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Title	Investigating The Appliance Use Patterns On The Households' Electricity Load Shapes From Smart Meters
ID_Doc	33403
Authors	Afzalan M.; Jazizadeh F.
Year	2019
Published	Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
DOI	http://dx.doi.org/10.1061/9780784482445.020
Abstract	The adoption of smart meters in residential households provides electricity consumption data with high temporal resolution. Considering the wealth of the generated information, data analytics methods can be employed to segment the households based on the timing and magnitude of consumption. Specifically, the resultant load shapes reveal the lifestyle usage of consumers, which brings opportunities for the management of demand or the integration of renewables. Prior efforts have focused on the segmentation based on the whole-house consumption data. However, the discussed conclusions did not involve a deeper analysis of appliances-specific usage and the potential of energy reduction at critical times. To this end, this paper explores the contribution of major appliance types and the associated time-of-use patterns for the formation of common daily load shapes. The objective was to explore if there is any specific relationship between the type of appliances used and the load shapes. A preliminary case study evaluation was conducted on a real-world energy consumption dataset. The data includes both aggregate-level and appliance specific power time series with 15-minute resolution from 60 households for two weeks. Clustering on the aggregate building-level data was employed to capture the representative load shapes. Using the appliance-level data for each load shape, the appliance contribution in terms of consumption amount at different time intervals were investigated. The findings could help to improve the understanding of the drivers of households' daily consumption patterns in addition to investigating a more informed recommender system for direct load control. © 2019 American Society of Civil Engineers.
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