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This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world. [ABSTRACT FROM AUTHOR]

Healthcare buildings often have high energy use intensity, which is potentially influenced by a few factors, such as occupancy and climate. A suite of data analysis methods, including principal component analysis and regressions, is applied to analyse monthly electricity data of a modern major hospital in subtropical Australia. The analysis shows that occupancy is not highly correlated with the hospital's electricity use, nor is it important for building energy modelling. However, outdoor environment temperature is highly correlated with the hospital's electricity use. Then, the hospital's electricity uses in 2030 to 2090 scenarios are forecast with future climate files. The impacts are analysed in terms of bill increases and renewable capacity needed to offset the increased electricity use. This study has established a process to predict future hospital energy use using data-driven energy modelling. This succinct article provides vital evidence to support the healthcare sector to continuously improve energy efficiency for health buildings, which is a major asset to adapt to the changing climate. [ABSTRACT FROM AUTHOR]

• Bottom-up estimation tool to develop electric public transport (EPT) charging loads. • Uses GTFS public data and can be scaled to hundreds of cities/states globally. • EPT could add 17% to critical peak demand on some distribution zone substations. Battery electrified public transit (BEPT), including buses and passenger ferries, is a promising solution to transport-related climate emissions and urban air pollution, but introduces potentially challenging large, coincident demand in the low-voltage distribution network. This paper presents a tool for estimating the energy and charging demand of electrified public transit using public data that is available in over 150 cities/states globally and demonstrates it in two case studies. The tool applies heuristic vehicle scheduling to existing public General Transit Feed Specification data to model the charging profiles and electricity consumption of BEPT for various charging regimes across different geographical scales. For the case study of New South Wales, Australia, the impacts of BEPT are most significant at the low-voltage network, where adding a battery-electric bus depot was found to increase the annual critical peak demand at the local zone substation in Summer by up to 17% while exacerbating demand during the daily evening peak period by 20–30%. Across the entire state of NSW, a full transition to BEPT increases annual electricity consumption by 1.28–1.34% and peak daily demand by 1–3%. [ABSTRACT FROM AUTHOR]

• The impacts of COVID lockdown on six aged care communities' energy and peak demand are studied. • The lockdown period was compared with the same period of similar temperature in previous years. • The impacts of COVID lockdown varied enormously in different climate regions. • Effectiveness and limitations of existing energy KPIs are discussed. In late March to April 2020 residential aged care facilities (RAC) in Australia were under COVID-19 lockdown. This paper explores whether the resultant restrictions on entry and exit and elimination of site group activities within RACs impacted on the total electricity use, peak demand and electrical load profiles. Six RACs in four different climate zones are analysed, comparing historical electricity load and demand profiles with the lockdown period. The facilities in warm regions showed largest reductions in energy use and peak demands. There was a peak demand timing shift in temperate regions and hot regions' changes were negligible for energy use or peak demands. This study revealed the limitations of using aggregate data as the key performance indicator (KPI) – energy use per bed. Also, KPIs in relation to cooling degree days (or total cooling and heating degree days) have been examined and are not recommended for temperate regions or temperate seasons. The potential CO 2 emission reduction from onsite renewable generation is quantified. Further research is to investigate energy use changes at circuit levels under lockdowns, and develop more nuanced KPIs that include the rate of energy use and the timing of that use. [ABSTRACT FROM AUTHOR]

Aged care communities have been under the spotlight since the beginning of 2020. Energy is essential to ensure reliable operation and quality care provision in residential aged care communities (RAC). The aim of this study is to determine how RAC's yearly energy use and peak demand changed in Australia and what this might mean for RAC design, operation and energy asset investment and ultimately in the healthcare plan for elderly residents. Five years of electricity demand data from four case study RACs in the same climate zone are analyzed. Statistical tools are used to analyze the data, and a clustering algorithm is used to identify typical demand profiles. A number of energy key performance indicators (KPIs) are evaluated, highlighting their respective benefits and limitations. The results show an average 8% reduction for yearly energy use and 7% reduction for yearly peak demands in the COVID-19 year compared with the average of the previous four years. Typical demand profiles for the four communities were mostly lower in the pandemic year. Despite these results, the KPI analysis shows that, for these four communities, outdoor ambient temperature remains a very significant correlation factor for energy use. [ABSTRACT FROM AUTHOR]