Analysis of residential electricity consumption patterns utilizing smart-meter data: Dubai as a case study

Analyzing residential load profiles and usage patterns is critical to making better decisions for demand-side management initiatives and designing strategies to get more people interested in energy savings. Therefore, analyzing load profiles, it is crucial to know how hourly consumption varies during summers, winters, weekdays, and weekends. In addition, determining the influence of occupancy, dwelling size, and building topologies on consumption is equally important to build predictive models. Therefore, this paper presents detailed research on the electricity consumption and profiles of the residential sector in Dubai based on the dwellings' characteristics and 15-minute resolution smart meter data. The data utilized includes the cooling systems, the number of oc-cupants, and the physical characteristics of a dwelling, such as its typology, sizes, and number of bedrooms. First, using the K-Means clustering method, the authors grouped the households based on their consumption profiles. Second, the consumption patterns of each group of households were identified and organized based on similar consumption profiles over 24 h. Third, the authors applied several classification algorithms to assess the potential of using dwellings and occupants' characteristics to predict the patterns with which each household is associated. The analysis of consumption patterns showed that 43% of households with cooling included in their bills had the global peak demand at midnight during weekdays and weekends in summer. However, the global peak demand for cooling-excluded households occurs from 7:00 to 10:00 pm on weekdays and, in some specific cases, on the weekends, as early as 10:00 am. Finally, the methods used for classification were able to identify key charac-teristics driving the patterns of electricity demand and were well suited to this predictive modeling context.

Automated summary

Analyzing residential load profiles and usage patterns is crucial for demand-side management and energy-saving strategies. This paper presents detailed research on electricity consumption and profiles in Dubai based on dwellings' characteristics and smart meter data. The households were grouped using K-Means clustering, and consumption patterns were organized and identified. Classification algorithms were applied to predict household patterns based on characteristics. The analysis revealed peak demand variations and identified key characteristics driving electricity demand patterns.