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Breaking New Ground in Thermal Comfort: Advancing Building Efficiency with Explainable AI

Researchers are pioneering a cutting-edge approach to indoor thermal comfort using infrared imaging and explainable artificial intelligence (AI). Analyzing facial temperature data, this innovative method accurately predicts occupant comfort levels.

Environmental and physiological parameters were meticulously collected in climatic chamber experiments using data loggers and infrared cameras. Researchers evaluated various machine learning models to identify the most effective approach for predicting thermal comfort.

May 25, 2024

IR Building Analysis with Extraction of Elements Using Image Segmentation and RetinaNet

Thermal imaging inspections are a highly effective and non-intrusive approach to monitor and assess the condition of buildings. Utilizing a thermal imaging camera enables early detection of issues, allowing for timely correction before they escalate into more severe and costly problems. This method involves scanning the building with infrared light using a specialized camera, revealing temperature variations on the surfaces of building elements due to radiated heat. When coupled with heat loss calculations, thermal imaging offers a comprehensive and cost-effective solution to address thermal discomfort issues in buildings.

January 15, 2024

Innovative UAV-Borne Solution Utilizes Deep Learning and IR Imaging to Detect Heat Loss Damage

Cutting-edge technology and artificial intelligence have joined forces to revolutionize the way we detect and address heat loss in buildings. The introduction of deep learning (DL) and convolutional neural networks (CNN) has ushered in a new era of efficiency and precision in this endeavor. In a groundbreaking paper published in “Building Engineering,” researchers have achieved a significant milestone by utilizing infrared (IR) images alone for the detection of heat loss.
The study, accessible through this link Building Engineering – UAV-Borne IR Imaging for Heat Loss Detection, presents an innovative approach that employs a UAV-borne IR imaging system to capture data, allowing for comprehensive aerial imaging of buildings.

October 25, 2023

fast and accurate prediction of thermal distribution and heat losses

Today, a lot of studies look at how well buildings use thermal energy by using analytical or numerical methods. Even though the finite element method can predict how well a building will be at saving energy, it takes a long time to figure out and solve the heat transfer problem. Also, a skilled engineer is needed, and setting up a complicated simulation model is also important.In a very recent paper published in “Applied Thermal Engineering”, a new deep-learning method was used to predict the thermal behavior of building structures in a short amount of time without having to go through a lot of complicated steps. The method was pre-trained with data from thermal simulations.If you put information like blueprint images and thermal properties into the developed deep-learning architecture, you can figure out thermal behavior and heat loss with high accuracy in a split-second. Full paper can be accessed here:

October 25, 2023