Utilizing Wearable Devices To Design Personal Thermal Comfort Model

Apart from the common environmental factors such as relative humidity, radiant and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age and health status. This is manifested as a difference in comfort levels between people residing under the same roof, and hence no general comprehensive comfort model satisfying everyone. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquire biometric information. This work proposes to access and exploit this data to build personal thermal comfort model. Relying on various supervised machine learning methods, a personal thermal comfort model will be produced and compared to a general model to show its superior performance

A Bayesian Approach for Learning and Predicting Personal Thermal Preference

Typical thermal control systems automated based on the use of widely acceptable thermal comfort metrics cannot achieve high levels of occupant satisfaction and productivity since individual occupants prefer different thermal conditions. The objective of this study is to develop environmental control systems that provide personalized indoor environments by learning their occupants and being self-tuned. Towards this goal, this paper presents a new methodology, based on Bayesian formalism, to learn and predict individual occupants thermal preference without developing different models for each occupant. We develop a generalized thermal preference model in which our key assumption, Different people prefer different thermal conditions is explicitly encoded. The concept of clustering people based on a hidden variable which represents each individuals thermal preference characteristic is introduced. Also, we exploited equations in the Predicted Mean Vote (PMV) model as physical knowledge in order to facilitate modeling combined effects of various factors on thermal preference. Parameters in the equations are re-estimated based on the field data. The results show evidence of the existence of multi-clusters in people with respect to thermal preference

Visual Thermal Landscaping (VTL) Model: A qualitative thermal comfort approach based on the context to balance energy and comfort

The Visual Thermal Landscaping (VTL) model provides a practical solution to balance energy and comfort tailored for the context and the immediate needs of individual occupants in that context through a thermal visualisation analysis. The aim is to provide a solution to the limitations of current tools employed in practice which do not account for the richness of thermal experience, which is never neutral. This disconnect between analysis tools and experience results in buildings using more energy than they should and leaves occupants dissatisfied with their environment. The capabilities of the approach were demonstrated through a field survey in an open plan office building, which was naturally ventilated and very energy efficient, as is reflected in its BREEAM excellence award. The model demonstrated the complexity of thermal comfort through contextual analysis. It illustrated individual differences in perceiving the thermal environment and the dynamic aspect of thermal comfort (i.e. occupants change their mind). Hence, a particular room temperature cannot satisfy everyone all the time. This holistic qualitative approach enables to provide comfort for every individual as well as a strategy to lower the overall energy consumption of the building. The immediate results of the model can be used by facilities management systems and the future development of the model can be used to predict areas and periods of thermal discomfort, provide additional support for the use of energy efficiency measures, and promote the use of thermal diversity in buildings

Save Money or Feel Cozy?: A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences

We present the design of a fully autonomous smart thermostat that supports end-users in managing their heating preferences in a realtime pricing regime. The thermostat uses a machine learning algorithm to learn how a user wants to trade off comfort versus cost. We evaluate the thermostat in a field experiment in the UK involving 30 users over a period of 30 days. We make two main contributions. First, we study whether our smart thermostat enables end-users to handle real-time prices, and in particular, whether machine learning can help them. We find that the users trust the system and that they can successfully express their preferences; overall, the smart thermostat enables the users to manage their heating given real-time prices. Moreover, our machine learning-based thermostats outperform a baseline without machine learning in terms of usability. Second, we present a quantitative analysis of the users’ economic behavior, including their reaction to price changes, their price sensitivity, and their comfort-cost trade-offs. We find a wide variety regarding the users’ willingness to make trade-offs. But in aggregate, the users’ settings enabled a large amount of demand response, reducing the average energy consumption during peak hours by 38%

The colours of comfort:From thermal sensation to person-centric thermal zones for adaptive building strategies

Thermal comfort research has been traditionally based on cross-sectional studies and spatial aggregation of individual surveys at building level. This research design is susceptible to compositional effects and may lead to error in identifying predictors to thermal comfort indices, in particular in relation to adaptive mechanisms. A relationship between comfort and different predictors can be true at an individual level but not evident at the building level. In addition, cross-sectional studies overlook temporal changes in individual thermal perception due to contextual factors. To address these limitations, this study applied a longitudinal research design over 8 to 21 months in eight buildings located in six countries around the world. The dataset comprises of 5,567 individual thermal comfort surveys from 258 participants. The analysis aggregated survey responses at participant level and clustered participants according to their thermal sensation votes (TSV). Four TSV clusters were introduced, representing four different thermal sensation traits. Further analysis reviewed the probability of cluster membership in relation to demographic characteristics and behavioural adaptation. Finally, the analysis at individual level enabled the introduction of a new metric, the thermal zone (Zt), which in this study ranges from 21.5°C to 26.6°C. The thermal sensation traits and person-centric thermal zone (Zt) are a first step into the development of new metrics incorporating individual perceived comfort into dynamic building controls for adaptive buildings

Data Driven Approach to Thermal Comfort Model Design

Apart from the dominant environmental factors such as relative humidity, radiant, and ambient temperatures, studies have confirmed that thermal comfort significantly depends on internal personal parameters such as metabolic rate, age, and health status. This study reviews the sensitivity of the Predicted Mean Vote (PMV) thermal comfort model relative to its environmental and personal parameters of a group of people in a space. PMV model equations adapted in ASHRAE Standard 55–Thermal Environmental Conditions for Human Occupancy, are used in this investigation to conduct a parametric study by generating and analyzing multi-dimensional comfort zone plots. It has been found that personal parameters such as metabolic rate and clothing have the highest impact. Current and newly emerging advancements in state of the art wearable technology have made it possible to continuously acquired biometric information. This work proposes to access and exploit this data to build a new innovative thermal comfort model. Relying on various supervised machine-learning methods, a thermal comfort model has been produced and compared to a general model to show its superior performance. Finally, the study represents an architecture to employ new thermal comfort model in inexpensive, responsive and extensible smart home service. Advisor: Fadi Alsalee