Rapid evaluation of buildings thermal performance using infrared thermography and artificial intelligence

Domestic energy consumption significantly contributes to the UK’s overall energy usage. Space and water heating are responsible for most households’ energy consumption. Any price hike, such as the current energy price situation, would seriously affect the budget of many households living in poorly insulated buildings. Improving insulation by deep retrofitting of existing buildings is expected to be a reasonable solution for reducing the domestic heating energy demands for those households. However, the level of insulation is a key issue, as retrofitting with excess insulation will incur higher cost and result in longer payback periods, especially in countries with moderate temperatures such as the UK. Therefore, it is necessary to estimate the thermal performance of existing building stock at the planning stage of retrofitting. Such evaluation of thermal performance requires, in most cases, prolonged monitoring of buildings using sensors installed for data analysis leading to significant time and cost issues. To address this knowledge gap and provide rapid evaluation of expected energy savings of retrofitting, this paper presents a novel technology with a case study to estimate energy savings between insulated and uninsulated residential buildings using Infrared Thermography and Artificial Intelligence. The results prove that the suggested AI technology, combined with infrared thermography, can provide rapid evaluation of heat losses through the building envelop and estimate the potential energy savings due to the enhancement of wall insulation by retrofitting.