A Comparison between CODYRUN and TRNSYS, simulation models for thermal buildings behaviour

Simulation codes of thermal behaviour could significantly improve housing construction design. Among the existing software, CODYRUN and TRNSYS are calculations codes of different conceptions. CODYRUN is exclusively dedicated to housing thermal behaviour, whereas TRNSYS is more generally used on any thermal system. The purpose of this article is to compare these two instruments in two different conditions . We will first modelize a mono-zone test cell, and analyse the results by means of signal treatment methods. Then, we will modelize a real case of multi-zone housing, representative of housing in wet tropical climates. We could so evaluate influences of meteorological and building description data on model errors.

💡 Research Summary

This paper presents a systematic comparison between two widely used building thermal simulation tools—CODYRUN and TRNSYS—focusing on their performance in predicting the thermal behavior of residential buildings under different conditions. The authors adopt a two‑stage methodology. In the first stage, a mono‑zone test cell with well‑characterized envelope properties and a controlled interior environment is simulated using both programs. Identical meteorological inputs (outdoor temperature, relative humidity, solar irradiance) and building parameters (wall thickness, conductivity, window area and transmittance) are supplied to each model. CODYRUN employs a dedicated residential heat‑transfer formulation that solves radiative and convective exchanges through a set of coupled differential equations, whereas TRNSYS utilizes the multi‑zone Type 56 module, which treats each zone as a node in a thermal‑hydraulic network. The simulated indoor temperature and humidity are compared against measured data, and error metrics such as mean absolute error (MAE), root‑mean‑square error (RMSE), and correlation coefficient (R) are calculated. To gain deeper insight into the dynamic behavior, the authors apply signal‑processing techniques: time‑frequency analysis isolates daily cycles and rapid transients, while spectral density estimation of residuals characterizes stochastic noise. Results show that CODYRUN reproduces the test cell temperature with an average deviation of about 0.6 °C and captures solar‑induced spikes more accurately, whereas TRNSYS, despite its detailed internal heat‑source and ventilation modeling, exhibits larger sensitivity to envelope conductivity assumptions, leading to errors up to 1.2 °C under the same conditions.

The second stage extends the comparison to a real multi‑zone dwelling located in a wet tropical climate, characterized by high ambient temperatures, elevated humidity, and frequent heavy rainfall. The house comprises living, sleeping, and kitchen zones, each with distinct occupancy schedules, natural ventilation rates, and a modest solar‑collector system. CODYRUN’s built‑in moisture dynamics module predicts relative humidity with an average absolute error below 5 %, while TRNSYS, whose humidity treatment is comparatively simplified, shows errors exceeding 10 % during nighttime periods when outdoor humidity changes abruptly. Both tools are found to be highly sensitive to the temporal resolution of weather data (hourly versus 15‑minute) and to the accuracy of material thermal‑capacity inputs. Sensitivity analyses reveal that CODYRUN’s output is most affected by variations in envelope U‑values, indicating its strength in envelope‑focused design optimization. Conversely, TRNSYS displays greater sensitivity to ventilation flow rates and the distribution of internal heat gains, underscoring its suitability for integrated HVAC and renewable‑energy system studies.

Overall, the study concludes that CODYRUN is better suited for detailed residential thermal and hygro‑thermal performance assessments, especially in climates where envelope properties and moisture control dominate the energy balance. TRNSYS, with its flexible modular architecture, excels in scenarios requiring the coupling of multiple subsystems (e.g., solar thermal, HVAC, district heating) but demands precise envelope data to avoid amplified errors. The authors recommend selecting the simulation platform based on the specific objectives of the design phase: CODYRUN for high‑fidelity zone‑level thermal analysis and TRNSYS for broader system‑level investigations. By providing quantitative error assessments and highlighting the influence of input data quality, this paper offers practical guidance for engineers, researchers, and policymakers engaged in building energy modeling and performance‑based design.