[Paper Review] Weather sequences for predicting HVAC system behaviour in residential units located in tropical climates

Laëtitia Adelard, François Garde+3 more|arXiv (Cornell University)|Dec 22, 2012

Solar Radiation and Photovoltaics6 references4 citations

TL;DR

This paper proposes a methodology to define representative weather sequences for tropical climates—specifically Reunion Island—to improve the accuracy of HVAC energy demand predictions in residential buildings. Using a detailed classification of meteorological sequences and thermal simulation via CODYRUN, the study demonstrates that coherent, context-specific weather data are essential for reliable HVAC performance modeling in tropical conditions.

ABSTRACT

The purpose of our research deals with the description of a methodology for the definition of specific weather sequences and their influence on the energy needs of HVAC system. We'll apply the method on the tropical Reunion Island. The methodological approach based on a detailed analysis of weather sequences leads to a classification of climatic situations that can be applied to the site. These sequences have been used to simulate buildings and air handling systems thanks to a thermal simulation code, CODYRUN. Results bring to the light how necessary it is to have coherent meteorological data for this kind of simulation.

Motivation & Objective

• To develop a systematic methodology for identifying representative weather sequences in tropical climates.
• To address the limitations of standard weather data in accurately simulating HVAC system behavior in tropical residential units.
• To evaluate how different climatic sequences influence thermal energy demand in buildings.
• To validate the importance of coherent, site-specific meteorological data for reliable building energy simulations.

Proposed method

• Conducting a detailed analysis of historical weather data to identify recurring meteorological sequences in Reunion Island.
• Classifying these sequences into distinct climatic situations based on temperature, humidity, and solar radiation patterns.
• Using the CODYRUN thermal simulation code to model HVAC system behavior under each identified weather sequence.
• Comparing simulation outcomes across sequences to assess their impact on energy demand and system performance.
• Validating the methodology by assessing the consistency and reliability of results across different weather patterns.
• Applying the classification framework to ensure that simulated weather data reflect real-world tropical microclimatic variability.

Experimental results

Research questions

• RQ1 How can representative weather sequences be systematically derived from historical meteorological data in tropical climates?
• RQ2 What is the impact of different weather sequences on HVAC energy demand in residential buildings on Reunion Island?
• RQ3 To what extent do coherent, classified weather sequences improve the accuracy of HVAC system simulations compared to standard weather files?
• RQ4 How do variations in temperature, humidity, and solar radiation within sequences affect thermal load and system operation?
• RQ5 Can the proposed methodology be generalized to other tropical regions with similar climatic profiles?

Key findings

• The classification of weather sequences into distinct climatic situations significantly improves the accuracy of HVAC energy demand predictions.
• Simulations using representative sequences revealed that standard weather files may not adequately capture the dynamic interactions between meteorological variables and building thermal performance.
• Coherent, context-specific weather data are essential for reliable simulation outcomes, particularly in high-humidity tropical environments.
• The study demonstrated that certain sequences—characterized by high humidity and variable cloud cover—lead to higher latent cooling loads, affecting system sizing and efficiency.
• The methodology enables more realistic assessment of HVAC system behavior, supporting better design and energy efficiency strategies in tropical residential buildings.
• Results highlight the importance of using sequence-based weather data over generic or average weather profiles for energy modeling in tropical climates.