Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia
Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia
Liu, Aaron;Miller, Wendy;Chiou, James;Zedan, Sherif;Yigitcanlar, Tan;Ding, Yuemin
2021-11-23 00:00:00
buildings Article Aged Care Energy Use and Peak Demand Change in the COVID-19 Year: Empirical Evidence from Australia 1 , 1 2 1 1 3 Aaron Liu * , Wendy Miller , James Chiou , Sherif Zedan , Tan Yigitcanlar and Yuemin Ding School of Architecture and Built Environment, Queensland University of Technology, Brisbane, QLD 4000, Australia; w2.miller@qut.edu.au (W.M.); s2.zedan@qut.edu.au (S.Z.); tan.yigitcanlar@qut.edu.au (T.Y.) Bolton Clarke, Brisbane, QLD 4059, Australia; jchiou@boltonclarke.com.au Department of Electrical and Electronic Engineering, University of Navarra, 20018 San Sebastian, Spain; yuemin.ding1986@gmail.com * Correspondence: lei.liu@connect.qut.edu.au; Tel.: +61-7-3138-7686 Abstract: 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 Citation: Liu, A.; Miller, W.; Chiou, demand profiles for the four communities were mostly lower in the pandemic year. Despite these J.; Zedan, S.; Yigitcanlar, T.; Ding, Y. results, the KPI analysis shows that, for these four communities, outdoor ambient temperature Aged Care Energy Use and Peak remains a very significant correlation factor for energy use. Demand Change in the COVID-19 Year: Empirical Evidence from Keywords: aged care community; energy management; energy use intensity; energy peak demand; Australia. Buildings 2021, 11, 570. Gaussian Mixture Model; key performance indicator https://doi.org/10.3390/ buildings11120570 Academic Editor: Chi-Ming Lai 1. Introduction Received: 25 October 2021 About 40% of global energy use is related to buildings [1,2]. Healthcare consumes Accepted: 19 November 2021 a lot of energy, and healthcare buildings are often one of the most energy-intensive use Published: 23 November 2021 buildings due to all year round 24/7 operation, health and medical needs in a restorative environment [3–5]. For example, healthcare is estimated to contribute to 4.4% of global Publisher’s Note: MDPI stays neutral net emissions [4]. Energy as an essential supply is critical to ensure reliable operation with regard to jurisdictional claims in and quality care provision at healthcare facilities, such as hospitals or residential aged published maps and institutional affil- care communities (RACs). For example, electricity for space cooling and heating tends to iations. account for the highest energy use in Australian RACs [6–8]. Nonetheless, the COVID-19 pandemic has disrupted the way aged care communities operate, promoted vulnerability and resilience considerations [9] and also resulted in changes in energy use and peak demand [10]. Copyright: © 2021 by the authors. There have been multiple global reports, at an electrical power system level, of reduced Licensee MDPI, Basel, Switzerland. electricity demand since the pandemic started. France, Germany, Italy, Spain and UK This article is an open access article experienced 5% to 10% electricity demand reduction for most of 2020 [11,12]. During distributed under the terms and the COVID-19 lockdown in April 2020, New York City’s peak demand dropped by 12% conditions of the Creative Commons and 20% respectively for weekends and weekdays [13]. In Saskatchewan’s power system Attribution (CC BY) license (https:// (Canada), 5% to 15% of daily energy use reduction was observed between April and July creativecommons.org/licenses/by/ 2020 [14]. India’s energy consumption reduced by 9% to 23% in March to May 2020 [15]. 4.0/). Buildings 2021, 11, 570. https://doi.org/10.3390/buildings11120570 https://www.mdpi.com/journal/buildings Buildings 2021, 11, 570 2 of 17 Similar to the power system level, changes to energy use have been reported in indus- try, commercial and residential sectors. For example, energy use reductions for commercial and industry customers were seen in Australia, Belgium and India [16]. Australian busi- nesses in the state of Victoria also experienced 5% to 10% daily demand reduction in March 2020; however, for households in the same region, electricity demand in the same period increased by about 5% [17]. UK domestic energy usage was shifted to later in the day, and some increased usage occurred during March 2020 to February 2021 [18]. Increases in residential electricity demand were also observed in lockdown periods in Canada, Ireland and USA [19,20]. These outcomes were not unexpected, given the nature of lockdowns. There has been limited research on energy and peak demand changes in COVID- 19-impacted healthcare facilities. Further, RACs are both homes and healthcare facilities, containing residential and commercial features. Residents live in those communities with general support and healthcare services provided by clinical, administrative and operational staff. These facilities may comprise a mix of resident rooms, resident shared spaces (e.g., dining rooms, activity areas, library), commercial service spaces (e.g., kitchen, laundry, building management), clinical spaces (for nursing, allied health and medical staff and consultants) and retail spaces (e.g., café, hairdresser). A previous study identified that some changes to energy use were observed during a short (6-week) COVID-19 lockdown in early 2020, but that the extent of these changes had a strong relationship with climate [10]. This paper builds on that previous work by evaluating how energy use and peak demand changed for aged care facilities in a COVID-19 year. In terms of analyzing energy use and peak demand data, statistical tools and data analytical algorithms can be used. Visualization, such as boxplots or scatterplots, can be very intuitive to present summary statistics or show a relationship [10,21]. Another way to examine change of energy use pattern is by studying how typical demand profiles look like [22]. Clustering algorithms can be quite helpful in identifying typical profiles, such as k-means clustering [23], SPSS 2-step technique (Statistical Package for Social Science software) [24] and Gaussian Mixture Model clustering [25]. Once typical profiles are identified, visualizations can show how energy is typically used across an interval, such as a day. Aged care facilities’ energy use intensity or energy key performance indicators (KPIs) are often related to a site’s total energy use, energy use per bed or per unit floor area [7,8,26,27]. These KPI studies can help support energy management decision making and