Figure - available from: Environmental Research Letters
This content is subject to copyright. Terms and conditions apply.
(a): Percentage of homes identified as having an AC with all five heat metrics (i.e. consensus across all metrics). (b)–(e): The additional homes identified as having AC with a consensus of n metrics. (f): Summary of the percentage of homes identified as having AC by n heat metrics. The transition from dark to light blue implies diminishing confidence in the homes identified as having AC (e.g. we have more confidence in the homes identified with 5 metrics, represented with dark blue, than the homes identified with 1 metric, represented with light blue).
Source publication
Global cooling capacity is expected to triple by 2050, as rising temperatures and humidity levels intensify the heat stress that populations experience. Although air conditioning (AC) is a key adaptation tool for reducing exposure to extreme heat, we currently have a limited understanding of patterns of AC ownership. Developing high resolution esti...
Similar publications
Children’s sleep is essential for healthy development, yet over a third of children in the United States experience inadequate sleep. Environmental factors can influence sleep: greenspace exposure can promote better sleep, while heat exposure can disrupt sleep. As global climate change raises nighttime and daytime temperatures, greenspace may mitig...
Citations
... Much of the work that has modeled electricity consumption in relation to weather uses only the outdoor dry bulb temperature as the most influential meteorological variable [39] or the concepts of heating degree days and cooling degree days [40]. However, recent work has recognized the importance of relative humidity to demand for space cooling, especially in the context of extreme heat [41,42]. ...
Changes in climate and energy technologies motivate a greater understanding of residential electricity usage and its relation to weather conditions. The recent proliferation of smart electricity meters promises an influx of new datasets spanning diverse cities, geographies, and climates worldwide. However, although analytics for smart meters is a rapidly expanding field of research, issues such as generalizability to new data and robustness to data quality remain underexplored in the literature. We characterize residential electricity consumption patterns from a large, uncurated testbed of smart electricity meter data, revealing challenges in adapting existing methodologies to datasets with different scopes and locations. We propose a novel feature—the proportion of electricity used below a temperature threshold—summarizing a household’s demand-temperature profile that is productive for identifying electric primary space heating in a smart meter data set of Chicago single-family residences. Weighted logistic regression using the proportion of electricity consumed below a selected low temperature mitigates difficulties of the dataset such as skew and class imbalance. Although the limitations of the dataset restrict some approaches, this experiment suggests advantages of the feature that can be adapted to study other datasets beyond the identification of space heating. Such data-driven approaches can be valuable for knowledge distillation from abundant, uncurated smart electricity meter data.
