Accurate cooling and heating load estimation is the foundation of energy-efficient HVAC system design. Over the years, ASHRAE has developed and refined various methods for heat load calculations. Each method offers a unique balance between accuracy, complexity, and computational effort.
In this blog, we’ll explore three of the most significant and widely used methods:
CLTD - Cooling Load Temperature Difference Method
RTS - Radiant Time Series Method
ASHRAE Heat Balance Method (the gold standard and the one we use in our Buildings AI tool)
1) CLTD Method (Cooling Load Temperature Difference)
The CLTD method is a simplified, tabular approach developed by ASHRAE to estimate cooling loads from heat gain through building envelopes, solar radiation, internal loads, and infiltration. It was designed primarily for hand or spreadsheet-based calculations.
How does it work?
It uses pre-calculated CLTD values (derived from hourly heat transfer simulations) for different materials, orientations, and solar exposures. The adjustments are made for:
Design temperature difference
Color and insulation of the surface
Time of day (solar angle)
Latitude and date
Additional correction factors include the latitude adjustment factor (LF) and solar load factor (SLF).
Advantages:
Easy to use with minimal data
Good for quick estimates and educational purposes
Widely understood in the HVAC industry
Limitations
Based on static tables — lacks real-time flexibility
Approximates time lag and solar effects through adjusted tables but lacks the dynamic responsiveness of modern methods like RTS and Heat Balance.
Not suitable for detailed hourly load or energy simulations
Best Use Case: Small to medium buildings, early design stages, or for learning HVAC fundamentals.
2) RTS Method (Radiant Time Series)
The Radiant Time Series method is an hour-by-hour dynamic method that improves upon CLTD by introducing time delay and heat storage effects. It accounts for the fact that heat from solar radiation and internal gains doesn’t immediately impact room temperature.
ASHRAE introduced RTS as a replacement for the CLTD/SCL/CLF methods, which offer much better accuracy.
How does it work?
Sensible gains from solar radiation and internal loads are broken into radiant and convective components.
The radiant portion is distributed over 24 hours using predefined RTS weighting factors, simulating the thermal mass effect.
The convective portion is added directly to the current hour.
The total cooling load at any hour is:
Advantages:
Hourly load profiles for better equipment selection
Accounts for thermal mass, solar lag, and intermittent gains
Can be complex to do manually — better suited for software
Best Use Case: Detailed system design, medium to large buildings, educational simulations, peak load estimation.
3) ASHRAE Heat Balance Method (HBM)
The ASHRAE Heat Balance Method is the most comprehensive, physics-based method available today. It forms the foundation of Energy Plus, DOE-2, and other modern simulation engines, and it’s also at the core of our Buildings AI load & energy simulation engine.
Core Principle
Every space in a building is modeled as an energy balance system, where the sum of all energy entering, leaving, and being stored must equal zero at every timestep.
It involves solving multiple simultaneous equations for:
Surface heat balance (walls, roofs, floors)
Air heat balance (indoor air node)
Radiant exchange between surfaces and occupants
Latent heat gains due to infiltration and internal sources
How does it work?
Surface Heat Balance: Accounts for conduction, solar gain, longwave radiation, convection, and thermal storage.
Zone Air Heat Balance: Includes heat from all surfaces, people, equipment, lighting, and ventilation.
Radiant/Convective Split: Each internal gain is divided between radiant and convective parts.
Solves Using Numerical Methods: At every timestep (e.g., hourly or sub-hourly), it solves for temperature, heat flow, and load.
Advantages:
Highly accurate, suitable for both load calculation and long-term energy simulation
Models’ thermal storage, solar tracking, shading, air exchange, and equipment schedules
Capable of compliance modeling (e.g., LEED, Title 24, ECBC)
Limitations:
Data-intensive: requires detailed building geometry, materials, occupancy, schedules, etc
Computationally heavy (but manageable with modern computing)
Summary:
Each method has its place in HVAC design. But as building design moves toward high performance and energy compliance, the Heat Balance Method becomes essential.
ASHRAE Heat Balance Method in Buildings AI
Buildings AI leverages the power of HBM while eliminating its complexity through a user-friendly interface. Here's how
Users just input their building geometry, schedules, and materials
Our engine handles the heavy math in the background
You get hourly load profiles, peak sizing, and energy simulation outputs
Integrates climate data, internal gains, ventilation, and envelope properties
Learn more about Buildings AI — Schedule a demo call today!
Rohit is a Senior Software development Manager of the simulationHub CFD cloud platform. He is a graduate of Computer Science from Pune University. An agile leader who has helped the team at simulationHub build several simulation apps. He has deep expertise in building scalable, resilient, beautiful web apps using Autodesk Forge, AWS, SWS, and a range of full-stack technologies. A scrum advocate to build cross-functional and self-organizing teams to create high-value products.
Rohit Chavan
Rohit is a Business Unit Head of the simulationHub CFD cloud platform. He is a graduate of Computer Science from Pune University. An agile leader who has helped the team at simulationHub build several simulation apps. He has deep expertise in building scalable, resilient, beautiful web apps using Autodesk Forge, AWS, SWS, and a range of full-stack technologies. A scrum advocate to build cross-functional and self-organizing teams to create high-value products.