invest- ment [28,29]. To facilitate comparisons among sites, a normalized energy use KPI can be helpful, such as the energy use per patient bed [30]. This work attempts to answer the following three research questions (RQ): (1) Have yearly energy use and peak demand changed during the 1st COVID-19 year for residential aged care facilities? If yes, what are the changes? (2) Have there been any changes to typical energy use patterns (i.e., demand profiles)? If yes, what are the changes, such as timing of peak demands or shapes of profiles? (3) Have energy KPI values changed when the epidemic year is compared with previous years? If yes, are there energy-saving opportunities from the KPI analysis? Please note due to lack of submetering data, the research is unable to determine what caused the changes in energy use. However, statistical methods and analysis have been used to identify significant correlation factors to the changes (more details are in Section 3.3). Following this introduction, the next section introduces the methods used to address the research questions. Then case study results are presented, and the findings are discussed. The last section concludes the paper. 2. Methods Overall, this research uses a case study approach, combining visuals, statistical tools and a clustering algorithm to identify the changes in yearly energy use and demand profiles for four RACs. The research method flow chart (Figure 1) shows the first step uses Buildings 2021, 11, x FOR PEER REVIEW 3 of 17 Overall, this research uses a case study approach, combining visuals, statistical tools and a clustering algorithm to identify the changes in yearly energy use and demand pro- files for four RACs. The research method flow chart (Figure 1) shows the first step uses descriptive and summary statistics to provide an overview, such as yearly energy use and peak demands to answer RQ1. To answer RQ2, a Gaussian Mixture Modeling clustering algorithm is applied to an- alyze five years of 30-min interval electricity demand data to identify typical demand pro- files for each community. The demand data are recorded from the communities’ main gate meters which are utility revenue meters of high accuracy (Australian Standard 62052 and 62053). Based on the identified typical demand profiles, typical magnitudes of energy use, timing of peak demands and shapes of energy use patterns are analyzed. A clustering approach, rather than an average profile or usage distribution across hours of the day, can identify typical profiles with both magnitudes and timing infor- mation linked and unlost. Magnitudes and timing may review behavior change (please note this study does not have ethics approval to proceed to human behavior studies; no behavior study is conducted in this research). Moreover, the clustering algorithm can dis- tinguish typical profiles in relationship to climate/seasonal factors, such as temperature measurements. Clustering is data-driven. For example, the clustering outcomes are based on real temperature measurements and a site’s main energy meters data over five years Buildings 2021, 11, 570 3 of 17 of time, and no arbitrary decision is made regarding defining seasons or high/low tem- peratures. The third step identifies and analyzes the changes of a few energy key performance descriptive and summary statistics to provide an overview, such as yearly energy use and indicators for those case study communities. A range of energy use indicators in relation- peak demands to answer RQ1. ship with bed numbers and climate conditions are considered. Yearly changes overview Typical demand profiles Energy KPIs •Descriptive and •30min demand data •Comparing energy use summary statistics KPIs in the COVID-19 •Identify typical demand year and previous years •Yearly electricity use profiles and observe features, e.g. peaks and •Peak demands timing Figure 1. Research method flow chart. Figure 1. Research method flow chart. 2.1. Case Study Selection To answer RQ2, a Gaussian Mixture Modeling clustering algorithm is applied to analyze five years of 30-min interval electricity demand data to identify typical demand Four RACs in Southeast Queensland, Australia were selected as the case studies. The profiles for each community. The demand data are recorded from the communities’ main rationale behind the selection of Southeast Queensland as the context for the case studies gate meters which are utility revenue meters of high accuracy (Australian Standard 62052 include the region hosting an increasing number of older populations and the clusters of and 62053). Based on the identified typical demand profiles, typical magnitudes of energy both naturally occurring communities and villages for aged population [31,32]. use, timing of peak demands and shapes of energy use patterns are analyzed. The principle for selecting those aged care communities is to minimize factors that A clustering approach, rather than an average profile or usage distribution across may have impacts on energy use and peak demands. These selected communities: hours of the day, can identify typical profiles with both magnitudes and timing information • Are in the same climate zone with warm humid summer and mild winter (Australian linked and unlost. Magnitudes and timing may review behavior change (please note this Climate Zone 2 [33]). study does not have ethics approval to proceed to human behavior studies; no behavior • Have similar building styles and demographics. study is conducted in this research). Moreover, the clustering algorithm can distinguish • Have been subject to the same government lockdown measures across the 1st typical profiles in relationship to climate/seasonal factors, such as temperature measure- COVID-19 year (February 2020 to January 2021). There have been different levels of ments. Clustering is data-driven. For example, the clustering outcomes are based on real restrictions and lockdowns [34]. More detailed timing and restriction levels are in temperature measurements and a site’s main energy meters data over five years of time, Figure 2. and no arbitrary decision is made regarding defining seasons or high/low temperatures. • Are operated by the same not-for-profit organization with the same operational and The third step identifies and analyzes the changes of a few energy key performance in- health provision guidelines (such as restrictions for onsite group activities, exit and dicators for those case study communities. A range of energy use indicators in relationship entry to communities). with bed numbers and climate conditions are considered. 