Whole building energy simulation (BES) models play a significant role in the design and optimisation of buildings. Simulation models may be used to compare the cost-effectiveness of energy-conservation measures (ECMs) in the design stage as well as assessing various performance optimisation measures during the operational stage. However, due to the complexity of the built environment and prevalence of large numbers of independent interacting variables, it is difficult to achieve an accurate representation of real-world building operation. Therefore, by reconciling model outputs with measured data, we can achieve more accurate and reliable results. This reconciliation of model outputs with measured data is known as calibration. This paper presents a detailed review of current approaches to model development and calibration, highlighting the importance of uncertainty in the calibration process. This is accompanied by a detailed assessment of the various analytical and mathematical/statistical tools employed by practitioners to date, as well as a discussion on both the problems and the merits of the presented approaches.

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This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

Ceiling fans may cool room occupants very efficiently, but the air speeds experienced in the occupied zone are inherently non-uniform. Designers should be aware of several generic flow patterns when positioning ceiling fans in a room. Key to these are the fan jet itself and lateral spreading near the floor. Adding workstation furniture redirects the jet's airflow laterally in a deeper spreading zone, making room air flows more complex but potentially increasing the cooling experienced by the occupants. This paper presents the first evaluation of the effects of tables and workstation partitions on a room's generic air flow and comfort profiles. In a test room with a ceiling fan, we moved five anemometers mounted in a "tree" at heights of 0.1, 0.6, 0.75, 1.1, and 1.7 m to sample a dense measurement grid of 7 rows and 6 columns. We tested five different table and partition configurations and compared them to the empty room base case. From the results we propose a simplified model of room airflow under ceiling fans, useful for positioning fans and workstation furniture. We also present comfort contours measured in two ways that have comfort standards implications. The measured data are publicly available on the internet. Keywords: Ceiling fan; air speed; furniture; comfort cooling; corrective power Highlights 1. We performed high resolution measurements of ceiling-fan-induced air flow in an empty room; 2. We compare this reference case to air flow profiles measured in the room with five different table and partition configurations. The data are included as publicly available supplementary material; 4. The initial ceiling fan flow in the room could be modeled as a free jet; 5. The subsequent room circulation, with and without tables and partitions, may be represented by an intuitive model for designers who are placing fans and furniture; 6. The extent of comfort cooling provided by the fan air flow can be represented by the metric 'corrective power'. Corrective power equates the cooling effect of the fan as an ambient temperature reduction, ºC. We present the corrective power distribution in the room in two ways--with and without the air speed at ankle level--to evaluate air speed cooling effect. This evaluation is significant for thermal comfort standards.

Personal Comfort Systems (PCS) provide individual occupants local heating and cooling to meet their comfort needs without affecting others in the same space. It saves energy by relaxing ambient temperature requirements for the HVAC system. Aside from these benefits, PCS offers a wealth of data that can describe how individuals interact with heating/cooling devices in their own environment. Recently developed Internet-connected PCS chairs have unlocked this opportunity by generating continuous streams of heating and cooling usage data, along with occupancy status and environmental measurements via embedded sensors. The data allow individuals' comfort and behavior to be learned, and can inform centralized systems to provide 'just the right' amount of conditioning to meet occupant needs. In summer 2016, we carried out a study with PCS chairs involving 37 occupants in an office building in California. During the study period, we collected >5 million chair usage data-points and 4500 occupant survey responses, as well as continuous measurements of environmental and HVAC system conditions. The data analysis shows that (1) local temperatures experienced by individual occupants vary quite widely across different parts of the building, even within the same thermal zone; (2) occupants often have different thermal preferences even under the same thermal conditions; (3) PCS control behavior can dynamically describe individuals' thermal preferences; (4) PCS chairs produce much higher comfort satisfaction (96%) than typically achieved in buildings. We conclude that PCS not only provide personalized comfort solutions but also offer individualized feedback that can improve comfort analytics and control decisions in buildings.

We assessed the difference between mean radiant temperature (! ) and air temperature (! ) in conditioned office buildings to provide guidance on whether practitioners should separately measure ! or operative temperature to control heating and cooling systems. We used measurements from 48 office buildings in the ASHRAE Global Thermal Comfort Database, five field studies in radiant and all-air buildings, and five test conditions from a laboratory experiment that compared radiant and all-air cooling. The ASHRAE Global Thermal Comfort Database is the largest of these three datasets and most representative of typical thermal conditions in an office; in this dataset the median absolute difference between ! and ! was 0.4 (with 5 th , 25 th , 75 th , and 95 th percentiles = 0.2, 0.2, 0.6, and 1.6 °C). More specifically, the median difference shows that was 0.4 warmer than (with 5 th , 25 th , 75 th , and 95 th percentiles = -0.4 °C, 0.2 °C, 0.6 °C, and 1.6 °C). The laboratory experiments revealed that in a radiant cooled space ! was significantly (p<0.05) cooler than ! (average difference -0.1 ! ), while in the all-air cooled space ! was significantly (p<0.05) warmer than ! (average difference +0.3 ! ). These observations indicate that ! and ! are typically closer in radiant cooled spaces than in all-air cooled spaces. Although the differences are significant, the effect sizes are negligible to small based on Cohen's d and Spearman's rho. Therefore, we conclude that measurement of ! is sufficient to estimate ! under typical office conditions, and that separate measurement of ! or operative temperature is not likely to have practical benefits to thermal comfort in most cases -this is especially true for buildings with radiant systems. Furthermore, spatial and temporal variations in ! can be greater than or equal to the difference between ! and ! at any one location in a thermal zone, thus we expect that such variations typically have a greater impact on occupant thermal comfort than the differences between ! and ! .