Exemplary Energy is collaborating with Dr Tim Law, an expert architectural scientist specialising in mould in Australian buildings. Dr Law is Head of Building Sciences at the Melbourne-based consultancy, RIC Solutions, and was previously a part of the University of Tasmania’s Tasmanian Architectural Science Laboratory (TASL).

RIC Solutions has researched compliance paths for the condensation and mould provisions in the National Construction Code (NCC) including how they relate to the AIRAH guide DA07 ‘Criteria for Moisture Control Design Analysis’ proposing Moisture Design Reference Years (MDRYs) and Moisture Reference Years (MRYs) along with the alternative of 10 or more consecutive years of weather data which include hourly precipitation data.

For now, our collaborative work is focused on three radically different sites: Brisbane, Darwin and Melbourne. For each place, a 15-year weather file has been prepared along with the MRY derived from that data for testing and analysis. RIC Solutions has been provided with this data along with the identification of the 10th and 90th percentile years according to the 3 alternative definitions of temperature. They will use that data in hygrothermal simulations and report on the sensitivity of the results to the definition of temperature selected. 

On Monday 13 October, AIRAH’s STG on Building Physics spontaneously convened a virtual meeting for an update on weather and climate data available for hygrothermal simulations. Exemplary Energy’s Trevor Lee and Hong Gic Oh attended to advise the STG of the current status of our analyses. RIC Solutions’ Dr Tim Law was unavailable due to an unfortunate clash with the Building and Plumbing Commission industry conference in Melbourne.

On Monday, 3 November, Exemplary Energy conducted a trial to generate MRY files by adapting the Brambilla (2022) method – originally designed to select MRYs on a yearly basis to a monthly selection for Brisbane, Darwin, and Melbourne. The method is using wet index (based on wind-driven rain) and dry index (based on dry bulb temperature, relative humidity and station pressure) to calculate the moisture index.  Dr Law is currently performing a sensitivity analysis for Melbourne. 

According to AIRAH DA07 (Criteria for moisture control design analysis in buildings), Moisture design reference years: it suggests using MDRY from the 10th-percentile warmest and 10th-percentile coldest years from a 30-year weather analysis. However, there are no measured hourly precipitation data for 30 years in Australia. Accordingly, this would require application of Exemplary’s in-house software for synthesizing (disaggregating) hourly values from daily data in the early years (Oates et al, 2025).

Dr Law mentioned “…Just stuck to 10 consecutive years. That keeps us well within the automatic tipping buckets era without a need to disaggregate rainfall data”. Similarly, Jesse Clarke (Pro Clima) noted “…Using repeated yearly data means the results are periodic in nature, if they are not then there is clearly a simulation problem. This is a massive bonus for fault finding and determining if silly inputs have been used in a relatively immature market of WUFI users. The 10 consecutive years are technically more accurate.”

Work is progressing in this field, including a recent report by RIC Solutions (T Law et al) to the Australian Building Codes Board (ABCB) entitled ‘Condensation: Physical Testing in Tropical and Subtropical Climates’. Its publication is understood to be imminent, but for now, to appreciate its context, see the ABCB’s ‘Handbook of Condensation in Buildings’ here.

The intention is to expand this to the full NatHERS list of climate zone locations for routine practitioner applications.

The report by RICsolutions has been with the ABCB for many months. Exemplary and RICsolutions are collaborating with the Fraunhoffer Institute in Germany and AIRAH’s Building Physics Special Technical Group (STG) to prepare advanced advice for AIRAH members and subscribers to ‘Ecolibrium’. Exemplary Energy has recently proposed that the AIRAH formally request immediate access to that report to allow peer review. 

Updates on this work will be included in future editions of ‘Exemplary Advances’.

Retail electricity and gas prices have recently increased across Australia: electricity prices generally rose on 1 July but some gas tariffs won’t rise until 1 August.

We offer a free ready-reckoner tool on our website to help homeowners estimate the annual costs of gas and electricity for heating and cooling any dwelling – house, townhouse or apartment – in any of the eight Australian capital cities. The calculations have been updated with current electricity prices that took effect on 1 July 2025 specific to each location, and include the option of all-electric home conditioning. Additional energy costs for ducted systems are separately estimated on the basis of a single-storey house with ducts in its roof space.

Users can select a home size between 75 and 500 m² and compare the estimated energy costs of heating and cooling a home corresponding to a NatHERS Energy Efficiency Rating (EER) using appliances of varying energy ratings. The matrix covers the following five NCC Climate Zones (CZ), and users can apply results to other locations in the same CZ (for example, Canberra values for Armidale NSW and Ballarat VIC in CZ 7):

CZ 1 – Hot Humid Summer, Warm Winter (Darwin)

CZ 2 – Warm Humid Summer, Mild Winter (Brisbane)

CZ 5 – Warm Temperate (Adelaide, Perth, Sydney)

CZ 6 – Mild Temperate (Melbourne)

CZ 7 – Cool Temperate (Canberra, Hobart)

The increased electricity prices can be attributed, in large part, to the country’s energy transition away from fossil fuels like coal and gas towards renewable sources such as solar and wind. General inflation and higher interest costs are other factors known to impact the outcome for consumers.

