Climate change cannot be addressed without improving the energy efficiency of the buildings in which we live and work. The papers in this collection describe and release a series of datasets that help us understand how occupants influence and experience building energy use, both to aid future research and policy-development, and to spark wider data sharing in this important area

Huebner, GM

Mahdavi, A

English

UCL Discovery

1Scientific Data |           (2019) 6:292  | https://doi.org/10.1038/s41597-019-0276-2www.nature.com/scientificdataa structured open data collection on occupant behaviour in buildingsGesche Margarethe Huebner  1 & ardeshir Mahdavi2Climate change cannot be addressed without improving the energy efficiency of the buildings in which we live and work. The papers in this collection describe and release a series of datasets that help us understand how occupants influence and experience building energy use, both to aid future research and policy-development, and to spark wider data sharing in this important area.Operation and construction of buildings were responsible for 36% of global final energy use and 39% of energy-related carbon dioxide (CO2) emissions in 20171. Decarbonisation of the building stock is hence crucial in mitigating climate change. Building occupants and operators influence buildings’ energy use through their interaction with the building envelope and control systems. Systematic collection and analysis of data from buildings can support, amongst others, energy and performance contracting, smart load balancing, model-predictive building systems control, and preventive building maintenance2. It is also necessary for under-standing, quantifying, modelling, and ultimately influencing the impact of people in buildings.Historically, those researching occupant influences on energy use in buildings have not frequently shared data publicly. This is at odds with recent trends in other disciplines where data sharing has increased in response to the replicability crisis, i.e. the fact that attempts at replicating established findings often failed3 and generic principles for data sharing been introduced4. There are many reasons for the paucity of initiatives to make data available. One barrier is likely to be commercial sensitivity - research is often conducted together with industrial partners who might oppose data sharing. Some data cannot be shared for data protection reasons. The drive to publish in competitive academic environments might also play a role: Field data collection is often a long and expensive process and researchers might fear premature data sharing may deprive them of the rewards of their effort includ-ing scientific prestige and publication opportunities. Furthermore, data sharing requires time and effort and can expose weaknesses in data collection processes5.There are many benefits of sharing data publicly beyond aiding replicability. For example, sharing data helps researchers to build on the work of others, to combine multiple data sets into larger ones allowing for an analysis with greater power, and to perform meta-analyses. For these benefits to be realized the data sets must be similar in nature and the data collection methods must be consistent. Occupant behaviour in buildings, however, is a very wide field. A set of qualitative interviews with social housing tenants can be classified as a study of occupant behaviour in buildings; as can a monitoring or modelling study of indoor air quality in offices. As such, data can be obtained via different methods, e.g. sensor-driven monitoring, interviews, focus groups, surveys, modelling, observation, and time-use diaries. Collected data may pertain to thermal comfort, occupant presence, window operation, light levels, air movement, energy use, and occupant productivity and other variables.Discussions initially within Annex 66 of the Technology Collaboration Programme on Energy Buildings and Communities by the International Energy Agency (https://annex66.org/) and then continued under Annex 79 (http://annex79.iea-ebc.org/) highlighted the challenges of comparing different studies due to the variety of methods and data ontologies used. The need for structured approaches to the collection, storage, sharing, and analyses of monitored data relevant to building occupants became a key focus of work in these Annexes resulting in books and articles on the subject. Motivated by the absence of publicly available and generally agreed-upon building monitoring data ontologies, Mahdavi et al.2,6 proposed a general ontology for the representation and incorporation of multiple layers of monitored building data in pertinent computational applications. The pro-posed ontology entails six basic data categories, namely i) inhabitants, ii) indoor environmental conditions; iii) external