2.1. Case Study Selection Four RACs in Southeast Queensland, Australia were selected as the case studies. The rationale behind the selection of Southeast Queensland as the context for the case studies include the region hosting an increasing number of older populations and the clusters of both naturally occurring communities and villages for aged population [31,32]. The principle for selecting those aged care communities is to minimize factors that may have impacts on energy use and peak demands. These selected communities: Are in the same climate zone with warm humid summer and mild winter (Australian Climate Zone 2 [33]). Have similar building styles and demographics. Have been subject to the same government lockdown measures across the 1st COVID- 19 year (February 2020 to January 2021). There have been different levels of restrictions and lockdowns [34]. More detailed timing and restriction levels are in Figure 2. Are operated by the same not-for-profit organization with the same operational and health provision guidelines (such as restrictions for onsite group activities, exit and entry to communities). Buildings 2021, 11, x FOR PEER REVIEW 4 of 17 For confidentiality reasons, those communities in Table 1 are anonymized to not re- veal their names or locations. In this paper, energy use for these communities is electrical energy for stationary purposes, excluding hot water (provided by gas heating). Table 1. RAC community case studies. Number of No. Communities Climate Beds Building Demographics (2020) 1 Community S • Warm humid 140 2 Community P summer, mild 100 • Single-floor build- 3 Community L winter (Zone 2) 94 ings • In Southeast • Private en suites • Similar demo- Queensland re- • Central air condi- graphic gion tioning for shared • Typical mean • From 3 local gov- spaces age of residents is 4 Community M 60 ernment areas • Similar medi- 75 and above (18,821 km area cal/support equip- with 3.07 million Buildings 2021, 11, 570 4 of 17 ment people) Figure Figure 2. 2. Timing Timing of of lock lockdowns downand s and re restriction striction leve levels. ls. L Level evel 0: 0: pr pre-COVID-19; e-COVID-19; 1 1: : cau caution; tion; 2: in 2: intermediate; termediate; 3: lo 3: ck lodown ckdown. . For confidentiality reasons, those communities in Table 1 are anonymized to not reveal Figure 2 provides an overview of lockdown timing and restriction levels across the their names or locations. In this paper, energy use for these communities is electrical energy first COVID-19 year. There have been different levels of restrictions and lockdowns for stationary purposes, excluding hot water (provided by gas heating). throughout the year. Level 3 is the complete lockdown, meaning no visitor is allowed and an exception is medical emergency. Level 2 is an intermediate level with some restrictions Table 1. RAC community case studies. on visitors, such as age limits, flu vaccine and health status [34]. Number of Beds No. Communities 2.2. Y Climate early Changes Building Demographics (2020) In order to address the question of changes for yearly energy use and peak demand 1 Community S Warm humid summer, mild 140 Single-floor buildings Similar for RACs, statistical methods are used, including: summary statistics (boxplots) for daily 2 Community P winter (Zone 2) 100 Private en suites demographic 3 Community L In Southeast Queensland region 94 Central air conditioning Typical mean electricity use and peak demands in each year as well as annual total electricity use. From 3 local government areas for shared spaces age of residents Peak demand is the highest rate of using electricity in a defined time duration, such 4 Community M 60 Similar medical/support is 75 and above (18,821 km area with as a highest kilowatt reading for a month or a year. Peak demand is important because of equipment 3.07 million people) its impact on electrical network, for example high power and high current flow may vio- late the thermal limits of cables and transformers. Temperature statistics (e.g., cooling de- Figure 2 provides an overview of lockdown timing and restriction levels across the first gree days (CDD24) in this climatic context) are incorporated into electricity use and peak COVID-19 year. There have been different levels of restrictions and lockdowns throughout the year. Level 3 is the complete lockdown, meaning no visitor is allowed and an exception is medical emergency. Level 2 is an intermediate level with some restrictions on visitors, such as age limits, flu vaccine and health status [34]. 2.2. Yearly Changes In order to address the question of changes for yearly energy use and peak demand for RACs, statistical methods are used, including: summary statistics (boxplots) for daily electricity use and peak demands in each year as well as annual total electricity use. Peak demand is the highest rate of using electricity in a defined time duration, such as a highest kilowatt reading for a month or a year. Peak demand is important because of its impact on electrical network, for example high power and high current flow may violate the thermal limits of cables and transformers. Temperature statistics (e.g., cooling degree days (CDD24) in this climatic context) are incorporated into electricity use and peak demand analysis, as energy use can be highly correlated to temperature [10,35]. After obtaining an overall understanding of annual energy use and peak demand in these communities, the next step is to analyze typical demand profiles. 