Author: HongGic Oh

Examining Darwin’s Climate Trends: Implications for PV System and Building Performance

As part of the ongoing investigation into our evolving climate, we routinely process and analyse meteorological data from successive years, conducting comparative assessments to reveal emerging trends and patterns.

Our previous temporal analyses only focused on examining variations in various weather elements. However, in this latest iteration, we have incorporated the results of EnergyPlus simulations, specifically targeting HVAC systems and heating and cooling dynamics within buildings. We have also added the results of System Advisor Model (SAM) photovoltaic (PV) system simulations to enhance the comprehensiveness of our investigation.

The most recent temporal analysis was carried out for all eight capital cities, though this issue of Exemplary Advances will focus on the city of Darwin. The findings for Canberra were previously discussed in the April issue of Exemplary Advances. The analysis for other capital cities can be viewed here.

For the analysis of weather elements, we examined the temporal variations in dry bulb temperature, humidity, wind speed, global horizontal irradiation (GHI), direct normal irradiation (DNI), and total precipitation. The analysis involved averaging these elements over three 15-year periods—1990-2004, 2005-2019, and the latest 15-year period from 2009 to 2023—and then comparing the results. A comparison between data from the latest 15 years, the data corresponding to the years and months specified in Industry Standard Meteorological Year (ISMY) files, and the data exclusively from 2023 was also undertaken. ISMYs were originally developed for application in house energy rating software used in NatHERS and derive from historical Bureau of Meteorology (BOM) weather data spanning from 1990 to 2015. Over time, they have become the industry’s de facto standard. It is therefore important to compare against ISMY data, as it provides a reference to gauge alignment with established benchmarks and understand the significance of temporal variations in weather elements.

Comparing 1990-2004 with 2009-2023 showed an increase in Darwin’s mean temperature of 0.31°C, an increase to moisture of 1.22%, and a significant increase in wind speed of 14.52%. GHI had a decrease of 3.59%, and DNI had a enormous decrease of 11.1%. Meanwhile, comparing 2005-2019 with 2009-2023 showed a decrease in the mean temperature of 0.18°C, an increase to moisture of 1.38%, a decrease in wind speed of 4.46%, and a decrease in GHI and DNI of 2.36% and 5.08%, respectively. The small incline in mean temperature, GHI, and DNI for 2005-2019 vs 2009-2023 is likely a result of 2020-2022 experiencing comparatively higher annual average dry bulb temperatures and lower GHI, and DNI when compared to other years.

Total precipitation in 2009-2023 averaged 2.79% lower than in 1990-2004. and 0.27% lower than the 2005-2019 period.

The annual Cooling energy consumption reveals intriguing patterns across various archetypes. From 1990 to 2023, as well as in the periods of2005-2019 and 2009-2023, there is a notable upward trend for all archetypes. However, only during the period of 1990-2004, a decline in trends is observed for all archetypes. This indicates the notable differences in temperature, moisture and precipitation trend within the recent 2009-2023 period compared to older 15-year periods, and ISMY.

Exemplary Weather and Energy (EWE) Index May 2024

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 (%)

WordPress Data Table Plugin

The solar PV simulation output results were 28.1% higher than the long-term average. The cooling peak load was lower than the long-term average for all archetypes (7.7%, 15.8% and 21.7% for 3-storey office, 10-storey office and supermarket 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.

Adelaide experienced a less humid of normal temperatures May compared to the long-term average. The GHI and wind speed were comparably similar to the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

BRISBANE

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 14.4% lower than the long-term average. The cooling peak load was 19.9%, 22.2% lower for the 3-storey office and 10 storey office while 3.3% higher for supermarket 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 comparably warmer and very humid May than the long-term average. The GHI and wind speed were lower than the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

CANBERRA

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 5.3% lower than the long-term average. The heating peak load was higher than the long-term average for all archetypes (42.2%, 69.0% and 9.6% for 3-storey office, 10-storey office and supermarket 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 more humid and slightly cooler May compared to the long-term average. The GHI and wind speed were higher than the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

DARWIN

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 2.4% lower than the long-term average. The cooling peak load was 5.6%, and 4.5% lower for the 3-storey office and 10 storey office while 1.5% higher for supermarket 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.

Darwin experienced a slightly warmer and less humid May than the long-term average. The GHI and wind speed were slightly higher than the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

HOBART

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 10.2% lower than the long-term average. The heating peak load was 12.7%, and 12.3% higher for the 3-storey office and 10 storey office while 6.1% lower for supermarket 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 more humid of normal temperatures May compared to the long-term average. The GHI and wind speed were similar to the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

MELBOURNE

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 12.8% higher than the long-term average. The cooling peak load was lower than the long-term average for all archetypes (33.1%, 55.3% and 86.6% for 3-storey office, 10-storey office and supermarket 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.

Melbourne experienced a more humid and slightly cooler May compared to the long-term average. The GHI was higher than the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

PERTH

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 0.8% lower than the long-term average. The cooling peak load was higher than the long-term average for all archetypes (48.1%, 50.7% and 41.8% for 3-storey office, 10-storey office and supermarket 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.