environmental conditions; iv) control systems and devices, v) equipment, and vi) energy flows. Each of these basic categories have multiple subcategories (see Fig. 1).1University College London, Energy Institute, 14 Upper Woburn Place, London, WC1H 0NN, UK. 2TU Wien (Technical University of Vienna), Department of Building Physics and Building Ecology, Karlsplatz 13, 1040, Vienna, Austria. email: g.huebner@ucl.ac.uk; amahdavi@tuwien.ac.atCoMMentoPen2Scientific Data |           (2019) 6:292  | https://doi.org/10.1038/s41597-019-0276-2www.nature.com/scientificdatawww.nature.com/scientificdata/In this ontology, the ‘Occupants’ category has four subcategories: (1) the position of occupants in the space (which hence indicates the presence of occupants), (2) control actions such as opening windows, (3) people’s attributes, e.g., their activity and clothing levels, and (4) people’s attitudes, e.g. their – often self-reported – ther-mal comfort assessment. Indoor conditions category captures spatial characteristics in hygro-thermal, visual, acoustical, and indoor air quality domains. External conditions can have a direct impact on internal conditions (e.g. through solar radiation) and an indirect one through occupants’ actions (e.g. closing windows to avoid traffic noise exposure). Control systems and devices category captures the state of systems for heating, cooling, ventilation, lighting, and shading as indicated, for instance, in terms of thermostat set-points and blind positions. The equipment category refers to any technical entities meant to support occupants’ activities (such as commu-nication devices, computers, electric appliances) but are not meant to control indoor environmental conditions. Energy category refers to magnitudes of energy provided to (or generated in) the buildings. Monitored data res-olution can be defined: (1) in spatial terms (e.g. rooms, floors, and whole buildings), (2) across multiple systems (e.g. heating, lighting, and equipment), and (3) in temporal terms (e.g. hourly, daily, monthly).All submissions to the special collection on ‘Occupant Behaviour in Buildings’ were requested to map the collected data onto the ontology proposed by Mahdavi et al., to encourage streamlining of reporting and to allow future users to see synergies between various data descriptors. The ontology proposed by Mahdavi et al. also sug-gests a specific structure on how variables from those six categories should be specified in terms of their values, associated sources, and possible actors. However, data descriptors in the present collection were not instructed to strictly conform to this.Langevin present data from a one-year study of 24 U.S. office focusing on indoor conditions, occupants’ atti-tudes, and control systems. In daily surveys, occupants reported on their thermal comfort, preference and behav-iour. Sensors recorded local thermal environmental and control states. In total, 2503 survey responses alongside tens of thousands of concurrent behaviour and environment measurements were generated7.Mahdavi et al. present data monitored over a period of one year in an office area in a university building in Vienna. The collected data includes occupants’ presence and operation of lights and windows. Moreover, to pro-vide a suitable interpretative context, monitored indoor environmental conditions (temperature, humidity) as well as plug loads are included in the data set, together with outdoor environmental parameters (i.e., temperature, humidity, global irradiance, wind speed, and wind direction)8.Paige et al. present a data set from a longitudinal affordable housing study collected in six affordable housing units in the USA where the performance of these units did not meet the design target (net-zero energy stand-ard). The authors provide energy data at a 1 Hz sampling rate for four circuits: main, hot water heater, dryer, and Fig. 1 Categories and sub-categories of building monitoring data. This figure displays categories and sub-categories of building monitoring data (based on6) as considered in the development of the building monitoring ontology2.3Scientific Data |           (2019) 6:292  | https://doi.org/10.1038/s41597-019-0276-2www.nature.com/scientificdatawww.nature.com/scientificdata/HVAC. They also report variables from the occupants category (i.e., attitudes, actions, and position), external conditions (i.e. weather), and control systems (e.g. heating type)9.Schwee et al. focused on indoor conditions and occupant presence, collecting room-level data on occupant counts, airflow, CO2, relative humidity, illuminance, and temperature data covering three rooms, one lecture room, and two study zones. The dataset consists of 47 full days10.Schweiker et al. present monitoring data collected over a period of four years from a naturally ventilated