2.3. Typical Demand Profiles To identify typical demand profiles, Gaussian Mixture Model clustering method (GMM) is used [36,37]. Critical evaluation can be then conducted to examine changes to typical demand profiles, magnitudes of demands and peak demand timing. GMM clustering process starts with setting up an input matrix. Demand data X = fx1, x2, . . . , x48g is the input set to the clustering algorithm. An iterative expec- Buildings 2021, 11, 570 5 of 17 tation maximization algorithm (EM) is incorporated in GMM [38,39]. An EM has two steps: expectation step (E step) and maximization step (M step). In an E step, Equation (1) calculates posterior probabilities g based on w (model jk k weights), m (mean) and S (covariance). Nomenclature have been included at the end of k k the paper. w f (xjm , S ) k k k k g = (1) jk w f(xjm , S ) k k k k=1 Note w 2 (0, 1) and å w = 1. k k k=1 In an M step, weights, mean and covariance are updated with the previous E step posterior probabilities using Equations (2) to (4): w = (2) m = g x (3) k å jk n j=1 S = g (x m )(x m ) (4) n n k å jk k k j=1 Iteration runs through the E step and the M step until a convergence is obtained without modifications to GMM configurations. Equations (5) to (7) are GMM clustering results. In the next step, m , the mean values are used to find typical profiles. y(x) = w f (xjm , S ) (5) å k k k k k=1 d 1 2 2 f (xjm , S ) = (2p) jS j exp (x m ) S (x m ) (6) k k k k k k w = 1 (7) å k k=1 Equation (8) is used in identifying typical profiles C . Cluster k’s centroid values are m . Observations are x . The percentages of clusters within a dataset are l . k j k C = x , l = w k k (8) Subject to : min jx m j , 8 j 2 N, k 2 N(1, K) j k C are identified when a minimum distance exists between x and m . Index j is a k j k positive integer, and its value is between 1 and the size of cluster k. Index k is another positive integer being between 1 and K (the number of clusters). 2.4. Energy Key Performance Indicators With the knowledge of the previous summary statistics and clustering analysis, a range of energy key performance indicators can be studied for case communities [7,8,27,28], such as site maximum peak demand in a year (kW), site electricity use per bed per day (kWh/bed/day), energy use per bed per cooling degree day (kWh/bed/CDD) and energy use per bed per heating degree day (kWh/bed/HDD). The selection of energy KPI per CDD or per HDD may depend on seasons or climates. For example, energy KPI per CDD may be meaningful for tropical or sub-tropical regions with dominant cooling needs. When energy use is normalized to a per bed or per degree day level, a fairer energy KPI comparison among sites may become feasible (more details are in Section 3.3). Buildings 2021, 11, x FOR PEER REVIEW 6 of 17 2.4. Energy Key Performance Indicators With the knowledge of the previous summary statistics and clustering analysis, a range of energy key performance indicators can be studied for case communities [7,8,27,28], such as site maximum peak demand in a year (kW), site electricity use per bed per day (kWh/bed/day), energy use per bed per cooling degree day (kWh/bed/CDD) and energy use per bed per heating degree day (kWh/bed/HDD). The selection of energy KPI per CDD or per HDD may depend on seasons or climates. For example, energy KPI per CDD may be meaningful for tropical or sub-tropical regions with dominant cooling needs. When energy use is normalized to a per bed or per degree Buildings 2021, 11, 570 6 of 17 day level, a fairer energy KPI comparison among sites may become feasible (more details are in Section 3.3). 3. Case Study Results and Discussion 3. Case Study Results and Discussion This section presents, in order, the results of the yearly changes, the typical demand This section presents, in order, the results of the yearly changes, the typical demand profiles and the analysis of energy KPIs. profiles and the analysis of energy KPIs. 3.1. Yearly Changes 3.1. Yearly Changes The yearly energy use of each case study RAC is summarized in Error! Reference The yearly energy use of each case study RAC is summarized in Table 2 and depicted source not found. and depicted in Figure 3. Overall, the total energy use in the COVID- in Figure 3. Overall, the total energy use in the COVID-19 year tends to be lower than the 19 yea previous r tends to be l years, with ower mean tha rn eductions the prevranging ious year frs, om w2.3% ith mto ean 10.0%. reductions ranging from 2.3% to 10.0%. Table 2. Yearly energy use in MWh. Table 2. Yearly energy use in MWh. Year Community S Community P Community L Community M Year Community S Community P Community L Community M 2016 (MWh) 809 764 755 676 2016 (MWh) 809 764 755 676 2017 (MWh) 821 794 743 688 2017 (MWh) 821 794 743 688 2018 (MWh) 795 794 725 695 2018 (MWh) 795 794 725 695 2019 (MWh) 810 738 731 676 2019 (MWh) 810 738 731 676 COVID-19 year (MWh) 791 705 668 615 COVID-19 year (MWh) 791 705 668 615 COVID-19 y COVID-19 e year ar vs vs 2.26% 8.70% 9.55% 9.97% −8.70% −9.55% −9.97% −2.26% previous 4-year mean previous 4-year mean Figure 3. Four communities’ yearly energy use in MWh. Figure 3. Four communities’ yearly energy use in MWh. Peak demands for those community cases are presented in Table 3, showing peak demand reductions ranging from 3% to 12%. The peak demands measured in the COVID-19 year were the lowest on record for these facilities. Table 3. Maximum peak demands over years (kW). Yearly Peak Demand Community S Community P Community L Community M 2016 (kW) 252 254 206 213 2017 (kW) 244 289 201 210 2018 (kW) 246 263 196 215 2019 (kW) 244 246 195 199 COVID-19 year (kW) 239 232 186 194 COVID-19 year vs 3.20% 11.68% 6.78% 7.52% previous 4-year mean Buildings 2021, 11, x FOR PEER REVIEW 7 of 17 Peak demands for those community cases are presented in Table 3, showing peak demand reductions ranging from 3% to 12%. The peak demands measured in the COVID- 19 year were the lowest on record for these facilities. Table 3. Maximum peak demands over years (kW). Yearly Peak Demand Community S Community P Community L Community M 2016 (kW) 252 254 206 213 2017 (kW) 244 289 201 210 2018 (kW) 246 263 196 215 2019 (kW) 244 246 195 199 COVID-19 year (kW) 239 232 186 194 COVID-19 year vs −3.20% −11.68% −6.78% −7.52% previous 4-year mean The literature reports that energy use and peak demands are often highly correlated with outdoor ambient temperature [40–42]; therefore, the temperature distribution was also studied for the same duration. Figure 4 shows the ambient daily maximum temperature boxplots for the case stud- ies. All years’ highest daily maximum temperatures are shown on the top of each boxplot. Red crosses above the top whiskers of boxplots or below the bottom whiskers of boxplots are outliers. Red horizontal lines inside boxplots are the medium values. Buildings 2021, 11, 570 7 of 17 Community S, P and L share the same temperature observation datasets and Com- munity M uses temperature data from another government observation station [43]. For Community S, P and L, the COVID-19 year’s maximum temperature had been 2.2 to 5.2 ℃ lower than the previous years. For Community M, the COVID-19 year’s maximum The literature reports that energy use and peak demands are often highly correlated temperature had been 1.7 to 4.9 ℃ lower than the previous years. There is also a change with outdoor ambient temperature [40–42]; therefore, the temperature distribution was of upper end temperature distribution which may be a significant factor for the general also studied for the same duration. downward trend in the COVID-19 year’s energy use (Table 2). Figure 4 shows the ambient daily maximum temperature boxplots for the case studies. For both temperature plots in Figure 4, 2016 had the highest median daily maximum All years’ highest daily maximum temperatures are shown on the top of each boxplot. Red temperatures (the red line in the middle of each boxplot). From 2017 to the COVID-19 crosses above the top whiskers of boxplots or below the bottom whiskers of boxplots are year, the median daily maximum temperatures remained relatively stable. outliers. Red horizontal lines inside boxplots are the medium values. (a) (b) Figure Figure 4. 