RESUMEN
The residential sector's substantial electricity consumption, driven by heating demands during winter, necessitates optimal energy consumption strategies in the era of decarbonization. To address this challenge, this paper introduces a synthetic dataset specifically tailored to simulate energy consumption in residential apartment buildings. Focusing on the interplay of cold weather conditions and the effects of aging factors, the dataset comprehensively encompasses key variables, including indoor temperature, energy consumption, outdoor temperature, outdoor humidity and solar radiation. It underscores the considerable impact of building aging on energy consumption patterns. The dataset's significance extends across various domains, particularly in the realms of energy forecasting and thermal modelling. It serves as a robust foundation for predicting future consumption patterns, optimizing resource allocation, and refining energy efficiency strategies. The inclusion of indoor temperature data facilitates an in-depth thermal modelling approach, shedding light on intricate relationships that influence building performance in cold climates. Beyond traditional, the dataset proves invaluable in nonlinear modelling and machine learning. It emerges as a key tool for algorithm training, enhancing forecast precision, and supporting well-informed decision-making. The introduction of a temporal dimension by accounting for aging factors allows for the exploration of evolving building components over time, a critical consideration for sustainable energy management and building maintenance strategies. The dataset was meticulously generated by creating geometry using SketchUp and conducting energy modelling and simulations via the OpenStudio platform, which integrates the Energy Plus modelling engine to enhance accuracy. In summary, this synthetic dataset generation provides valuable insights into energy consumption in residential buildings exposed to cold weather conditions and the influences of aging. Its multifaceted applications across forecasting, modelling, management, and planning underscore its potential to advance sustainable and efficient energy practices.
RESUMEN
In February 2021, a cluster of Beta variant (B.1.351) coronavirus disease 2019 (COVID-19) cases were identified in an apartment building located in Northern Ontario, Canada. Most cases had no known contact with each other. Objectives of this multi-component outbreak investigation were to better understand the social and environmental factors that facilitated the transmission of COVID-19 through this multi-unit residential building (MURB). A case-control study examined building-specific exposures and resident behaviours that may have increased the odds of being a case. A professional engineer assessed the building's heating, ventilation, and air-conditioning (HVAC) systems. Whole-genome sequencing and an in-depth genomic analysis were performed. Forty-five outbreak-confirmed cases were identified. From the case-control study, being on the upper floors (OR: 10.4; 95% CI: 1.63-66.9) and within three adjacent vertical lines (OR: 28.3; 3.57-225) were both significantly associated with being a case of COVID-19, after adjusting for age. There were no significant differences in reported behaviours, use of shared spaces, or precautions taken between cases and controls. An assessment of the building's ventilation found uncontrolled air leakage between apartment units. A single genomic cluster was identified, where most sequences were identical to one another. Findings from the multiple components of this investigation are suggestive of aerosol transmission between units.