low-energy office building located in Frankfurt, Germany. The data set includes indoor and outdoor environmen-tal conditions, energy-related information, as well as occupants’ presence and behaviour11.Schweiker et al. questioned the underlying assumptions of the measurement tool routinely used to assess thermal comfort, i.e. within the category of occupants’ attitudes. The authors collected data in 30 countries on the assumption of equidistance of the ASHRAE thermal comfort scale by asking participants to state the perceived distance between the verbal anchors of the survey questions. In addition to testing the assumption of equidis-tance, this dataset allows to analyse the influence of different contexts (e.g. language, climate, or season) and characteristics of individuals (e.g., males/females)12.We believe the present contribution represents the first open collection of data on occupant behaviour in buildings structured following the same data ontology. Needless to say, we welcome and encourage future con-tributions to this body of work. Ultimately, we hope to establish a common process for data reporting that may become more widely used over time and help the research community to reap the benefits of data sharing, includ-ing facilitation of meta-analyses, generation of large samples, and aiding replicability.Received: 9 September 2019; Accepted: 14 October 2019;Published: xx xx xxxxReferences 1. International Energy Agency and the United Nations Environment Programme. 2018 Global Status Report: towards a zero‐emission, efficient and resilient buildings and construction sector. http://hdl.handle.net/20.500.11822/27140 (2018). 2. Mahdavi, A. & Taheri, M. An ontology for building monitoring. J. Build. Perform. Simul 10, 499–508 (2017). 3. Open Science Collaboration. Estimating the reproducibility of psychological science. Science (80-.) 349, aac4716–aac4716 (2015). 4. Wilkinson, M. D. et al. Comment: The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016). 5. Pfenninger, S., DeCarolis, J., Hirth, L., Quoilin, S. & Staffell, I. The importance of open data and software: Is energy research lagging behind? Energy Policy 101, 211–215 (2017). 6. Mahdavi, A., Taheri, M., Schuss, M., Tahmasebi, F. & Glawischnig, S. Structured building data management: Ontologies, queries, and platforms. In Exploring Occupant Behavior in Buildings: Methods and Challenges. 261–286 https://doi.org/10.1007/978-3-319-61464-9_10 (Springer International Publishing, 2017). 7. Langevin, J. Longitudinal dataset of human-building interactions in U.S. offices. Sci Data. https://doi.org/10.1038/s41597-019-0273-5 (2019). 8. Mahdavi, A., Berger, C., Tahmasebi, F. & Schuss, M. Monitored data on occupants’ presence and actions in an office building. Sci Data. https://doi.org/10.1038/s41597-019-0271-7 (2019). 9. Paige, F., Agee, P. & Jazizadeh, F. flEECe, an energy use and occupant behavior dataset for net-zero energy affordable senior residential buildings. Sci Data. https://doi.org/10.1038/s41597-019-0275-3 (2019). 10. Schwee, J. H. et al. Room-level occupant counts and environmental quality from heterogeneous sensing modalities in a smart building. Sci Data. https://doi.org/10.1038/s41597-019-0274-4 (2019). 11. Schweiker, M., Kleber, M. & Wagner, A. Long-term monitoring data from a naturally ventilated office building. Sci Data. https://doi.org/10.1038/s41597-019-0283-3 (2019). 12. Schweiker, M. et al. The Scales Project, a cross-national dataset on the interpretation of thermal perception scales. Sci Data. https://doi.org/10.1038/s41597-019-0272-6 (2019).acknowledgementsThe initiative behind this contribution and the development of its underlying ontology benefited from discussions in the framework of IEA EBC Annexes 66 and 79. The editors wish specifically to thank Ms Christiane Berger and Prof. David Shipworth for their contributions toward the Special Collection and this comment. Gesche Margarethe Huebner was supported by UK Research and Innovation through the Centre for Research into Energy Demand Solutions, grant reference number EP/R 035288/1.Competing interestsThe authors declare no competing interests.Additional informationCorrespondence and requests for materials should be addressed to G.M.H. or A.M.Reprints and permissions information is available at www.nature.com/reprints.Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.4Scientific Data |           (2019) 6:292  | https://doi.org/10.1038/s41597-019-0276-2www.nature.com/scientificdatawww.nature.com/scientificdata/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2019

A structured open data collection on occupant behaviour in buildings

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A structured open data collection on occupant behaviour in buildings

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