Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Abstract Many new tools for improving the design and operation of buildings try to realize the potential of big data. In particular, data is an important element for occupant-centric design and operation as occupants’ presence and actions are affected by a high degree of uncertainty and, hence, are hard to model in general. For such research, data handling is an important challenge, and following an open science paradigm based on open data can increase efficiency and transparency of scientific work. This article reviews current practices and infrastructure for open data-driven research on occupant-centric design and operation of buildings. In particular, it covers related work on open data in general and for the built environment in particular, presents survey results for existing scientific practices, reviews technical solutions for handling data and metadata, discusses ethics and privacy protection and analyses principles for the sharing of open data. In summary, this study establishes the status quo and presents an outlook on future work for methods and infrastructures to support the open data community within the built environment.

[1]  Iain Staffell,et al.  The importance of open data and software: Is energy research lagging behind? , 2017 .

[2]  Barbara Ubaldi,et al.  Open Government Data , 2019, Government at a Glance: Latin America and the Caribbean 2020.

[3]  Ravi S. Srinivasan,et al.  From occupancy to occupant behavior: An analytical survey of data acquisition technologies, modeling methodologies and simulation coupling mechanisms for building energy efficiency , 2017 .

[4]  Manpreet Kaur,et al.  Shopping intent recognition and location prediction from cyber-physical activities via wi-fi logs , 2018, BuildSys@SenSys.

[5]  Luc Rocher,et al.  Estimating the success of re-identifications in incomplete datasets using generative models , 2019, Nature Communications.

[6]  Fisayo Caleb Sangogboye.  Data-driven methods for occupant sensing and privacy protection: (with applications to enable smart and energy efficient buildings). , 2018 .

[7]  Costas J. Spanos,et al.  PAD: protecting anonymity in publishing building related datasets , 2017, BuildSys@SenSys.

[8]  Clayton Miller,et al.  The Building Data Genome Project: An open, public data set from non-residential building electrical meters , 2017 .

[9]  Ardeshir Mahdavi,et al.  IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .

[10]  Tianzhen Hong,et al.  A library of building occupant behaviour models represented in a standardised schema , 2019 .

[11]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part II: Implementation of the DNAS framework using an XML schema , 2015 .

[12]  Peter Murray-Rust,et al.  Open Data in Science , 2008 .

[13]  Ardeshir Mahdavi,et al.  An ontology for building monitoring , 2017 .

[14]  Slinger Jansen,et al.  Defining software ecosystems: a survey of software platforms and business network governance , 2012 .

[15]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[16]  K. P. Lam,et al.  A comparative study of the IFC and gbXML informational infrastructures for data exchange in computational design support environments , 2007 .

[17]  Zhengwei Li,et al.  A BIM-enabled information infrastructure for building energy Fault Detection and Diagnostics , 2014 .

[18]  Virginia Gewin,et al.  Data sharing: An open mind on open data , 2016, Nature.

[19]  Cynthia Dwork,et al.  Differential Privacy , 2006, ICALP.

[20]  Rishee K. Jain,et al.  Spatial and Temporal Modeling of Urban Building Energy Consumption Using Machine Learning and Open Data , 2019 .

[21]  Kamin Whitehouse,et al.  Plaster: an integration, benchmark, and development framework for metadata normalization methods , 2018, BuildSys@SenSys.

[22]  Charles M. Eastman,et al.  An ontology-based analysis of the industry foundation class schema for building information model exchanges , 2015, Adv. Eng. Informatics.

[23]  Weiqing Wang,et al.  User-based collaborative filtering on cross domain by tag transfer learning , 2012, CDKD '12.

[24]  Jens Lehmann,et al.  DBpedia: A Nucleus for a Web of Open Data , 2007, ISWC/ASWC.

[25]  Rajesh Gupta,et al.  Brick : Metadata schema for portable smart building applications , 2018, Applied Energy.

[26]  Craig A. Knoblock,et al.  Semantic Labeling: A Domain-Independent Approach , 2016, SEMWEB.

[27]  P. Strobl,et al.  Benefits of the free and open Landsat data policy , 2019, Remote Sensing of Environment.

[28]  Salvatore Carlucci,et al.  The effect of spatial and temporal randomness of stochastically generated occupancy schedules on the energy performance of a multiresidential building , 2016 .

