is more likely to have real world application. To our knowledge this is the first work that uses cluster analysis to generate customer archetypes from representative daily load profiles in a highly variable, developing country context DA - 2019 DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2019 T1 - Evaluation of clustering techniques for generating household energy consumption patterns in a developing country TI - Evaluation of clustering techniques for generating household energy consumption patterns in a developing country UR - http://hdl.handle.net/11427/30905 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/30905
dc.identifier.vancouvercitationToussaint W. Evaluation of clustering techniques for generating household energy consumption patterns in a developing country. []. ,Faculty of Science ,Department of Computer Science, 2019 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/30905en_ZA
dc.language.rfc3066eng
dc.publisher.departmentDepartment of Computer Science
dc.publisher.facultyFaculty of Science
dc.subjectComputer Science
dc.titleEvaluation of clustering techniques for generating household energy consumption patterns in a developing country
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc
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