Learning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4 °C for the cooling mode with a median of 23.7 °C, and between 20.5 and 24.9 °C for the heating mode with a median of 22.7 °C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2–3 °C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking

Thermal comfort based fuzzy logic control

Most heating, ventilation and air conditioning (HVAC) control systems are considered as temperature control problems. In this work, the predicted mean vote (PMV) is used to control the indoor temperature of a space by setting it at a point where the PMV index becomes zero and the predicted percentage of persons dissatisfied (PPD) achieves a maximum threshold of 5%. This is achieved through the use of a fuzzy logic controller that takes into account a range of human comfort criteria in the formulation of the control action that should be applied to the heating system to bring the space to comfort conditions. The resulting controller is free of the set up and tuning problems that hinder conventional HVAC controllers. Simulation results show that the proposed control strategy makes it possible to maximize the indoor thermal comfort and, correspondingly, a reduction in energy use of 20% was obtained for a typical 7-day winter period when compared with conventional control

An Efficient Method for Learning Personalized Thermal Preference Profiles in Office Spaces

Incorporating occupant preferences in sensing and control operations of thermal systems has the potential to maximize thermal satisfaction and to supply energy when and where it is needed. Learning personalized thermal preferences is an essential part in this process. The latest studies have shown that it is possible to learn personalized thermal preference profiles using machine-learning and data-driven modeling algorithms. However, adequate data is required for each occupant, which is challenging in real buildings. This study presents: (i) a data-efficient method for learning personalized thermal preference profiles, based on Bayesian formalism, which shows good performance even with unobserved variables (i.e., air speed, metabolic rate, and clothing insulation level) and (ii) an efficient occupant feedback collection algorithm that minimizes disturbance of occupants. An experiment was conducted in identical perimeter offices, where human subjects were exposed to various thermal conditions and reported their respective thermal preference votes. Air temperature, globe temperature, and relative humidity were monitored continuously during the experiment. The collected data were used to infer occupants’ personalized thermal preference profiles, which showed better prediction performance and faster learning speed compared to existing methods. The method presented in this paper enables integration of occupant thermal preferences in smart environmental control systems of office buildings

Revisiting individual and group differences in thermal comfort based on ASHRAE database

Different thermal demands and preferences between individuals lead to a low occupant satisfaction rate, despite the high energy consumption by HVAC system. This study aims to quantify the difference in thermal demands, and to compare the influential factors which might lead to those differences. With the recently released ASHRAE Database, we quantitatively answered the following two research questions: which factors would lead to marked individual difference, and what the magnitude of this difference is. Linear regression has been applied to describe the macro-trend of how people feel thermally under different temperatures. Three types of factors which might lead to different thermal demands have been studied and compared in this study, i.e. individual factors, building characteristics and geographical factors. It was found that the local climate has the most marked impact on the neutral temperature, with an effect size of 3.5 °C; followed by country, HVAC operation mode and body built, which lead to a difference of more than 1 °C. In terms of the thermal sensitivity, building type and local climate are the most influential factors. Subjects in residential buildings or coming from Dry climate zone could accept 2.5 °C wider temperature range than those in office, education buildings or from Continental climate zone. The findings of this research could help thermal comfort researchers and designers to identify influential factors that might lead to individual difference, and could shed light on the feature selection for the development of personal comfort models

Artificial neural network analysis of teachers��� performance against thermal comfort

This is an accepted manuscript of an article published by Emerald in International Journal of Building Pathology and Adaptation on 17/04/2020, available online at: https://doi.org/10.1108/IJBPA-11-2019-0098 The accepted manuscript may differ from the final published version.Purpose: The impact of thermal comfort in educational buildings continues to be of major importance in both the design and construction phases. Given this, it is also equally important to understand and appreciate the impact of design decisions on post-occupancy performance, particularly on staff and students. This study aims to present the effect of IEQ on teachers��� performance. This study would provide thermal environment requirements to BIM-led school refurbishment projects. Design: This paper presents a detailed investigation into the direct impact of thermal parameters (temperature, relative humidity and ventilation rates) on teacher performance. In doing so, the research methodological approach combines explicit mixed-methods using questionnaire surveys and physical measurements of thermal parameters to identify correlation and inference. It was conducted through a single case study using a technical college based in Saudi Arabia. Findings: Findings from this work were used to develop a model using an Artificial Neural Network to establish causal relationships. Research findings indicate an optimal temperature range between 23��C and 25��C, with a 65% relative humidity and 0.4m/s ventilation rate. This ratio delivered optimum results for both comfort and performance

Vision based dynamic thermal comfort control using fuzzy logic and deep learning

A wide range of techniques exist to help control the thermal comfort of an occupant in indoor environments. A novel technique is presented here to adaptively estimate the occupant’s metabolic rate. This is performed by utilising occupant’s actions using computer vision system to identify the activity of an occupant. Recognized actions are then translated into metabolic rates. The widely used Predicted Mean Vote (PMV) thermal comfort index is computed using the adaptivey estimated metabolic rate value. The PMV is then used as an input to a fuzzy control system. The performance of the proposed system is evaluated using simulations of various activities. The integration of PMV thermal comfort index and action recognition system gives the opportunity to adaptively control occupant’s thermal comfort without the need to attach a sensor on an occupant all the time. The obtained results are compared with the results for the case of using one or two fixed metabolic rates. The included results appear to show improved performance, even in the presence of errors in the action recognition system