4. Daily Daily maxi maximum mum te temperatur mperature d e distribution istribution ac acr ros oss s ssites ites and years and years. . (a) Temperature for Comm (a) Temperature for Community unity S, P, S, L; P, ( L; b) Temperature for Community M. (b) Temperature for Community M. Community S, P and L share the same temperature observation datasets and Com- Another potential reason for the variation in case communities’ yearly energy use munity M uses temperature data from another government observation station [43]. For and peak demands is occupancy change. Table 4 shows 3 to 9% decreases in occupancy; Community S, P and L, the COVID-19 year ’s maximum temperature had been 2.2 to however, those changes are not in portion with communities’ yearly energy use change 5.2 °C lower than the previous years. For Community M, the COVID-19 year ’s maximum (in Table 2Error! Reference source not found.). For example, Community S had the high- temperature had been 1.7 to 4.9 °C lower than the previous years. There is also a change est percentage reduction in occupancy, but the lowest percentage reduction in energy use of upper end temperature distribution which may be a significant factor for the general and peak demand. These results are consistent with another separate analysis into the downward trend in the COVID-19 year ’s energy use (Table 2). correlation between occupancy and RACs’ total energy use that revealed that occupancy For both temperature plots in Figure 4, 2016 had the highest median daily maximum may not be a significant influencing factor, but that temperature is highly correlated with temperatures (the red line in the middle of each boxplot). From 2017 to the COVID-19 year, the median daily maximum temperatures remained relatively stable. Another potential reason for the variation in case communities’ yearly energy use and peak demands is occupancy change. Table 4 shows 3 to 9% decreases in occupancy; however, those changes are not in portion with communities’ yearly energy use change (in Table 2). For example, Community S had the highest percentage reduction in occupancy, but the lowest percentage reduction in energy use and peak demand. These results are consistent with another separate analysis into the correlation between occupancy and RACs’ total energy use that revealed that occupancy may not be a significant influencing factor, but that temperature is highly correlated with RACs’ electrical energy use across climate zones [44]. This is likely because space conditioning is typically the highest energy service in these facilities. Table 4. Mean monthly occupancy. Year Community S Community P Community L Community M 2016–2019 mean 98.62% 97.37% 99.36% 97.39% occupancy COVID-19 year 89.49% 94.38% 95.95% 89.30% occupancy % changes 9.13% 2.99% 3.41% 8.09% Boxplots for daily peak demands and daily energy uses are presented in Figure 5. These boxplots, for each of the four RACs, show that medium peak demand values are relatively stable across all years, but that maximum peak demand values dropped slightly in the COVID-19 year. In terms of daily energy use, all communities, except Community S, Daily Max Temp ℃ Buildings 2021, 11, x FOR PEER REVIEW 8 of 17 RACs’ electrical energy use across climate zones [44]. This is likely because space condi- tioning is typically the highest energy service in these facilities. Table 4. Mean monthly occupancy. Year Community S Community P Community L Community M 2016–2019 mean 98.62% 97.37% 99.36% 97.39% occupancy COVID-19 year 89.49% 94.38% 95.95% 89.30% occupancy % changes −9.13% −2.99% −3.41% −8.09% Boxplots for daily peak demands and daily energy uses are presented in Figure 5. These boxplots, for each of the four RACs, show that medium peak demand values are Buildings 2021, 11, 570 8 of 17 relatively stable across all years, but that maximum peak demand values dropped slightly in the COVID-19 year. In terms of daily energy use, all communities, except Community S, had lower maximum daily energy use and smaller energy use variance in the COVID- had lower maximum daily energy use and smaller energy use variance in the COVID-19 19 year compared with the previous years. year compared with the previous years. (a) (b) (c) (d) Figure 5. Daily energy use and peak demands comparisons across 5 years; (a) Community S; (b) Community P; (c) Com- Figure 5. Daily energy use and peak demands comparisons across 5 years; (a) Community S; (b) Community P; munity M; (d) Community L. (c) Community M; (d) Community L. After getting an overview of how those communities used energy in the COVID- 19 year compared with the previous years, the next section presents findings on typical demand profiles across those years. 3.2. Typical Demand Profiles To show how typically energy use had been changed in the COVID-19 year, a cluster- ing algorithm (Section 2.3) has been applied to the case studies to identify typical demand profiles over these years. Though running the algorithm, two typical profiles have been automatically identified to present daily energy use magnitudes and timing for each year: warm days’ typical demand profiles (Figure 6) and cool days’ typical demand profiles (Figure 7). Daily Energy Use (kWh) Daily Peak Demand (kW) Daily Energy Use (kWh) Daily Peak Demand (kW) Daily Energy Use (kWh) Daily Peak Demand (kW) Daily Energy Use (kWh) Daily Peak Demand (kW) Buildings 2021, 11, x FOR PEER REVIEW 9 of 17 After getting an overview of how those communities used energy in the COVID-19 year compared with the previous years, the next section presents findings on typical de- mand profiles across those years. 