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In this paper, we present a novel framework for enriching time series data in smart cities by supplementing it with information from external sources via semantic data enrichment. Our methodology effectively merges multiple data sources into a uniform time series, while addressing difficulties such as data quality, contextual information, and time lapses. We demonstrate the efficacy of our method through a case study in Barcelona, which permitted the use of advanced analysis methods such as windowed cross-correlation and peak picking. The resulting time series data can be used to determine traffic patterns and has potential uses in other smart city sectors, such as air quality, energy efficiency, and public safety. Interactive dashboards enable stakeholders to visualize and summarize key insights and patterns. [ABSTRACT FROM AUTHOR]

Energy poverty is a serious social problem with well-known adverse health consequences. This problem has been addressed mainly through improvements in the energy efficiency of housing. Still, little is known about the effects of information-based measures on energy poverty and their impacts on health. A quasi-experimental study was implemented to assess the effectiveness of an energy-counseling home visit intervention targeting the vulnerable population in a southern European city, Barcelona, in alleviating energy poverty and improving health. The intervention had beneficial impacts on keeping homes at an adequate indoor temperature and reducing primary care visits. No effects were found on self-perceived health or self-reported anxiety and depression. After the intervention, participants reported a decrease in arrears on utility bills, but less pronounced than in the comparison group. In conclusion, the study showed that information-based measures lead to psychosocial gains and reduced healthcare use. Nevertheless, the impact of these measures could be enhanced by combining them with policies and programmes that address the structural determinants of energy poverty. [ABSTRACT FROM AUTHOR]

[Display omitted] • The impact of the COVID-19 lockdown on energy consumption in university buildings was assessed. • Eighty-three academic buildings of the Universitat Politècnica de Catalunya - Barcelona Tech (UPC) were analysed. • Avoided energy consumption amounted to over 4.3 GWh in one year. • Vacant academic buildings still used 46.9% of their typical energy consumption. • Libraries (−74.2%) and teaching buildings (−68.1%) experienced the highest energy reductions during the strict lockdown. Exceptional pandemic lockdown measures enabled singular experiments such as analysing the energy consumption of vacant buildings. This paper assesses the impact of the COVID-19 lockdown on the energy use of academic buildings. For this purpose, weather-adjusted energy use was compared before and during the lockdown, including different levels of lockdown restrictions. Results obtained for the 83 academic buildings of Universitat Politècnica de Catalunya - Barcelona Tech (UPC) reveal that the avoided energy consumption amounted to over 4.3 GWh during the post-pandemic year. However, the results indicate that academic buildings were still using approximately 46.9% of their typical energy consumption during strict lockdown. This revelation emphasizes the high environmental burden of buildings, regardless of whether they are occupied. [ABSTRACT FROM AUTHOR]