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Predicting cooling loads in hot and humid climates using machinelearning approaches Bingyan JiaA Thesis in the Department of Building, Civil and Environmental EngineeringPresented in Partial Fulfilment
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01
Collect data on the building, including its dimensions, insulation, and windows.
02
Determine the specific cooling load factors for the building, such as occupants, equipment, lighting, and solar heat gain.
03
Calculate the conductive heat transfer through the walls, roof, and floor of the building.
04
Estimate the convective heat transfer from the building's occupants, equipment, and lights.
05
Consider the solar heat gain through windows and other transparent surfaces.
06
Determine the latent heat gain from moisture sources inside the building.
07
Combine the conductive, convective, solar, and latent heat gains to calculate the total cooling load.
08
Use a computer program or manual calculation method to perform the calculations.
09
Review and verify the results to ensure accuracy.

Who needs predicting cooling loads in?

01
Architects and engineers involved in building design.
02
HVAC (Heating, Ventilation, and Air Conditioning) professionals.
03
Energy consultants and assessors.
04
Building owners and property managers.
05
Researchers studying building performance and energy efficiency.
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Predicting cooling loads refers to estimating the amount of cooling energy required to maintain desired indoor temperatures in a building or space, considering factors such as heat gain from occupants, equipment, lighting, and outdoor climate conditions.
Generally, building designers, engineers, or contractors involved in the design or construction of commercial and residential buildings are required to file predicting cooling loads in, especially for compliance with building codes or energy regulations.
To fill out predicting cooling loads, one must collect relevant data on the building's dimensions, insulation levels, occupancy patterns, window types, and outdoor conditions, and then apply appropriate calculation methods or software to estimate cooling loads before reporting them in the required format.
The purpose of predicting cooling loads is to ensure that HVAC systems are appropriately sized to maintain indoor comfort, improve energy efficiency, and comply with regulatory requirements.
The report typically must include details about the building's design, specific assumptions made during calculations, peak cooling load estimates, and any applicable energy efficiency measures being implemented.
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