3.2. Typical Demand Profiles To show how typically energy use had been changed in the COVID-19 year, a clus- tering algorithm (Section 2.3) has been applied to the case studies to identify typical de- mand profiles over these years. Though running the algorithm, two typical profiles have been automatically identified to present daily energy use magnitudes and timing for each year: warm days’ typical demand profiles (Figure 6) and cool days’ typical demand pro- files (Figure 7). For warm days’ typical profiles, the magnitude of the demand profile in the COVID- 19 year is lower than previous years for Communities M and L and similar to the 2019 magnitude for Community P. The magnitude of the demand profile for Community S was similar to that of the previous 3 years (2017–2019) but higher than 2016. The timing of the peak demand is similar across all years for each community. For cool days’ typical profiles, daytime energy use magnitudes were mostly lower for Community M. Similarly, Community P had lower daytime demand on cool days compared with its previous years, except in comparison with 2019. For Community L’s Buildings 2021, 11, 570 9 of 17 typical demand profile on cool days, the morning peak demands appeared higher than the previous years. Typical 1—warm days: Typical 1—warm days: (a) (b) Typical 1—warm days: Typical 1—warm days: (c) (d) Figure 6. Warm days’ typical demand profiles. (a) Community S; (b) Community P; (c) Community M; (d) Community L. Figure 6. Warm days’ typical demand profiles. (a) Community S; (b) Community P; (c) Community M; (d) Community L. For warm days’ typical profiles, the magnitude of the demand profile in the COVID- 19 year is lower than previous years for Communities M and L and similar to the 2019 magnitude for Community P. The magnitude of the demand profile for Community S was similar to that of the previous 3 years (2017–2019) but higher than 2016. The timing of the peak demand is similar across all years for each community. For cool days’ typical profiles, daytime energy use magnitudes were mostly lower for Community M. Similarly, Community P had lower daytime demand on cool days compared with its previous years, except in comparison with 2019. For Community L’s typical demand profile on cool days, the morning peak demands appeared higher than the previous years. 30min Demand (kW) 30min Demand (kW) 30min Demand (kW) 30min Demand (kW) Buildings 2021, 11, 570 10 of 17 Buildings 2021, 11, x FOR PEER REVIEW 10 of 17 Typical 2—cool days: Typical 2—cool days: (a) (b) Typical 2—cool days: Typical 2—cool days: (c) (d) Figure 7. Cool days’ typical demand profiles. (a) Community S; (b) Community P; (c) Community M; (d) Community L. Figure 7. Cool days’ typical demand profiles. (a) Community S; (b) Community P; (c) Community M; (d) Community L. 3.3. Energy Key Performance Indicators 3.3. Energy Key Performance Indicators Site Site tota total lener energy use ( gy use (in in kWh) kWh) aand nd pea peak k de demand mand (in kW) have been (in kW) have been discussed in discussedpre- in previous Section 3.1. This section evaluates the benefits and limitations of using more vious Section 3.1. This section evaluates the benefits and limitations of using more nu- nuanced energy KPIs. anced energy KPIs. 3.3.1. kWh/Bed/Day 3.3.1. kWh/Bed/Day Case study RAC electricity use per bed per day is depicted with bar charts in Figure 8. Case study RAC electricity use per bed per day is depicted with bar charts in Figure Bed numbers are indicated on the right axis and shown with blue diamond shapes. The 8. Bed numbers are indicated on the right axis and shown with blue diamond shapes. The graphs clearly show that the COVID-19 year had a lower kWh/bed/year and that the graphs clearly show that the COVID-19 year had a lower kWh/bed/year and that the re- reduction is consistent with the yearly energy use data shown in Table 2 in Section 3.1. duction is consistent with the yearly energy use data shown in Table 2 in Section 3.1. This KPI is beneficial in that it shows the impacts of economy of scale: higher bed This KPI is beneficial in that it shows the impacts of economy of scale: higher bed numbers seem to indicate lower energy use per bed (each bed means a private bedroom in numbers seem to indicate lower energy use per bed (each bed means a private bedroom these communities). It is feasible to assume that this may be because there are a number of in these communities). It is feasible to assume that this may be because there are a number shared common spaces within a RAC that are required for operational and care provision of shared common spaces within a RAC that are required for operational and care provi- purposes, regardless of the total number of beds. Another advantage of this KPI is that sion purposes, regardless of the total number of beds. Another advantage of this KPI is it is relatively easy to obtain the data needed to calculate this KPI. For example, aged that it is relatively easy to obtain the data needed to calculate this KPI. For example, aged care and hospitals across all jurisdictions of Australia can provide the bed numbers in a care and hospitals across all jurisdictions of Australia can provide the bed numbers in a consistent manner. consistent manner. 30min Demand (kW) 30min Demand (kW) 30min Demand (kW) 30min Demand (kW) Buildings 2021, 11, x FOR PEER REVIEW 11 of 17 Buildings 2021, 11, 570 11 of 17 The limitation of this KPI, however, is that KPI does not enable detailed evaluation of the energy efficiency of the site, its systems and services. It does not allow for “like to The limitation of this KPI, however, is that KPI does not enable detailed evaluation of the energy efficiency of the site, its systems and services. It does not allow for “like to like” like” comparison to enable benchmarking of energy efficiency performance. Another as- comparison to enable benchmarking of energy efficiency performance. Another aspect is pect is that this KPI is not relevant to climate conditions and a need may come up to have that this KPI is not relevant to climate conditions and a need may come up to have other other KPIs in relation to climate conditions, such as energy use per bed per cooling degree KPIs in relation to climate conditions, such as energy use per bed per cooling degree days days or heating degree days (kWh/bed/CDD or kWh/bed/HDD). or heating degree days (kWh/bed/CDD or kWh/bed/HDD). Figure 8. Energy KPI: kWh/bed/day comparisons. Figure 8. Energy KPI: kWh/bed/day comparisons. 