The Exemplary Real Time Year weather files (RTYs), current Reference Meteorological Year files (RMYs) and Ersatz Future Meteorological Years (EFMYs) used for these monthly simulations are available for purchase. This will allow clients to simulate their own designs for energy budgeting and monitoring rather than rely on analogy with the performance of these archetypical buildings and systems. Especially in mild months, small differences in energy consumptions can result in large percentage differences. Solar irradiation data courtesy of Solcast.
Archetypical buildings and systems
10-storey office
3-storey office
Supermarket
5 kW domestic
PV system
Get the Best out of our Interactive Features
This monthly report has been interactive since April 2023. Once you have scrolled to your city of interest, check out those interactive features and how they work. Click here to read about the introduction.
- 1. Choose the energy or peak demand graph to best match your building or system of interest.
- 2. Choose the weather element graph to best match the sensitivity of your building or system of interest.
- 3. Mix and match to learn about their relative importance or sensitivity
ADELAIDE
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| +44.3% | +29.3% | +33.9% | +34.2% | +21.9% | +34.4% |
| Solar PV | |||||
| +30.1% |
The solar PV simulation output was 30.1% higher than the long-term average. The cooling peak load was slightly higher than the long-term average for 3-storey and 10-storey office buildings, by 8.9% and 1.6%, respectively, and significantly higher for the supermarket, at 32.3%. Adelaide’s temperature varied greatly from hour to hour. Typically, Adelaide’s temperature warmed up in the afternoon then dropped down significantly during the night, with large differences between warm afternoon and cold night temperatures – a wide daily range. Additionally, many individual days were either unseasonally warm or cold. These two phenomena (graphed in the selection of charts below) resulted in the counter-intuitive result of higher heating and cooling energy consumptions. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Adelaide experienced a warmer May during the daytime with less humidity compared to the long-term average. The GHI was much higher than the long-term average and the wind speed was similar to the long-term average.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| -1.0 | +0.0 | +0.7 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| -5.5 | -6.7 | -6.3 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| +26.4 | +26.3 | +10.9 |
BRISBANE
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| -89.7% | +29.4% | -96.7% | +25.4% | -83.5% | +46.1% |
| Solar PV | |||||
| -7.7% |
The solar PV simulation output was 7.7% lower than the long-term average. The cooling peak load was higher than the long-term average for the 3-storey office building, 10-storey office building, and supermarket, by 19.9%, 18.0%, and 18.1%, respectively. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Brisbane experienced a more humid and slightly warmer May compared to the long-term average. The GHI and wind speed were slightly lower than the long-term average.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| +2.2 | +1.4 | +0.8 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| +8.7 | +6.6 | +2.9 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| -2.8 | -3.6 | -2.1 |
CANBERRA
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| -9.9% | +91.6% | -4.4% | +71.6% | -11.6% | +218.0% |
| Solar PV | |||||
| +1.1% |
The solar PV simulation output was 1.1% higher than the long-term average. The heating peak load was higher than the long-term average for the 3-storey office building, 10-storey office building, and supermarket, by 24.1%, 18.9%, and 8.4%, respectively. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Canberra experienced a warmer May with similar humidity compared to the long-term average. The GHI was slightly higher than the long-term average while the wind speed was similar to the long-term average.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| +0.1 | +0.7 | +1.4 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| +1.0 | +3.8 | +3.5 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| -44.6 | +0.7 | +5.5 |
DARWIN
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| N.A. | +7.1% | N.A. | -4.4% | N.A. | -1.6% |
| Solar PV | |||||
| -5.9% |
The solar PV simulation output was 5.9% lower than the long-term average. The cooling peak load was higher than the long-term average for the 10-storey office and supermarket buildings, by 1.0% and 2.2%, respectively, while it was 1.1% lower for the 3-storey office building. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Darwin experienced a less humid and warmer May compared to the long-term average. The GHI was much higher while the wind speed was lower than the long-term average.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| +0.4 | +0.0 | -0.7 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| +11.3 | +1.4 | -1.5 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| +3.0 | -4.2 | -4.5 |
HOBART
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| -17.5% | +115.8% | -14.0% | +91.1% | -15.5% | N.A. |
| Solar PV | |||||
| +19.6% |
The solar PV simulation output was 19.6% higher than the long-term average. The cooling peak load was higher than the long-term average for the 3-storey and 10-storey office buildings, by 47.4% and 67.1%, respectively. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Hobart experienced a slightly warmer and more humid May compared to the long-term average. The GHI and Wind speed were similar to the long-term average.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| +0.3 | +0.5 | +2.1 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| -2.7 | -2.1 | -2.6 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| -16.0 | +14.9 | +0.2 |
MELBOURNE
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| +54.9% | +39.6% | +49.5% | +28.9% | +7.7% | +175.1% |
| Solar PV | |||||
| +24.6% |
The solar PV simulation output was 24.6% higher than the long-term average. The heating peak load was higher than the long-term average for the 3-storey office building, 10-storey office building, and supermarket, by 27.7%, 23.2%, and 28.9%, respectively. Melbourne’s temperature varied greatly from hour to hour. Typically, Melbourne’s temperature warmed up in the afternoon then dropped down significantly during the night, with large differences between warm afternoon and cold night temperatures – a wide daily range. Additionally, many individual days were either unseasonally warm or cold. These two phenomena (graphed in the selection of charts below) resulted in the counter-intuitive result of higher heating and cooling energy consumptions. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.
Melbourne experienced similar temperatures with slightly lower humidity in May compared to the long-term average. The GHI was much higher than the long-term average in the afternoon.
Weather Index
| Temperature (°C) | ||
| Mean Min | Mean Avg | Mean Max |
| -0.3 | +0.0 | +0.9 |
| Relative Humidity (%pt) | ||
| Mean Min | Mean Avg | Mean Max |
| -4.6 | -2.3 | +0.3 |
| Daily Solar Irradiation (GHI %) | ||
| Cloudiest | Mean | Sunniest |
| +26.6 | +19.9 | +12.4 |
PERTH
Energy Index (%)
| 10-storey | 3-storey | Supermarket | |||
| Heating | Cooling | Heating | Cooling | Heating | Cooling |
| -31.3% | +54.5% | -31.5% | +47.2% | -29.5% | +88.1% |
| Solar PV | |||||
| +11.4% |
The solar PV simulation output was 11.4% higher than the long-term average. The cooling peak load was higher than the long-term average for the 3-storey office building, 10-storey office building, and supermarket, by 68.3%, 70.6%, and 76.1%, respectively. It should be noted that peak load results are highly sensitive to the particular building and HVAC design and settings – it is more appropriate to evaluate those results from a bespoke building model using our RTY data.