Westin, Park Hyatt, ShangriLa, Alexander Centre Exergy Hotels and Correctional Very relevant. data but no time series. Tas gvt buildings (OSCAR) Tas DPAC (via DCCEE) several files of TAS police stations, health services data Medium quality. Energy usage data and scale data for 4 years (2007, 2009-11). Issues with scale data. NEW NABERS data DCCEE (Hotel, Office, Schools) - second dataset received. Total Consumption information. High quality. Energy usage data for individual buildings, for one year. Stockland Properties Stockland (via Exergy) Offices and some other Relevant. Total energy usage, but not always for a full year. Scale data. UTAS Buildings 2010 UTAS (via Pitt & Sherry) Data on UTAS buildings High quality. Energy usage data for individual campus buildings for years 2008 and 2009. Scale data. Must be confidentialised. Coles Data (via Exergy) retail data Medium quality data. Energy usage data for individual supermarkets. Scale data but no time series or EUBs. SA OSCAR Data (various government agencies) SA via DCCEE SA buildings , some correctional centres, law courts, tertiary education , offices Total energy usage data for 10 years (2001-11) and scale data VIC Hospitals data Vic Dept of Health (via DCCEE) VIC Hospitals consumption, incl diesel data energy usage data for individual hospitals and centres for 6 years Scale data. Investa Property Group Monthly Energy Data (excel file) Investa (via DCCEE) 2003-2011 monthly kWh & MJ data. Has NLA and normalised consumption. Noted that some of these already included via NABERS database. High quality. Total energy usage data for individual offices, 2003-11. Scale data Australand Australand (via Exergy) Electricity & Gas monthly consumption for Australand buildings, mostly offices - time series 2008-2011 High quality. Energy usage data but only some buildings have more than one year's data. Scale data. Wesfarmers (55 sites) (via Exergy) Retail from Wesfarmers. Medium quality. Electricity data. Scale data but no time series or EUBs. GPT EEO data GPT (via Exergy) GPT offices High. These EUBs fit desired data. Scale data but no time series. 2008-
WB NT 25 REGION NSW 913 PRIVATE TEN 347.1 443.1 VIC 468 QLD 604 WA 166 BB 290.9 563.5 SA 100 TAS 101 NT 30 WB 522.3 946.6 AE.3 – Hotels Location State Population Size Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 91 1,122.5 1,422.7 VIC 62 QLD 27 WA 27 SA 19 TAS 6 ACT 13 NT 8 REGION NSW 148 VIC 65 QLD 183 WA 40 SA 22 TAS 17 NT 18 AE.4 - Retail Shopping Centre Location Population Size Category Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 186 BB 268.1 390.5 VIC 199 QLD 73 WA 71 SA 51 TAS 12 ACT 17 WB 703 802 NT 6 REGION NSW 175 VIC 103 QLD 159 WA 36 TEN 2,410.9 1,550.3 SA 33 TAS 25 NT 5
TAS 95 ACT 166 NT 59 REGION NSW 2024 VIC 1249 QLD 1734 WA 507 SA 394 TAS 270 NT 93 Table AE.8 - Public Buildings Location State Population Size Ownership Type Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 15 GOVERNMENT 787.5 843.8 VIC 16 QLD 7 WA 10 SA 6 TAS 1 ACT 11 NT 2 REGION NSW 15 PRIVATE 318.2 975.1 VIC 8 QLD 17 WA 6 SA 4 TAS 3 NT 2 Table AE.9 - Law Courts Location Population Size Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 40 VIC 25 QLD 5 WA 16 SA 12 TAS 0 ACT 19 NT 4 REGION NSW 0 VIC 0 QLD 0
WA 38 SA 0 TAS 0 NT 7 Table AE.10- Universities Location Population Size Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 105 202.4 357.4 VIC 113 QLD 55 WA 56 SA 34 TAS 10 ACT 19 NT 5 REGION NSW 153 VIC 86 QLD 105 WA 0 SA 1 TAS 1 NT 0 Table AE.11- VETS Location Population Size Standard Deviation (MJ/m2) Mean (MJ/m2) CAPITAL CITY NSW 34 1,439.5 868.5 VIC 40 QLD 16 WA 13 SA 13 TAS 3 ACT 4 NT 1 REGION NSW 30 VIC 26 QLD 23 WA 6 SA 5 TAS 4 NT 2 The results are presented in Tables through to AE.12 – AE.16, where the ratio is 0%, it means that the population size is 0 (there are no buildings within that category). To determine the minimum number of buildings required to be sampled to achieve 95% confidence level and an accuracy within 10% of the mean, multiply the corresponding ratio with the known population size. The resulting numbers suggest that where we have large ratios e.g. 98% of population, the remaining small percentage of buildings (e.g. 2%) is causing a large deviation in the mean. This is not possible as the