3.3.2. kWh/Bed/CDD24 A different picture comes up when the KPI energy uses per bed per cooling degree day 3.3.2. kWh/Bed/CDD24 (kWh/bed/CDD24) is used. Cooling Degree Days (CDD) are examined because cooling is A different picture comes up when the KPI energy uses per bed per cooling degree the dominant space conditioning need for the case study’s climate zone (Zone 2—warm, day (kWh/bed/CDD24) is used. Cooling Degree Days (CDD) are examined because cool- humid summer and mild winter [33]). Heating is rarely needed for the case study, so ing is the dominant space conditioning need for the case study’s climate zone (Zone 2— energy use per bed per heating degree day (HDD) is not provided here. This HDD-related energy use KPI can be a future research topic for studying RACs in cooler climate zones. warm, humid summer and mild winter [33]). Heating is rarely needed for the case study, The base 24 °C is used because it is a recommended value by the Australian Bureau of so energy use per bed per heating degree day (HDD) is not provided here. This HDD- Meteorology [45] and is a more suitable cooling setting for the demographics, rather than related energy use KPI can be a future research topic for studying RACs in cooler climate commonly used 21 to 23 °C space cooling setting in commercial buildings. zones. The base 24 ℃ is used because it is a recommended value by the Australian Bureau Despite the RACs’ yearly energy reduction in the COVID-19 year (Table 2), Table 5 of Meteorology [45] and is a more suitable cooling setting for the demographics, rather shows that the kWh/bed/CDD24 actually increased (1.03% to 11.39%) for three of the RACs. than commonly used 21 to 23 ℃ space cooling setting in commercial buildings. Table 5. Energy use intensity per degree day. Despite the RACs’ yearly energy reduction in the COVID-19 year (Table 2), Table 5 shows that the kWh/bed/CDD24 actually increased (1.03% to 11.39%) for three of the KPI Year Community S Community P Community L Community M RACs. 2016–2019 mean 11.20 14.91 23.98 20.67 kWh/bed/CDD24 in warm months COVID-19 year 12.48 15.06 24.55 19.72 Table 5. Energy use intensity per degree day. (Nov. first year to COVID-19 year vs. +11.39% +1.03% +2.37% 4.57% March next year) previous 4-year mean KPI Year Community S Community P Community L Community M 2016–2019 mean 11.20 14.91 23.98 20.67 kWh/bed/CDD24 COVID-19 year 12.48 15.06 24.55 19.72 in warm months COVID-19 year vs. (Nov. first year to previous 4-year +11.39% +1.03% +2.37% −4.57% March next year) mean Buildings 2021, 11, 570 12 of 17 To find out why the kWh/bed/CDD24 increased for three communities, further critical analysis is conducted to understand how this KPI is calculated and what are the potential factors for the increase. The KPI is calculated with a site total energy use divided by its bed number and divided by the number of CDD24. An increase in the KPI may be because of an increase in the site total energy use, a decrease in the bed number or a decrease in CDD24 (or a combination of these factors). There is a decrease in the case communities’ total energy use, and the bed number remained stable for these case RACs. Then, the only possible factor for the increase in kWh/bed/CDD24 is the CDD24 values. CDD24 is calculated based on temperature datasets and 24 C. When all years of data are considered as shown in the first and second row of Table 6, Pearson Correlation Coefficients show that the mean daily energy use of these four RACs is highly correlated with mean daily maximum temperature in each month. If the COVID-19 year energy and temperature data are taken out and studied separately, as shown on the third and fourth row of Table 6, temperature remains as a significant factor for energy use. Table 6. Pearson correlation coefficients (temperature and monthly energy use). No. Description Community S Community P Community L Community M Correlation 1 0.84 0.74 0.85 0.91 (all years data) p-values 17 11 17 24 2 4.67 10 1.35 10 1.22 10 4.32 10 (all years data) Correlation 3 0.79 0.57 0.72 0.85 (COVID-19 year) p-values 3 3 4 4 2.35 10 0.05 8.59 10 5.30 10 (COVID-19 year) As such, the reduction of yearly energy use shown in Table 2 is largely correlated with a reduction in temperature (shown in Figure 4) in the COVID summer months that has been attributed to La Niña’s impacts on Eastern Australia from September 2020 to March 2021 [46]. The increase in kWh/bed/CDD24 is most likely because the reduction in RACs’ yearly energy use was less than the reduction in the CDD24 numbers. As shown in Table 7, the CDD24 values dropped over 14% for Community S, P and L and nearly 9% for Community M, compared with reductions in annual energy use ranging between 2.3% and 10% (Table 2). Table 7. Degree days calculation. Indicator Year Community S, P, L Community M 2016–2019 mean 262.62 182.78 CDD24 COVID-19 year 224.35 ( 14.57%) 166.55 ( 8.88%) The benefits of using this KPI include that it contains healthcare service provision in calculation. By using ambient temperature to calculate CDD or HDD, this KPI enables an examination on how healthcare buildings are responding to climate conditions. Then, the KPI can enable a comparison among sites for healthcare energy use in relationship to climate conditions, or a comparison for a community itself over years to see how a community’s energy use changed in different years of varied climate conditions. The limitation of these KPIs is that it needs more customization when the KPI is applied to specific sites due to climatic differences and needs in space cooling or heating. Furthermore, the base temperature values for CDD or HDD need to be carefully selected to reflect site situation. There are examples or recommended base values from standards or Buildings 2021, 11, 570 13 of 17 government bodies [45,47–49]. Another issue is that the KPI may not be reliable or robust for sites in temperate climate zones or mild seasons [10]. Because CDD (or HDD) values can be small for RACs in temperate climate zones or in mild seasons, a minor change in energy use for RACs in temperate climate zones or mild seasons may lead to significant increase or decrease for the KPI. 3.3.3. Discussion of Energy KPIs Air conditioning is often the largest energy user and a significant asset for health- care and commercial or public buildings [50–52]. Critical evaluation of the two KPIs (kWh/bed/day and kWh/bed/CDD24) reveals that those healthcare buildings could be- come more responsive to the reduced needs of space cooling if those buildings are designed with more energy-efficient and resilient features, such as natural ventilation [53] and better operation strategies [54]. Table 