Author: HongGic Oh

Home Energy Models Validated Against Real Bills

Dayne Tompson

energyFit’s home energy models are validating at exceptional absolute accuracy against metered consumption, and are now being tested across six additional climate zones. This article sets out the evidence, and issues an open call to academics who would like to be involved in the research.

The gap between modelled and metered energy use is a stubborn problem in residential building science. Design-stage predictions routinely diverge from what a household actually pays, and closing that divergence is what allows retrofit and upgrade advice to be relied upon. energyFit, developed in the ACT since 2016, was built to close it.

A Digital Twin of the Actual Home

energyFit constructs a calibrated digital twin of the specific dwelling, combining LiDAR capture with EnergyPlus simulation. EnergyPlus is embedded as the thermal core, with energyFit’s engine running over the top to model occupant behaviour, generation and storage, and the full appliance load, thermal and non-thermal alike. The output is a dollar-denominated upgrade roadmap: what each intervention costs, what it saves, and in what order to do it where household budgets cannot action all advantageous changes promptly.

Validated Accuracy

Against metered household consumption in the ACT, energyFit has validated at 93% absolute accuracy for general electricity use, 96.4% for gas, and 98.3% for hot water. These are absolute figures using standard climate files – higher accuracy is expected using Real-Time Year actual weather data directly coinciding in time with the metered consumption data and is currently being tested. Overestimates and underestimates are not netted against one another, so the results are not flattered by errors in opposite directions cancelling out. For work that has to hold up against a real electricity and gas bill, that is an exceptional result.

Six NCC Climate Zones

Accuracy in a single climate is necessary but not sufficient. The method is now being tested across all six NCC climate zones in NSW, to establish whether ACT-level accuracy holds across a wider range of thermal conditions, building stock, and household behaviour. That trial is the mechanism through which the evidence is being built.

Reducing the Cost of a Low-Bill Home

Delivering upgrades at scale has generally meant standardised packages: the same insulation, the same heat pump, the same solar specification across many homes. That approach is administratively efficient, but the marginal saving from any measure depends on the specific building. A retrofit that pays for itself in one dwelling can return very little in the one next door.

Because energyFit models each home individually, it can find the least-cost path to a target bill for that home, and identify which measures will do the work. The cost of delivering a low-bill home falls, not because ambition is lowered, but because capital goes where the model shows it will earn a higher return. For a scheme administrator, that means more low-bill homes per dollar of subsidy. The same applies even if the household is targeting a Net Zero Emissions outcome.

Call for Collaboration

The six-zone trial will generate an unusual quantity of matched modelled-and-metered data across diverse conditions. Researchers who would like to be involved are invited to make contact.

Collaboration would be especially valuable in thermal simulation validation, the economics of least-cost retrofit optimisation, behavioural questions around why households act on cost information, and the evaluation of information-barrier versus price-barrier segmentation in energy efficiency schemes.

  Expressions of interest can be directed to Dayne Thompson at dayne@sjtconsulting.com.au

Disclousre of Interest

Exemplary Investments has made a small hybrid-debt investment in this work; and Exemplary Energy will conduct a randomly selected blind re-simulation of a sample of homes to establish any enhancement of accuracy achieved by applying Real-Time Year weather data to the energyFit method.

Exemplary Weather and Energy (EWE) Index June 2026

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

All values are % higher/lower energy demand/output relative to climatically typical weather.

Especially during the mild seasons, large % changes can occur from small absolute differences. RTYs are available for purchase for your own simulations.

ADELAIDE

Energy Index (%)

The solar PV simulation output was 6.6% lower than the long-term average. The heating peak load was lower than the long-term average for the 3-storey office building and 10-storey office building by 11.2% and 6.3% while the supermarket is slightly higher by 0.5%.  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 the bespoke building model (digital twin) using our RTY data.

Adelaide experienced warmer-than-average conditions in June compared with the long-term mean, along with higher daytime relative humidity. GHI was slightly below the long-term average, while wind speeds were lower during the daytime but higher during the remaining hours of the day.

Weather Index



BRISBANE

Energy Index (%)

The solar PV simulation output was 6.9% lower than the long-term average. The heating peak load was much lower than the long-term average for the 3-storey office building, 10-storey office building and supermarket, by 63.1%, 74.0% and 44.1%, 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 the bespoke building model (digital twin) using our RTY data.

Brisbane experienced higher than average temperatures in June compared with the long-term mean, except during daytime hours and relative humidity was higher after 7:00 am. GHI was below the long-term average, while wind speeds remained broadly similar to the long-term average.

Weather Index



CANBERRA

Energy Index (%)

The solar PV simulation output was 13.2% lower than the long-term average. The heating peak load was lower than the long-term average for the 3-storey office, 10-storey office and supermarket, by 20.9%, 20.4% and 0.5%, 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 the bespoke building model (digital twin) using our RTY data.

Canberra experienced higher-than-average temperatures and relative humidity in June compared with the long-term average. GHI was below average, while wind speeds were broadly similar to the long-term average.

