Real-time clothing insulation level classification based on model transfer learning and computer vision for PMV-based heating system optimization through piecewise linearization

Abstract

Achieving a balance between energy efficiency and thermal comfort is a pivotal aspect of sustainable building design. Traditional control methods typically maintain indoor air temperature within predetermined limits, disregarding variable factors like occupancy activity and clothing levels, which significantly influence thermal comfort perception. Conversely, comfort-based control strategies present an opportunity to automate heating and cooling systems, dynamically responding to variations in thermal comfort. To achieve this, real-time information on clothing insulation (and its adjustment) is indispensable for accurately estimating thermal comfort. In this study, we explore the potential of a novel detection approach capable of classifying clothing insulation in real-time and utilizing this information to optimize the operation of building energy systems. By doing so, the proposed method facilitates the delivery of indoor conditions tailored to user requirements and potentially reduces energy wastage. The development of a 2 stage computer vision-based framework for occupancy detection and clothing insulation classification forms the core of this approach. Leveraging deep learning network algorithms, this framework performs detection and recognition tasks, even with limited training data, enabling real-time classification of light, medium and heavy clothing. To address the nonlinearity of traditional predicted mean vote (PMV) models, we applied a piecewise linearization approach to our PMV-based optimal control strategy. Through initial experimental field tests conducted in a case study university building, we evaluate the detection method's performance. The results demonstrate the proposed method's ability to classify clothing insulation levels and generate real-time profiles. We further analyze the impact of our proposed approach on thermal comfort and energy performance through scenario-based modelling and simulations. The initial results showed the potential of integrating our method with PMV-based controls to enhance thermal comfort and overcome the limitations of predefined or fixed schedules. However, while our study highlights the feasibility of classifying clothing insulation levels for multiple occupants engaged in diverse indoor activities, we acknowledge the need for further refinement to enhance detection accuracy and seamless integration with building energy systems.

Topics & Concepts