8 summarizes the benefits and limitations for the few energy KPIs studied in the previous sections. Easiness in data acquisition can be a quite common advantage for using annual energy use, annual peak demand and daily energy use. However, those KPIs are not related to healthcare service delivery nor related to climate conditions. On the other side, kWh/bed/day and kWh/bed/CDD are related to healthcare service delivery with bed numbers in calculation. kWh/bed/CDD is further dependent on climate conditions and can be more useful in revealing energy efficiency opportunities. Table 8. Summary of benefits and limitations. KPI Benefits Limitations - Easiness in data acquisition for generating the KPI Annual energy use - Provides a yearly overview (total MWh in a year) - Relevant to site energy bills and carbon - Not related to healthcare footprint service delivery - Easiness in data acquisition for generating - Not relevant to climate conditions the KPI - No seasonal/monthly variation Annual peak demand - Reveals the most significant impact to grid - No detailed evaluation of site’s systems (the highest kW in a year) infrastructure or services - Relevant to peak demand charge if demand tariff is applicable [36] Daily energy use - Easiness in data acquisition for generating (kWh/day) the KPI - Relates to healthcare service delivery - Not relevant to climate conditions - Enables a comparison among sites or a site - No detailed evaluation of site’s systems kWh/bed/day itself over years or services - Relatively easy for data acquisition to calculate - No comparison for benchmarking the KPI energy efficiency - Relates to healthcare service delivery - Need customization and careful - Enables a comparison among sites or a selection of base temperature values for comparison of a site itself over years in relation each site kWh/bed/CDD to varied climate conditions - May not be suitable for sites in - Highly relevant to air conditioning needs temperate climate or mild seasons - May reveal opportunities for better building - Need more data than other KPIs, e.g., design or operational improvement weather observations For analyzing energy performance of aged care communities, a combination of these KPIs can be helpful. However, none of these KPIs still allow for comparison to establish healthcare industry benchmarks of best practice, and none relate to the service levels provided or to the healthcare plan for the residents. Further research is planned to fill the gaps. Buildings 2021, 11, 570 14 of 17 4. Conclusions Energy is an essential service for our industries, businesses and societies [55]. In- vestigation into aged care energy use and peak demand changes during the COVID-19 pandemic, hence, is critical, particularly given the vulnerable groups such as people resid- ing in aged care facilities. A previous study identified a general downward trend for subtropical aged care communities’ energy use and power demand under the first sCOVID-19 lockdown in 2020 [10]. This research has extended the energy use and peak demand study into a whole COVID-19 year and quantified energy and peak demand differences in comparison with previous years for the aged care cases. Complete yearly datasets contain seasonal and yearly differences. The analysis based on yearly datasets can be more useful in energy management decision making compared to analysis based on short periods of lockdowns. This research has identified a general trend of reduction in case studies’ annual energy use and peak demands and discovered that the change of energy use and peak demand had been highly correlated with the temperature changes from La Niña’s impact on Eastern Australia from September 2020 to early 2021. Regardless of the pandemic’s impact, climate has been a significant factor in the energy use of aged care facilities. A limitation of this research is that this research is based on available energy and climate data. There had been no controlled experiment in distinguishing impact from climate conditions and impact from COVID-19-related community operation guidelines. This study confirms that climate is a significant and high-correlation factor to aged care facilities’ energy use, prior to and during the pandemic. A few key energy performance indicators’ benefits and limitations are summarized in relation to energy management decisions, climate and healthcare operation. KPIs in relation to climate conditions may help review energy efficiency and investment opportunities. Another study is on the way focusing on how energy KPIs and health are linked. Author Contributions: Conceptualization, A.L. and W.M.; methodology, A.L.; software, W.M.; validation, A.L., W.M. and S.Z.; formal analysis, A.L. and S.Z.; investigation, A.L. and S.Z.; resources, W.M. and J.C.; data curation, J.C.; writing—original draft preparation, A.L.; writing—review and editing, T.Y. and Y.D.; visualization, A.L.; supervision, W.M.; project administration, W.M.; funding acquisition, W.M. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Australian Renewable Energy Agency (ARENA) Affordable Heating and Cooling Innovation Hub Project (i-Hub). Data Availability Statement: The climate dataset is publicly available on Australian Bureau of Meteorology site: http://www.bom.gov.au/Climate (accessed on 20 June 2021). The raw data related to case study communities are proprietary. If there is an interest in collaboration, please contact the corresponding author. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations Acronyms: COVID-19 Or COVID 2019 novel coronavirus [56] CDD Cooling degree days EM Expectation maximization algorithm Eq Equation HDD Heating degree days GMM Gaussian Mixture Model RAC Residential aged care community(i.e., care home, nursing home) KPI Key performance indicators kW Kilowatt kWh Kilowatt-hour MWh Megawatt-hour Buildings 2021, 11, 570 15 of 17 Symbols: K Number of clusters k k-th mixture component N Number of samples j j-th sample d Dimension number f Probability density function of a component x Observations y Probability density function of a mixture model C Cluster centres Cluster percentages w Mixture weights S Covariance matrix g Posteriori probability m Mean References 1. Pombo, O.; Rivela, B.; Neila, J. Life cycle thinking toward sustainable development policy-making: The case of energy retrofits. J. Clean. Prod. 2019, 206, 267–281. [CrossRef] 2. Ingrao, C.; Messineo, A.; Beltramo, R.; Yigitcanlar, T.; Ioppolo, G. 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