Tag: building modelling

Simulation of Buildings Using Real-Time Weather Data – by Veronica Soebarto

Building performance simulation is often used to assess the performance of an existing building, diagnose the main issues or identify existing problems, and investigate various strategies to ratify the problems and improve the building performance. Although it may seem straight forward, simulating an existing building is actually not simple, because, first, you need to ensure that the model accurately represents the actual building in terms of the geometry, envelope materials used and their thermal properties, as well as all the internal loads and use patterns. The second fundamental step in simulating an existing building is to compare the simulation results to measured data and to calibrate the model to minimize the discrepancies between the two.

In order to do this comparison, it is crucial to use the actual weather data file for the same period as the measured data. This is unfortunately often not done by many building simulators. Instead, they often use a standardised climate data file (e.g. TMY, EPW) that is more readily available. This is problematic, because there is no way you can adequately compare the simulation results to the data from the actual building, if you use a synthetic climate data file. Actual building performance, such as indoor temperature or cooling/heating energy use, is affected by the actual weather conditions; simulation results from running the simulation model with a standardised climate data file will only reflect the building performance as affected by those synthetic weather conditions.

Unless you install your own weather station for the location of the building you are simulating, there is almost no way you can obtain actual hourly weather data file. This is where Exemplary Energy plays a significant role in simulating existing buildings in Australia as they are able to provide the required actual weather data file for many locations in Australia. For more than 20 years, Exemplary Energy has been able to provide actual weather data files for our research at The University of Adelaide where simulating existing buildings is necessary.

Below are examples of the simulated hourly indoor temperatures versus measured data from our previous ARC Discovery Project where we monitored more than 50 homes of older people in South Australia. Exemplary Energy provided the actual weather data file (in EPW) for Adelaide (house 1) and Victor Harbor (houses 2 and 3). As can be seen here, the simulation model well represented the actual building as shown in the simulated indoor temperatures that compared well with the measured data.

Author: Professor Veronica Soebarto

School of Architecture and Civil Engineering

Faculty of Sciences, Engineering and Technology

University of Adelaide

https://researchers.adelaide.edu.au/profile/veronica.soebarto

Veronica joined the University of Adelaide in 1998 after completing a Post-Doctorate Research Associate position at Texas A&M University in College Station, Texas, USA, and PhD and Master of Architecture degrees from the same university. Her research interests span from age-friendly built environment, environmental performance assessments of buildings, building performance simulation, building monitoring, human thermal comfort, to the social dimension of sustainable design.

Images shown in this article are from a paper published in Journal of Building Performance Simulation: Arakawa Martins, L., Williamson, T., Bennetts, H., & Soebarto, V. (2022). The use of building performance simulation and personas for the development of thermal comfort guidelines for older people in South Australia. Journal of Building Performance Simulation, 15(2), 149-173.

2023 – the warmest year on record?

Mid-2023 has marked a significant period in climate change history, characterized by a series of global and regional heat records. According to an article from the Yale Climate Centre republished by Renew Economy, the single warmest day on record globally occurred in July 2023, and the month of June 2023 was also the warmest June in a 45-year dataset. Global sea surface temperatures have reached unprecedented highs, particularly in the North Atlantic, and the Antarctic sea ice has been slower to grow this year, with the extent well below previous record lows.

The primary driver of these heat records is global warming resulting from human activities. Billions of tons of greenhouse gas emissions from fossil fuel burning continue to be released into the atmosphere each year, and although the rate of emissions is increasing by “only” around 1% per year, the warming effect will persist until net-zero emissions are achieved.

The oceans store almost 90% of the energy trapped by greenhouse gases, and even a minor shift in oceanic warmth can significantly impact the atmosphere. The global ocean has been storing a larger share of energy since 2020 due to a rare 3-year-long La Niña event, and now that the earth is transitioning to El Niño conditions, additional heat is being transferred from the deep ocean to the surface and the atmosphere. El Niño events have historically led to global temperature records, and it is plausible that 2023 and 2024 could surpass previous records. The accumulation of greenhouse gases will have long-lasting and catastrophic effects on Earth’s climate for centuries or even millennia, and climate scientists highlight the urgency of reducing emissions and implementing strategies to mitigate climate change’s far-reaching consequences.

This is the strongest of corroborations for the urgent need for an updated design climate data set for building energy simulation – outdated climate data will produce inaccurate energy performance estimates and poorly optimised systems. Updates to the data are essential for both the energy efficiency provisions in the NCC Section J approval process and NatHERS as well as for design optimisation and outperformance such as NABERS Energy and Green Star ratings.

Exemplary Energy is filling that void. Full data sets for the era 1990-2022 are available now for all Australian capital cities; and our program aims to complete the data for all NatHERS Climate Zones by the end of August. In parallel with that work, we will be producing latest-15-years Reference Meteorological Years (RMYs) and eXtreme Meteorological Years (XMYs) to represent our current climate.

XMYs for HVAC: statistical analysis

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

Developing XMYs for HVAC: Is one “extreme” valid for all?

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