November 2nd 2022

The Impact of Weather File Selection

The Impact of Weather File Selection

This article is the first one of a series discussing “Net Zero” strategies by investigating modelling approaches to bridge the performance gap and ultimately improve the prediction of energy models. In this study we will investigate the impact of weather file selection on the energy performance of a building in France with built-up area of 13,000 m2 and an energy-use intensity (EUI) of 90 kWh/m2.

An issue that energy modellers, clients, design teams and facility managers often deal with is the building energy performance gap, which is the difference in energy consumption of the operational building against the modelled prediction. 

A common misconception regarding energy modelling is that design stage energy models are used as a means for closely predicting energy use. Instead design stage energy modelling is a comparative analysis process of energy / carbon saving design measures intended to support the building’s use, essentially a decision-making tool. The analysis does not necessarily predict closely what the real building will do, nor is it the purpose of this analysis. Whereas operational stage analysis can predict very closely what the real building will do, particularly when the design stage model is now being utilised.

With the drive to “Net Zero” in the built environment receiving great attention, it is important to bridge this gap to make a fuller prediction of energy performance. For this, modelling approaches need more careful consideration to build high quality energy models and the selection of appropriate weather file is one aspect since many types of weather files exist to perform different functions in dynamic simulations.

Typical weather files used by energy modellers are:

  1. IWEC (International Weather for Energy Calculation)
  2. TRY (Test Reference Year)
  3. TMY (Typical Meteorological Year)
  4. DRY (Design Reference Year)
  5. DSY (Design Summer Year)

Each of these weather files contain meteorological data as an initial source but then involve some statistical processing to serve modelling.

For example, a TRY weather file is obtained by selecting 12 separate months of data from a historical set. Each one of these months is the most average month whose weather patterns are closest to the long-term trend over the observation period. The ISO method of selecting the most representative months primarily uses air temperature, humidity, and solar radiation with wind speed as the secondary parameter. When a TRY file is used in building energy simulations, the simulation program typically estimates the amount of solar radiation based on the cloud cover and cloud type information available for the TRY location.

An upgrade to a TRY weather file is a TMY file where total horizontal and direct normal solar insolation are added.

On the other hand, the procedure to develop an IWEC file is different: it consists in selecting twelve typical months from the different years available; then these months are stitched together to create an annual weather file.

Considering that each type of these weather files comes from a different historical dataset by applying different statistical processes, the predicted energy consumption using these files is also expected to vary. The degree in variation can contribute to a potential energy performance gap. Another concern is using weather data that is statistically derived over a decade since it fails to reflect the nature of climate change witnessed in recent years. For example, in the current year of 2022, the peak temperatures for Paris reached 40°C on July 19 compared to 33.1°C that comes from a standard weather file such as “Paris Montsouris 2005”. This implies that the important weather variables such as the Dry Bulb Temperature, Relative Humidity, Global Solar irradiance, etc in these weather files will fail to represent the most recent weather conditions. This will likely contribute further to the building energy performance gap.

Let’s take the example where the weather file “Paris Montsouris 2005” is compared to the latest recording of Heating Degree Days (HDD) and Cooling Degree Days (CDD) from 2021-2022. 

HDD and CDD are special kind of temperature indices used for estimating the weather-related energy consumption for heating and cooling of buildings. HDD is the summation of the deviation between the outside temperature and a base temperature of 18.33°C (65°F) when the average outside temperature is below the base temperature. Similarly, CDD is the summation of the deviation between daily outside temperature and 18.33°C (65°F) when the average temperature is above 18.33°C (65°F). 

The HDDs and CDDs can potentially be used to update a standard weather file to reflect real weather conditions in simulation. This is done by either Scaling, Offsetting or Normalizing the weather data in reference to the latest HDD and CDD data. For the “Paris Montsouris 2005” weather file, if we plot the monthly difference in HDD and CDD between the original weather file and the updated one, then we can clearly identify divergences between the 2005 weather file and 2021-2022 updated dataset.

The annual trend of HDD is now reduced, indicating warmer outdoor conditions and suggesting less need for space heating in winter months.


Meanwhile, the annual trend of CDD is now increased, indicating warmer outdoor conditions and suggesting greater need for space cooling in summer.

Now taking this forward into the model and a dynamic simulation of the building we see the following changes:

  • a decrease in annual Heating Energy consumption by 9% - implies the heating coil capacity could be overestimated.
  • an increase in annual Cooling Energy Consumption by 5% - implies the cooling coil capacity could be underestimated.
  • a subsequent increase in unmet load hours and the dry-bulb temperatures in certain rooms – implies an increased risk of overheating.

The following graph of the weekly temperature profile in an office space shows the potential increase in ~1oC during the occupied period and even greater differential overnight.

Is the weather file selection that important?

Yes, it absolutely is. Climate change and its impact on energy performance is a widely debated issue. For these reasons, the need for including appropriate climate knowledge in urban planning and decision-making processes from early design stages has been recognized by both designers and researchers. Therefore, the weather datasets used in dynamic simulations need to realistically represent the present (and near future) climate conditions to evaluate how various systems within a building interact. The weather file closest to your location is not necessarily the best choice. At the same time, a certain type of weather file does not guarantee the best representation of real weather conditions with the prevailing extreme winter and summer seasons.

This article demonstrates with the increase in both summer and winter temperatures there is a serious risk of the building not meeting design setpoints or operational temperatures. This subsequently impacts the sizing of the heating and cooling plant.

At IES we can assess different operational scenarios for weather conditions and accurately size heating and cooling plant for your building. There is a pressing need to analyse the standard weather files before using them for a dynamic simulation to make sure the plant meets the design intent in real life. 

How can we help?

IES can help you explore these opportunities. We can work with you right from concept using the latest in analysis technology to communicate visually and statistically the impact of a potential design strategy.

Find out more about our services by visiting and contact us today by emailing to get started.