[29]  K. Cranmer,et al.  Open is not enough , 2018, Nature Physics.

[30]  Mikkel Baun Kjærgaard,et al.  The impact of occupancy resolution on the accuracy of building energy performance simulation , 2018, BuildSys@SenSys.

[31]  Omid Ardakanian,et al.  ODToolkit: A Toolkit for Building Occupancy Detection , 2019, e-Energy.

[32]  Ashwin Machanavajjhala,et al.  Pythia: Data Dependent Differentially Private Algorithm Selection , 2017, SIGMOD Conference.

[33]  Ulf Melin,et al.  Investigating Open Government Data Barriers - A Literature Review and Conceptualization , 2018, EGOV.

[34]  Mikkel Baun Kjærgaard,et al.  ObepME: An online building energy performance monitoring and evaluation tool to reduce energy performance gaps , 2018 .

[35]  William O'Brien,et al.  Sensing and Data Acquisition , 2018 .

[36]  Yuan Jin,et al.  Modeling occupancy and behavior for better building design and operation—A critical review , 2018, Building Simulation.

[37]  Kevin Leyton-Brown,et al.  Auto-WEKA: combined selection and hyperparameter optimization of classification algorithms , 2012, KDD.

[38]  J. Gray.  Towards a Genealogy of Open Data , 2014 .

[39]  Mikkel Baun Kjærgaard,et al.  Evaluation of the opportunities and limitations of using IFC models as source of building metadata , 2018, BuildSys@SenSys.

[40]  Tianzhen Hong,et al.  An ontology to represent energy-related occupant behavior in buildings. Part I: Introduction to the DNAs framework , 2015 .

[41]  Burcin Becerik-Gerber,et al.  A systematic approach to occupancy modeling in ambient sensor-rich buildings , 2014, Simul..

[42]  Fisayo Caleb Sangogboye,et al.  Evaluating Practical Privacy Attacks for Building Data Anonymized by Standard Methods , 2019 .

[43]  Costas J. Spanos,et al.  A Framework for Privacy-Preserving Data Publishing with Enhanced Utility for Cyber-Physical Systems , 2018, ACM Trans. Sens. Networks.

[44]  P. Gurian,et al.  Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices , 2015 .

[45]  Ece Kamar,et al.  Revolt: Collaborative Crowdsourcing for Labeling Machine Learning Datasets , 2017, CHI.

[46]  Daniel Rudmark,et al.  Harnessing Digital Ecosystems through Open Data - Diagnosing the Swedish Public transport Industry , 2019, ECIS.

[47]  Ashwin Machanavajjhala,et al.  l-Diversity: Privacy Beyond k-Anonymity , 2006, ICDE.

[48]  Yongli Ren,et al.  OccuSpace: Towards a Robust Occupancy Prediction System for Activity Based Workplace , 2019, 2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops).

[49]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[50]  Hongsan Sun,et al.  An occupant behavior modeling tool for co-simulation , 2016 .

[51]  Flora D. Salim,et al.  RUP: Large Room Utilisation Prediction with carbon dioxide sensor , 2018, Pervasive Mob. Comput..

[52]  T. M. Lillesand,et al.  Emerging legal and ethical issues in advanced remote sensing technology , 1998 .

[53]  Mohammad Malekzadeh,et al.  Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis , 2017, 2018 IEEE/ACM Third International Conference on Internet-of-Things Design and Implementation (IoTDI).

[54]  Alastair Dunning,et al.  Are the FAIR Data Principles fair? , 2017, Int. J. Digit. Curation.

[55]  David E. Culler,et al.  Non-intrusive occupancy monitoring for energy conservation in commercial buildings , 2018 .

[56]  Matthew B. Jones,et al.  Challenges and Opportunities of Open Data in Ecology , 2011, Science.

[57]  Therese Peffer,et al.  Mortar: an open testbed for portable building analytics , 2018, BuildSys@SenSys.

[58]  Yannis Charalabidis,et al.  Benefits, Adoption Barriers and Myths of Open Data and Open Government , 2012, Inf. Syst. Manag..

[59]  Bing Dong,et al.  Occupancy behavior based model predictive control for building indoor climate—A critical review , 2016 .