Daily electricity consumption profiles from smart meters are explored as proxies of active behavior regarding space heating and cooling. The influence of the environment air temperature (multiple maximum and minimum daily thresholds) on electricity consumption was explored for a final sample of 19 households located in southwestern Europe (characterized by hot, dry summers and cool, wet winters), taking the full year of 2014. Statistical analysis of the deviations from hourly average electricity consumptions for each temperature thresholds was performed for each household. Firstly, these deviations could act as proxies highlighting possible lack of thermal comfort on space cooling, and partially on space heating, supported by door-to-door survey data, on socio-economic details of occupants, buildings bearing structure and equipment's ownership and use. Secondly, meaningful differences of consumers' behavior on electricity consumption pattern were identified as a response for space heating and cooling to the environment air temperatures thresholds. Additionally, statistical clusters of active and non-active behavior groups of households were assessed, showing the electricity use for space heating. This paper illustrates the importance of the widespread use of smart-meters data on the increasingly electrified buildings sector, to understand whether and how thermal comfort could be achieved through active climatization behavior of its occupants. This is particularly important in regions where automatic HVAC systems are almost absent.

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Fuel poverty is a recognized and increasing problem in several European countries. A growing body of literature covers this topic, but dedicated analysis for Portugal are scarce despite the high perception of this condition. This paper contributes to fill this knowledge gap focusing on a European southern city while bringing new datasets and analysis to the assessment of this topic; consumer groups identification and to policy discussion. Daily electricity smart meters' registries were combined with socio-economic data, collected from door-to-door surveys, to understand the extent and the determinants of energy consumption for two contrasting consumer groups (herein called fuel poverty and fuel obesity groups). The analysis is based on the amount and annual profile of electricity consumption and was complemented with building energy simulations for relevant building typologies in those groups, to identify heating and cooling thermal performance gaps. The existence of these gaps allowed confirming and/or discarding the initial hypothesis of the poverty or obesity conditions. Results disclose socio-economic variables, as income, and consumers' behavior as key determinants of electricity consumption. It was identified a severe lack of thermal comfort levels inside households of both groups, either in cooling (98% for fuel poverty and 87% for fuel obesity) and heating seasons (98% for fuel poverty and 94% for fuel obesity). Major conclusion refers that electricity consumption cannot be used alone to segment consumer groups. This assessment may serve to support energy policy measures and instruments targeted to different consumers' groups. For example, distinct campaigns and differentiated incentives may apply to achieve energy efficiency and reduction while keep or improve indoor comfort levels.

Improvements of energy efficiency and reduction of Electricity Consumption (EC) could be pushed by increased knowledge on consumption profiles. This paper contributes to a comprehensive understanding of the EC profiles in a Southwest European city through the combination of high-resolution data from smart meters (daily electricity consumption) with door-to-door 110-question surveys for a sample of 265 households in the city of Évora, in Portugal. This analysis allowed to define ten power consumption clusters using Ward's method hierarchical clustering, corresponding to four distinct types of annual consumption profiles: U shape (sharp and soft), W shape and Flat. U shape pattern is the most common one, covering 77% of the sampled households.
The results show that three major groups of determinants characterize the electricity consumption segmentation: physical characteristics of a dwelling, especially year of construction and floor area; HVAC equipment and fireplaces ownership and use; and occupants’ profiles (mainly number and monthly income).
The combination of the daily EC data with qualitative door-to-door survey-based data proved to be a powerful data nutshell to distinguish groups of power consumers, allowing to derive insights to support DSOs, ESCOs, and retailers to design measures and instruments targeted to effective energy reduction (e.g. peak shaving, energy efficiency).