The COVID-19 pandemic rapidly reoriented the lives of billions of people across the globe toward working, learning, and subsisting from home. This paper examines the consequences of this disruption of electricity use in Australian households. Using high-frequency electricity monitoring from 491 houses and per-circuit monitoring and in-depth interviews with 17 households, the paper (1) compares changes in energy use before and during COVID-19 lockdown, (2) quantifies the key drivers of changes in energy use experienced by households during lockdown, and (3) tracks households’ interactions with energy use feedback. The findings identify significant increases in certain aspects of household electricity use directly related to COVID-19, including increased cooking and digital device use. Yet despite the government mandate requiring a large proportion of the population to remain at home, overall energy use among the majority of Queensland households monitored actually decreased during lockdown versus prior, driven primarily by a reduction in air conditioner use during lockdown as the weather cooled. Further, despite significant quantified and self-reported changes in energy use, users who had energy use feedback installed accessed their dashboards less during lockdown than they did prior. The paper discusses these results in the context of statistics on COVID-19 related energy demand fluctuations elsewhere, and the implications for the provision of energy use information to residents during significant disruptions such as lockdown.

Climate services can support on-farm decisions, yet this potential is currently not fully realized. Here, using a participatory qualitative risk analysis framework, we introduced 24 Australian farmers to My Climate View, an Australian online, multi-decadal climate service, and asked them to identify, assess and discuss management of long-term risks in light of its projections. We found that multi-decadal projections can help farmers to better understand future climate risks, potentially reducing the psychological distance of climate change. The use of long-term climate projections, however, can be impeded by lack of confidence in data, so leveraging the expertise of trusted service providers may help boost farmers’ confidence. Finally, though climate services providing multi-decadal projections can help farmers to identify future climate risks, they require interactive and recurring engagement to turn awareness into action.

In this paper we discuss results of a field study focused on understanding the ways money and financial issues are handled within family settings. Families develop ‘systems’ or methods through which they coordinate and manage their everyday financial activities. Through an analysis of our fieldwork data collected from fifteen families, we provide several examples of such systems, highlighting their qualities and illustrating how such systems come to support the handling of financial activities in the home. Our results show that these systems are developed with a careful consideration of familial values, relationships and routines; and incorporate the use of physical and digital tools. Consequently, we suggest that design should consider the use and non-use of technology when supporting household financial management, taking into account the richness of families’ existing organically formed practices surrounding financial systems. Finally, our findings point to the fact that financial management in the domestic setting is socially organized and is closely connected to supporting everyday household activities

In this article, we address the interrelated challenges of predicting user comfort and using this to reduce energy consumption in smart heating, ventilation, and air conditioning (HVAC) systems. At present, such systems use simple models of user comfort when deciding on a set-point temperature. Being built using broad population statistics, these models generally fail to represent individual users’ preferences, resulting in poor estimates of the users’ preferred temperatures. To address this issue, we propose the Bayesian Comfort Model (BCM). This personalised thermal comfort model uses a Bayesian network to learn from a user’s feedback, allowing it to adapt to the users’ individual preferences over time. We further propose an alternative to the ASHRAE 7-point scale used to assess user comfort. Using this model, we create an optimal HVAC control algorithm that minimizes energy consumption while preserving user comfort. Through an empirical evaluation based on the ASHRAE RP-884 dataset and data collected in a separate deployment by us, we show that our model is consistently 13.2% to 25.8% more accurate than current models and how using our alternative comfort scale can increase our model’s accuracy. Through simulations we show that using this model, our HVAC control algorithm can reduce energy consumption by 7.3% to 13.5% while decreasing user discomfort by 24.8% simultaneously.