The predicted mean vote (PMV) and predicted percentage of dissatisfied (PPD) are the most widely used thermal comfort indices. Yet, their performance remains a contested topic. The ASHRAE Global Thermal Comfort Database II, the largest of its kind, was used to evaluate the prediction accuracy of the PMV/PPD model. We focused on: (i) the accuracy of PMV in predicting both observed thermal sensation (OTS) or observed mean vote (OMV) and (ii) comparing the PMV-PPD relationship with binned OTS -observed percentage of unacceptability (OPU). The accuracy of PMV in predicting OTS was only 34%, meaning that the thermal sensation is incorrectly predicted two out of three times. PMV had a mean absolute error of one unit on the thermal sensation scale and its accuracy decreased towards the ends of the thermal sensation scale. The accuracy of PMV was similarly low for air-conditioned, naturally ventilated and mixed-mode buildings. In addition, the PPD was not able to predict the dissatisfaction rate. If the PMV model would perfectly predict thermal sensation, then PPD accuracy is higher close to neutrality but it would overestimate dissatisfaction by approximately 15-25% outside of it. Furthermore, PMV-PPD accuracy varied strongly between ventilation strategies, building types and climate groups. These findings demonstrate the low prediction accuracy of the PMV-PPD model, indicating the need to develop high prediction accuracy thermal comfort models. For demonstration, we developed a simple thermal prediction model just based on air temperature and its accuracy, for this database, was higher than PMV.

Recognizing the value of open-source research databases in advancing the art and science of HVAC, in 2014 the ASHRAE Global Thermal Comfort Database II project was launched under the leadership of University of California at Berkeley's Center for the Built Environment and The University of Sydney's Indoor Environmental Quality (IEQ) Laboratory. The exercise began with a systematic collection and harmonization of raw data from the last two decades of thermal comfort field studies around the world. The ASHRAE Global Thermal Comfort Database II (Comfort Database), now an online, open-source database, includes approximately 81,846 complete sets of objective indoor climatic observations with accompanying "right-here-right-now" subjective evaluations by the building occupants who were exposed to them. The database is intended to support diverse inquiries about thermal comfort in field settings. A simple web-based interface to the database enables filtering on multiple criteria, including building typology, occupancy type, subjects' demographic variables, subjective thermal comfort states, indoor thermal environmental criteria, calculated comfort indices, environmental control criteria and outdoor meteorological information. Furthermore, a web-based interactive thermal comfort visualization tool has been developed that allows end-users to quickly and interactively explore the data.

International standards that define thermal comfort in uniform environments are based on the steady-state heat balance equation that posits 'neutrality' as the optimal occupant comfort state for which environments are designed. But thermal perception is more than an outcome of a deterministic, steady-state heat balance. Thermal alliesthesia is a conceptual framework to understand the hedonics of a much larger spectrum of thermal environments than the more thoroughly researched concept of thermal neutrality. At its simplest, thermal alliesthesia states that the hedonic qualities of the thermal environment are determined as much by the general thermal state of the subject as by the environment itself. A peripheral thermal stimulus that offsets or counters a thermoregulatory load-error will be pleasantly perceived and vice versa, a stimulus that exacerbates thermoregulatory load-error will feel unpleasant. The present paper elaborates the thermophysiological hypothesis of alliesthesia with a particular focus on set-point control and the origins of thermoregulatory load-error signals, and then discusses them within the broader context of thermal pleasure. Alliesthesia provides an overarching framework within which diverse and previously disconnected findings of laboratory experiments, field studies and even comfort standards spanning the last 40 years of thermal comfort research can be more coherently understood.

Addressing two common challenges for building performance-reducing the carbon footprint attached to the provision of comfortable indoor environments, and improving the health and wellbeing of occupants-requires a more comprehensive understanding of how the indoor environments of buildings are operated. This paper introduces SAMBA, a state-of-the-art monitoring station for continuous, real-time measurements of indoor environmental quality (IEQ) parameters from occupants' work desks. It combines a hardware solution that integrates a low-cost suite of sensors with a software platform designed to automatically analyse and visualize data for quick interpretation of IEQ performance by non-scientist. In addition to feeding a massive IEQ database for research, the resulting data may be used to better inform the metrological requirements for popular international IEQ rating schemes. This new era of indoor environmental monitoring based upon systems such as SAMBA affords a fundamentally new approach to built environmental field research that holds significant promise to improve building performance and indoor environmental quality and occupant satisfaction, health, wellbeing and performance.