Weather Index



DARWIN

Energy Index (%)

The solar PV simulation output was 9.7% lower than the long-term average. The cooling peak load was higher than the long-term average for the 3-storey office, the 10-storey office and the supermarket, by 11.1%, 12.2% and 0.5%, 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 the bespoke building model (digital twin) using our RTY data.

Darwin experienced temperatures similar to the long-term average in June, along with higher relative humidity. GHI was below average, while wind speeds were broadly similar to the long-term mean for most of the day.

Weather Index



HOBART

Energy Index (%)

The solar PV simulation output was 4.1% lower than the long-term average. The heating peak load was lower than the long-term average for the 3-storey office building, the 10-storey office building and the supermarket by 19.3% and 19.1% and 4.3% 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 the bespoke building model (digital twin) using our RTY data.

Hobart experienced much warmer and more humid conditions in June compared with the long-term average. GHI was higher than average, while wind speeds were generally lower for most of the time.

Weather Index



MELBOURNE

Energy Index (%)

The solar PV simulation output was 22.4% lower than the long-term average. The heating peak load was significantly lower than the long-term average for the 3-storey office building, the 10-storey office building and supermarket by 35.0% and 35.8% and 33.1% 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 the bespoke building model (digital twin) using our RTY data.

Melbourne experienced warmer temperatures and significantly higher relative humidity in June compared with the long-term average, while GHI was substantially below average.

Weather Index



PERTH

Energy Index (%)

The solar PV simulation output was 2.4% lower than the long-term average. The heating peak load was higher than the long-term average for the 3-storey office building and 10-storey office building and the supermarket by 13.4%, 12.6% and 16.9% 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 the bespoke building model (digital twin) using our RTY data.

Perth experienced warmer daytime temperatures, but similar overnight temperatures compared with the long-term average in June, along with similar relative humidity. GHI was slightly higher, while wind speeds were lower during the daytime and similar during other times of the day.

Weather Index



SYDNEY

Energy Index (%)

The solar PV simulation output was 9.1% lower than the long-term average. The heating peak load was lower than the long-term average for the 3-storey office, the 10-storey office and the supermarket by 22.5%, 27.0% and 26.9%, 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 the bespoke building model (digital twin) using our RTY data.

Sydney experienced much higher temperatures and slightly higher relative humidity in June compared with the long-term average. Both GHI and wind speeds were similar to the long-term average.

Weather Index



How reliable is South Korea in Australia’s energy security mix?

Foreign Minister Cho Hyun and Australian Foreign Minister Penny Wong pose for a photo ahead of their talks at the Foreign Ministry in the Government Complex in Jongno-gu, Seoul, on the afternoon of the 30th April. Source: Yonhap News

This is the follow up article from ‘How reliable is South Korea in Australia’s energy security mix?’ last month’s edition.

Exemplary Energy team member, Hong Gic Oh, visited Korea, his homeland, last month and into early June and he brings now follow up news about the energy trade mix between South Korea and Australia.

Australia is Korea’s largest supplier of liquefied natural gas (LNG) and a major supplier of condensate and critical minerals, while Korea is a major supplier of refined petroleum products and diesel to Australia. South Korea heavily relies on imports for its natural gas needs, sourcing the vast majority of its Liquefied Natural Gas (LNG) from Australia, the United States, and Qatar. State-owned Korea Gas Corporation (KOGAS) dominates these purchases, but recent geopolitical disruptions and shifting domestic regulations have altered market dynamics.

Korea is one of the Indo‑Pacific’s key suppliers of refined fuels. Its four major refineries process more than 3 million barrels per day, making Korea a central source of petrol, diesel and jet fuel for Australia, particularly as domestic refining in Australia has been contracting. Korea’s refinery network and trade routes now form part of Australia’s practical fuel‑security architecture. At the same time, Australia has been actively adjusting its import portfolio to compensate for reduced Qatari supply, including increased reliance on alternative suppliers across Asia.

According to reporting by ABC News, Australia has actively moved to secure additional fuel and fertiliser supplies in response to disruptions linked to the Strait of Hormuz crisis. The federal government has utilised export finance guarantees to underwrite commercial fuel purchases and accelerate delivery of refined products, including diesel shipments sourced from multiple regions such as South Korea and Brunei. In parallel, additional fertiliser agreements have been secured through coordinated government–industry arrangements, aimed at stabilising agricultural input supply chains during the period of heightened geopolitical uncertainty. These measures reflect a proactive diversification strategy, with Australia engaging directly in global spot and contractual markets to offset volatility in traditional Gulf-linked supply routes.

Beyond trade, Korea is emerging as a partner in electrification and clean‑energy technologies. The government plans to deploy 3.5 million heat pumps over the next decade and supply 4.5 million electric and hydrogen vehicles by 2030, supported by one of the world’s densest charging networks. Korea also aims to deliver 100 gigawatts of renewable electrical peak power by 2030, reshaping regional supply chains in batteries, EVs and clean‑energy equipment. For Australia, engaging with Korea’s accelerating transition is essential to remaining competitive in a decarbonising Indo‑Pacific.