Perth experienced a less humid and warmer May compared to the long-term average. The GHI and wind speed were much higher than the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

SYDNEY

Energy Index (%)

WordPress Data Table Plugin

The solar PV simulation output results were 17.0% lower than the long-term average. The cooling peak load was lower than the long-term average for all archetypes (18.5%, 21.5% and 17.9% for 3-storey office, 10-storey office and supermarket 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.

Sydney experienced a more humid of normal temperatures May compared to the long-term average. The GHI was lower while wind speed was similar to the long-term average.

Weather Index

WordPress Data Table Plugin
WordPress Data Table Plugin
WordPress Data Table Plugin

Highway to Hell: Climate Change and Australia’s Future

Review by Trevor Lee

This is the title of the recently published Quarterly Essay (QE 94) by Joëlle Gergis.

It is a thoroughly researched and impassioned work made all the more timely by last week’s declaration by our federal coalition opposition that they intend to renege on Australia’s commitment to the Paris Agreement COP 27 including the deep cuts to our greenhouse gas emissions by 2030 if they form government after the elections due next May. They do, however, remain committed to (the easier to say but harder to do) Net Zero Emissions by 2050. Here are a few excerpts from QE 94:

“Climate modelling studies have shown that an increase of ‘just’ 2°C in the Earth’s average temperature will lead to days above 50°C in Sydney and Melbourne as early as the 2040s …” p24

“… the International Monetary Fund estimates that in 2022, world governments spent around $7 trillion on fossil fuel subsidies.” p33

“… our failure to genuinely reduce our emissions is masked … (by using) a reference year of 2005, a year when Australia had unusually high levels of land clearing.” p43

“Walking away from what I thought was my dream job … given the lack of institutional support and how high the stakes are, I felt I had no choice.” p44

Gergis’ web bio cites “From 2019–2024, she was a Senior Lecturer in Climate Science in the Fenner School of Environment and Society at the Australian National University.” In the context, this is a very disappointing revelation about the ANU.

The webinar for this topic will be held on 28th June 2024. Click here to register the webinar.

Vulnerability of National Construction Code (NCC) changes for 2025

The Australian Building Codes Board (ABCB) hosted a webinar and discussion on 24th May, 2024 on NCC 2025: Proposed changes and Section J discussion. This session provided an overview of proposed changes to the next edition of the NCC. The NCC sets the minimum required level for the safety, health, amenity, accessibility and sustainability of buildings. Between 1 May and 1 July, the ABCB is seeking public comments on the proposed changes. These include an increase in the stringency of Section J, which deals with energy efficiency. As part of the update, there are changes proposed to J1V1, J1V3 and Specification 34.

The session covered:

  1. Changes to the J1V3 energy modelling pathway.
  2. Updates to existing building envelope solar admittance requirements.
  3. New requirements related to mandatory onsite solar photovoltaic systems (PV) and electric vehicle (EV) charging.
  4. Updates to existing fan efficiency and HVAC chiller requirements, including requirements for variable speed control.
  5. New provisions which facilitate the future electrification of buildings with mixed fuel use.

However, the J1V3 proposal is based on CSIRO Projected Weather Files (actually climate files) for building energy modelling, all of which have multiple time off set errors making the simulation results poentially misleading and counterproductive. ABCB used this data mandatory for the NCC 2025 changes however, we addressed this issue two times (CSIRO timing offset error in several weather elements (2022-04-21) and Solar data timing error skews simulation results (2022-01-17)), We have re-checked future climate scenario RCP 8.5 on 2050, but the timing offset errors remain despite the CSIRO knowing about their errors for over two years.

The data files available from CSIRO have two versions: Australian Climate Data Bank (ACDB) format for NatHERS and Energy Plus Weather (EPW) format for non-residential simulations. The solar and cloud cover datai in the ACDB format is timestamped at the centre of the time period (each hourly data point representing 30-minutes either side of the timestamp), while EPW format is timestamped at the end of the period. However, CSIRO’s EPW format data is transposed from the ACDB format without considering this time convention difference. Also, the ACDB format’s timestamp for the other weather elements is from 00:00 – 23:00 while EPW format is from 1:00 – 24:00. Transposing the data line-for-line created a 1-hour offset errors in those other weather elements.

We set an example to describe why simply tranposing the solar data is wrong. Let’s say the Global Horizontal Irradiation (GHI) value is 33Wh/m2 at 6:00 (i.e. from 5:30 to 6:30), 103Wh/m2 at 7:00, and 97Wh/m2 at 8:00 in ACDB format. Then in CSIRO’s EPW format, GHI is recorded as 33Wh/m2 at 7:00 (i.e. from 6:00 til 7:00), 103Wh/m2 at 8:00, and 97Wh/m2 at 9:00. When we plot the GHI data according to each format’s half-hour timeline and we can find both format’s GHI values on time are not identical. From ACDB format, between 5:30 and 6:30 GHI value is 33Wh/m2, while between 6:00 and 7:00’s GHI value is 33Wh/m2 in EPW format. This 30 minute time discrepancy happened to not only GHI, it happens to all other solar and cloud cover variables. The other weather variables are out by a whole hour.

To make more reliable data for simulation, this time offset error needs to be improved. We will comment on this issue to ABCB so that they can correct this long standing and long known mistake.

Our overriding concern is that all of the simulations done to validate the proposed changes to the NCC are unreliable because these multiple erroneous files were used for that very sensitive purpose.

i And in the case of Exemplary files, the precipitation data as well.