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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.

Anomalous patterns in subjective votes can bias thermal comfort models built using data-driven approaches. A stochastic-based two-step framework to detect outliers in subjective thermal comfort data is proposed to address this problem. The anomaly detection technique involves defining similar conditions using a k-Nearest Neighbor (KNN) method and then quantifying the dissimilarity of the occupants’ votes from their peers under similar thermal conditions through a Multivariate Gaussian approach. This framework is used to detect outliers in the ASHRAE Global Thermal Comfort Database I & II. The resulting anomaly-free dataset produced more robust comfort models avoiding dubious predictions. The proposed method has been proven to effectively distinguish outliers from inter-individual variabilities in thermal demand. The proposed anomaly detection framework could easily be applied to other applications with different variables or subjective metrics. Such a tool holds great promise for use in the development of occupancy responsive controls for automated building HVAC systems.

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 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 technologies therefore offer a unique opportunity to improve our quantitative understanding of, and response to, indoor environmental quality issues.