eXtreme Meteorological Year (XMY) data for HVAC represents conditions that produce an extremely high or low building energy consumption across an entire year, and are important for building energy simulations that can help us gain understanding on how a building performs in an “extreme” year or a year of extreme seasons (like a hot, humid summer and a cold wet winter).

In our development of XMY data for HVAC, we ran historical weather data for Canberra and Brisbane through EnergyPlus simulations to obtain heating and cooling energy data for our three archetype buildings: Supermarket, 3-storey and 10-storey office building. Preliminary analysis of the energy data indicate a reasonable correlation between the three building types, and the results fit a rough Gaussian distribution (bell curve), reassuring us that standard statistical techniques can be applied.

When performing statistical analyses, probability values (P-values) provide an insight into the statistical likelihood of a dataset or event. In our study, P-values of annual HVAC energy consumption were calculated by applying each calendar year of historical weather to estimate the average energy consumption across the three building archetypes in Canberra and Brisbane, then calculating the mean and standard deviation of the result.

Using the empirical rule, in the knowledge that the data (approximately) fits a Gaussian distribution, we estimate that 68% is within one standard deviation of the mean, 95% is within two standard deviations of the mean and 99.7% of the data is within three standard deviations of the mean. The P01, P10, P90 and P99 data are those years that would result in energy consumption that is expected to exceed 1%, 10%, 90% and 99% of the cases (respectively) in a temporal sample.

In the figure below we have ranked the average annual energy use across the three archetypes from lowest to highest, and inserted the P-values that arise from this distribution (green, yellow, orange and red bars).

One option for creating a representative P-value climate data set is to take the historical year that most closely matches the target. So, for example, we know that a P90 year results in slightly more than 20 kWh/m2 (averaged across the three archetypes). The closest historical year is 1992, which resulted in marginally less consumption than the P90 target. In fact, for this distribution, the probability of exceeding the energy consumption of 1992 is 91.9 per cent.

The closest historical data to the targeted P-values are listed below:

• P99 is 0.988 (1996)
• P90 is 0.919 (1992)
• P10 is 0.127 (2015)
• P01 is 0.014 (2017)

We think we can improve on this.

Our next step in developing XMY data for HVAC will be to devise a technique to concatenate a series of months to create an artificial year of 12 months which more closely results in the target consumption at the P1, P10, P90 and P99 level. In the process, we will remain alert for lessons indicating how best to synthesize years which may not be realistic but which will allow simulators to evaluate high heating months/seasons with high cooling months/seasons in the same 12 month simulation.

This data will be used in simulations to test the robustness of building designs. Potential applications include risk assessments for developers, owners and regulators, as well as Green Star certification, NABERS Energy commitments and other areas of Energy Efficiency (for example NCC Section J compliance or Net Zero Emissions declarations).

Readers interested to engage with us in our development of XMY data are invited to make contact soon via exemplary.energy@exemplary.com.au

In a previous article we outlined the concept of eXtreme Meteorological Year (XMY) as a hypothetical data set representing an extreme year of weather. An XMY for HVAC represents conditions that produce extremely high or low energy consumption across the entire year (note the focus on energy, as distinct from the extreme design conditions used for HVAC sizing which evaluate peak power demand).

As discussed previously, XMY data is important in building energy simulations to give us an insight into building energy performance as climatic conditions vary in the near future. If Representative Meteorological Year (RMY) data can tell us about the expected energy demand, the XMY data indicates the uncertainty due to climate variability.

In our work to develop XMYs for HVAC, one of the first questions to arise was whether an “extreme” year of climate should be considered as extreme for all building types.

To answer this, we ran historical weather data from 1990 to 2017 for Canberra and Brisbane through a series of EnergyPlus simulations to calculate heating and cooling energy data for our three archetype buildings: Supermarket, 3-storey and 10-storey office buildings. The analysis of the data were conducted for heating and cooling separately as well as combined, for individual building types and as a collective of buildings, over yearly and monthly time periods. Our results indicate a reasonable correlation between the three building types.

Correlation of HVAC energy use between the three building archetypes in Canberra

Pairwise comparison of the annual cooling energy use between the 3-storey and 10-storey office buildings in Canberra

We have also found that the energy results fit a rough Gaussian distribution (bell curve). This is important as it means that standard statistical techniques can be applied to the next stages of analysis which we will discuss further in a future post.

Readers interested to engage with us in our development of XMY data are invited to make contact soon via exemplary.energy@exemplary.com.au