Print Email Facebook Twitter Smart Home Energy Efficiency Title Smart Home Energy Efficiency: Application of machine learning on smart meter power data to improve efficient electric energy usage in households Author Boers, Milo (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Blok, K. (mentor) Kamp, L.M. (graduation committee) Itard, L.C.M. (graduation committee) Degree granting institution Delft University of Technology Programme Electrical Engineering | Sustainable Energy Technology Date 2022-08-11 Abstract Nonintrusive Load Monitoring (NILM) can be used to disaggregate household energyusage collected from a central meter to the level of individual appliances, but has so far mostly been applied in controlled, small-scale settings. Further potential such as the application for energy efficiency classification and management have remained largely untapped. In this research a machine learning model was developed and deployed to determine energy consumption, usage pattern and energy efficiency characteristics of real-life dishwasher usage based on smart meter data. The developed NILM system was deployed on a full year of smart meter data for nearly 130.000 households in the Netherlands. The analysis was accompanied by a survey to gain additional information on the households usage behaviour. The average energy consumption for all households was found to be 1.18kWh per wash, with dishwashers used 240 times per year on average. Dependencies are shown for household size, washing temperature, machine efficiency label as well as time of day, week and year. It was estimated that 9 in 10 households could reduce their dishwasher energy consumption, with an average saving potential of more than 30% per year. The developed method showed to be suitable to gain insight into average electricity consumption and usage patterns on an appliance level, non-intrusively and at large-scale. Additional survey data was shown to provide comparative insight between different user groups. The developed framework can be easily expanded for other major appliances and could be used to drive tailored consumer feedback on energy efficiency improvements within households. Subject Smart Meter Load MonitoringLoad DisaggregationPattern RecognitionEnergy AnalysisEnergy Efficiency To reference this document use: http://resolver.tudelft.nl/uuid:957f8a68-b485-4846-a286-4f64a9b32e40 Part of collection Student theses Document type master thesis Rights © 2022 Milo Boers Files PDF 20220704_MBoers_SET_Maste ... _Final.pdf 10.22 MB Close viewer /islandora/object/uuid%3A957f8a68-b485-4846-a286-4f64a9b32e40/datastream/OBJ/view