Improving the detection of thermal insulation in buildings –which
includes the development of models for heating and ventilation processes and
fabric gain - could significantly increase building energy efficiency and substantially contribute to reductions in energy consumption and in the carbon
footprints of domestic heating systems. Thermal insulation standards are now
contractual obligations in new buildings, although poor energy efficiency is often a defining characteristic of buildings built before the introduction of those
standards. Lighting, occupancy, set point temperature profiles, air conditioning
and ventilation services all increase the complexity of measuring insulation
efficiency. The identification of thermal insulation failure can help to reduce
energy consumption in heating systems. Conventional methods can be greatly
improved through the application of hybridized machine learning techniques to
detect thermal insulation failures when a building is in operation. A three-step
procedure is proposed in this paper that begins by considering the local building
and heating system regulations as well as the specific features of the climate
zone. Firstly, the dynamic thermal performance of different variables is specifically modelled, for each building type and climate zone. Secondly, Cooperative
Maximum-Likelihood Hebbian Learning is used to extract the relevant features.
Finally, neural projections and identification techniques are applied, in order to
detect fluctuations in room temperatures and, in consequence, thermal insulation failures. The reliability of the proposed method is validated in three winter
zone C cities in Spain. Although a great deal of further research remains to be
done in this field, the proposed system is expected to outperform conventional
methods described in Spanish building codes that are used to calculate energetic
profiles in domestic and residential buildings.