The release of the largest database of thermal comfort field studies presents an opportunity to perform a quality assurance exercise on the first generation adaptive comfort standards (ASHRAE 55 and EN15251). The analytical procedure used to develop the ASHRAE 55 adaptive standard was replicated on 60,321 comfort questionnaire records with accompanying measurement data. Results validated the standard's current adaptive comfort model for naturally ventilated buildings, while suggesting several potential nudges relating to the adaptive comfort standards, adaptive comfort theory, and building operational strategies. Adaptive comfort effects were observed in all regions represented in the new global database, but the neutral temperatures in the Asian subset trended 1-2 °C higher than in Western countries. Moreover, sufficient data allowed the development of an adaptive model for mixed-mode buildings that closely aligned to the naturally ventilated counterpart. We present evidence that adaptive comfort processes are relevant to the occupants of all buildings, including those that are air conditioned, as the thermal environmental exposures driving adaptation occur indoors where we spend most of our time. This affords significant opportunity to transition air conditioning practice into the adaptive framework by programming synoptic-and seasonal-scale set-point nudging into building automation systems.

Buildings influence diverse factors (e.g. health, wellbeing, productivity, and social connection). Occupants' direct experiences with their indoor environments allow them to determine whether those spaces support or hinder the activities performed. However, most post-occupancy evaluations (POEs) focus solely on measuring people's levels of comfort and environmental satisfaction. With increasing attention and interest in occupant health and wellness, there is a need to reassess whether occupant surveys are evaluating all they need to. An analysis is presented of data collected from a widely used online POE tool: The Center for the Built Environment's (CBE) Occupant Survey (more than 90,000 respondents from approximately 900 buildings) in order to summarise its database and evaluate the survey's structure and benchmarking metrics. A total of 68% of the respondents are satisfied with their workspace. Satisfaction is highest with spaces' ease of interaction (75% satisfied), amount of light (74%), and cleanliness (71%). Dissatisfaction is highest with sound privacy (54% dissatisfied), temperature (39%), and noise level (34%). Correlation, principal component, and hierarchical clustering analyses identified seven distinct categories of measurement within the 16 satisfaction items. Results also revealed that a reduction in the scale may be possible. Based on these results, potential improvements and new directions are discussed for the future of POE tools. PRACTICE RELEVANCE Assessing the measurement properties in a widely used occupant satisfaction survey reveals what is still useful to include and what may be missing from occupant surveys. These insights are increasingly important as built-environment research evolves and an increasing emphasis is placed on the physical and mental wellbeing of occupants and their productivity. Typical occupant satisfaction rates are reported for indoor environmental quality parameters and benchmark values. These can be used as references by practitioners and other survey tools. Based on this analysis, recommendations are made for different clustering and themes of measurement categories, along with the scope of additional questions that can be posed to occupants.

No abstract

The quality of buildings can be assessed in terms of the indoor air quality, thermal comfort, lighting quality, acoustic comfort afforded the occupants, collectively referred to as Indoor Environmental Quality (IEQ). A major barrier to a more thoroughly representative audit of actual IEQ performance are the expense and complexity of the measurement instrumentation required. Rapid developments in sensor technology in recent years present the opportunity for continuous and pervasive IEQ monitoring to deliver truly representative characterisations of building performance at a modest cost. The last remaining obstacle to realising these developments seems to be a concern about instrument accuracy. In this paper we test the performance of a low-cost IEQ monitoring system (SAMBA) introduced in an earlier paper. Calibration data from 100 devices was analysed to calculate the standard error of the estimate as a measure of equipment accuracy. Those performance specifications were used in a Monte Carlo simulation based on measurements of thermal comfort parameters from 24 office buildings. Performance measures suggests the low-cost system, whilst not as accurate as laboratory equipment, is more than sufficient for building IEQ diagnostics and compliance assessments. Furthermore, the results of the Monte Carlo simulation show that continuous monitoring systems are better at characterising long-term performance than ad hoc measurement strategies using precision equipment. Low-cost pervasive monitoring