Australia and the Republic of Korea have reaffirmed their role as core partners in regional fuel security amid the ongoing Middle East conflict and the effective closure of the Strait of Hormuz. In their 30 April 2026 Joint Statement, both governments renewed their commitment to maintain a stable, secure and reliable supply of diesel and other liquid fuels—an essential step as Australia faces heightened vulnerability in global energy markets. South Korea, already Australia’s largest supplier of refined petroleum products and its top source of diesel, confirmed that it will continue to prioritise open, rules‑based trade and resist unjustified import or export restrictions.

The two countries agreed to strengthen supply chain resilience, deepen regional cooperation and accelerate the energy transition, recognising that diversified and transparent fuel flows are now a strategic necessity. A key feature of the agreement is a mutual notification and consultation mechanism for potential trade disruptions, giving Australia advance warning of any fuel shortages and enabling timely preparation across critical sectors.

Australian ministers emphasised that Korean diesel imports underpin Australia’s ability to remain a reliable exporter of energy, food and commodities, while reaffirming Australia’s long‑standing role as a stable supplier of energy and resources to Korea and the wider region. The joint commitment underscores how exposed global supply chains have become—and how central the Australia–Korea partnership is to maintaining economic stability and energy security across the Indo‑Pacific. – Read more

Max-Min data in Quality Assured Weather and Climate Files

Recently, we received enquiries regarding differences in maximum and minimum temperatures between our weather data and the monthly reports published by Bureau of Meteorology (BoM). In some cases, the differences reached up to 2–3°C for both maximum and minimum temperatures.

The main reasons for these differences are related to:

  1. Different definitions of a “day”
  2. Different temperature measurement and processing methods

BoM defines a climatological day differently from most building simulation software. Historically, weather observations in Australia were manually recorded at 9:00 am, particularly for max-min temperature and precipitation measurements, and this convention continues today.

According to BoM, the daily maximum temperature is determined over the 24-hour period from 9:00 am to 9:00 am the following day, while the daily minimum temperature is determined over the 24 hours leading up to 9:00 am on the day indicated.

However, simulation software such as EnergyPlus, WUFI, and IES VE uses calendar-day data (typically 1:00–24:00) when calculating daily maximum and minimum temperatures and they simply select the highest and lowest hourly instantaneous temperatures. As a result, the same “day” can contain different temperature extremes depending on the time window used.

The chart below demonstrates how minimum temperature values can vary depending on the definition of the daily time window.


For example, for 2 January 2026, the BoM hourly minimum temperature is derived from observations recorded near 10:00 am on 1 January (17.4°C), whereas the simulation-based daily minimum temperature comes from 11:00 pm on 2 January (17.8°C). The minimum temperature in the BoM monthly report for 2 January is also 17.3°C which probably happened near 10:00 am on 1 January. This is an uncommon example because the minimum temperature usually occurs around dawn (1 January had the lowest maximum temperature on the month).

Historically, the BoM had no record of when the max and min temperatures occurred but they do now because of the way they are recorded in an Automatic Weather Station (AWS). Because we only use the max-min data to QA (and occasional gap filling) of the 24 hourly values, we have not accessed this time data because it is not useful to us.

For maximum temperature (Chart below zoomed at 2 January 12:00), both BoM daily maximum temperature and the simulation-based daily maximum are 25.2°C. However, the maximum temperature in the BoM monthly report for 2 January is 26.0°C which probably happened near 12:00 pm on 2 January.

Another source of difference relates to how temperatures are measured and processed. According to the Bureau of Meteorology (BoM) documentation on air temperature measurements, maximum and minimum temperatures at AWS sites are derived from 1-second observations recorded by the Automatic Weather Station (AWS). Therefore, BoM daily maximum and minimum temperatures are based on high-frequency point measurements rather than hourly observation data.

In contrast, our EPW and ACDB weather files are based on hourly observation temperatures. Therefore, short-duration temperature peaks or dips captured by AWS point measurements may not appear in hourly observation datasets. This means:

  • Daily maximum temperatures in BoM reports can be slightly higher than hourly observation simulation data
  • Daily minimum temperatures in BoM reports can be slightly lower than hourly observation simulation data

This difference is especially noticeable during rapidly changing weather conditions, where temperature extremes may occur briefly between hourly timestamps.

Although our hourly maximum and minimum temperatures do not always align with the BoM monthly report due to differing time windows, we use the BoM daily extremes as reference values in our QA process before 2024. When estimating missing values, our algorithm refers to these BoM-derived extremes to guide the estimation and ensure that the estimated temperatures remain realistic and within reasonable bounds. However, from 2025 onward, datasets from BoM contain very few missing observations and remaining gaps are supplemented using our internal quality-control and estimation algorithms.