While renewable energy is cheaper to produce over time, the upfront costs are substantial due to the complexity of developing and integrating these new technologies to the grid. These costs are being passed on to consumers through higher network and wholesale electricity charges. Renewables like solar and wind are intermittent by nature, requiring advanced grid management systems, backup generation, and large-scale battery storage to ensure reliable and consistent supply. Moreover, the closure of traditional baseload power stations has reduced the system’s stability, prompting the need for additional measures—such as synchronous condensers and grid-forming inverters—to maintain frequency control and voltage stability. Ultimately, while renewable energy is essential for long-term sustainability and emissions reduction, the transitional period brings significant financial pressures and these are reflected in the 2025 price increases.

As we do every month with our building archetypes, to produce the EWEI, clients can simulate their actual buildings every month using the existing digital models prepared for JV3 validation of their meeting or exceeding the energy efficiency standards of the National Construction Code (NCC). The metered energy consumption of those buildings can then be compared with the simulation results generated with very little cost to identify possible inefficient operation. 

Readers wanting to test out this method can avail themselves of the free sample RTY file to simulate their buildings operating in the immediate past month. An RTY comprises 12 consecutive months, so historic comparisons can also be applied to establish when the problem arose. 

Calibrated Simulation

A highly targeted version of this routine simulation comparison with metered consumption is the calibrated simulation. Calibrated simulation is a complex and powerful tool for accurately modelling the energy consumption of existing buildings to support cost effective remedial and refurbishment projects using recent real weather data. Exemplary Energy supplied that real weather data used in the simulation case study reported here by Hongsen Zhang, a leading building energy simulation expert and managing director of the company EnerEfficiency Pty Ltd. Hongsen is an Associate of Exemplary Energy, and is shown here receiving the Australian Institute of Refrigeration, Air Conditioning and Heating (AIRAH) W.R.Ahern Award jointly with Dr Paul Bannister in 2018 for their technical paper:A Calibrated Simulation Case Study for an Office Building in Canberra.

Read that award-winning paper here.

Exemplary Energy’s work on Precipitation Disaggregation, converting daily data as was recorded by the Bureau of Meteorology (BoM) in the 1990s and much of the 2000s, has been reported on in Exemplary Advances during its development over several years. It was presented in extended abstract format at the Asia Pacific Solar Research Conference (APSRC) in December 2024. Our full paper on that ground-breaking work has now been published in the learned journal Stochastic Environmental Research & Risk Assessment.

Our team has successfully developed an innovative machine learning approach that generates high-resolution precipitation data (mostly rainfall) critical for modern building performance and hygrothermal simulations such as in WUFI. Our work introduces a long short-term memory (LSTM) neural network that can disaggregate daily precipitation readings into half-hourly intervals—a significant advancement over existing machine learning models limited to hourly resolution. 

The table below summarizes model performance across the five climate zones, showing Root Mean Square Error (RMSE), wet half-hour detection, and skill score accuracy on test data set. 

Location	NCC Climate Zone	RMSE (mm)	Wet Half-Hour Detection (%)	Skill score (Half-Hourly)
Cairns	Zone 1	0.6899	65.88	0.68
Brisbane	Zone 2	0.5670	60.68	0.65
Sydney	Zone 3	0.5136	70.85	0.73
Melbourne	Zone 6	0.2345	55.86	0.62
Canberra	Zone 7	0.2857	68.14	0.70

Note:

• RMSE measures average prediction error (lower is better). 
• Wet Half-Hour detection shows the percentage of rainy half-hours correctly identified in the exact half-hour, reflecting timing accuracy. 
• Skill score evaluates how well the model captures the frequency and distribution of precipitation events (higher is better, 1.0 is perfect). 

In comparison to the previous model (Ferrari et al. (2022)), the RMSE improved by over 30% for Canberra (0.4513 vs 0.65 mm). 

In the Australian context, the extreme weather conditions like draughts and heavy rains make precise data essential. By incorporating meteorological variables and a specialised normalisation layer, our model maintains statistical consistency while preserving daily precipitation totals. 

This breakthrough enables more precise modeling for building designs and water management systems, especially valuable in regions like late 20^th century Australia where historical sub-hourly precipitation data is limited.

You can read the full paper here: https://doi.org/10.1007/s00477-025-02996-0

Weather and climate files incorporating precipitation data in ACDB and EPW formats are available through our sales portal at no extra charge.