Does historical data still count? Exploring the applicability of smart building applications in the post-pandemic period

Abstract The emergence of COVID-19 pandemic is causing tremendous impact on our daily lives, including the way people interact with buildings. Leveraging the advances in machine learning and other supporting digital technologies, recent attempts have been sought to establish exciting smart building applications that facilitates better facility management and higher energy efficiency. However, relying on the historical data collected prior to the pandemic, the resulting smart building applications are not necessarily effective under the current ever-changing situation due to the drifts of data distribution. This paper investigates the bidirectional interaction between human and buildings that leads to dramatic change of building performance data distributions post-pandemic, and evaluates the applicability of typical facility management and energy management applications against these changes. According to the evaluation, this paper recommends three mitigation measures to rescue the applications and embedded machine learning algorithms from the data inconsistency issue in the post-pandemic era. Among these measures, incorporating occupancy and behavioural parameters as independent variables in machine learning algorithms is highlighted. Taking a Bayesian perspective, the value of data is exploited, historical or recent, pre- and post-pandemic, under a people-focused view.

[1]  Didem Gürdür Broo,et al.  Flourishing systems: re-envisioning infrastructure as a platform for human flourishing , 2020, Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction.

[2]  H. Burak Gunay,et al.  Data analytics to improve building performance: A critical review , 2019, Automation in Construction.

[3]  Nora El-Gohary,et al.  A review of data-driven building energy consumption prediction studies , 2018 .

[4]  S. Hallam,et al.  A metagenomic survey of forest soil microbial communities more than a decade after timber harvesting , 2017, Scientific Data.

[5]  Miriam A. M. Capretz,et al.  An ensemble learning framework for anomaly detection in building energy consumption , 2017 .

[6]  Qing Gao,et al.  Disaggregating power consumption of commercial buildings based on the finite mixture model , 2019, Applied Energy.

[7]  Mengjie Han,et al.  A study on influential factors of occupant window-opening behavior in an office building in China , 2018 .

[8]  Colin N. Jones,et al.  Data-driven methods for building control — A review and promising future directions , 2020 .

[9]  Xiaoping Li,et al.  Model-free control method based on reinforcement learning for building cooling water systems: Validation by measured data-based simulation , 2020 .

[10]  Tianzhen Hong,et al.  Ten questions concerning occupant behavior in buildings: The big picture , 2017 .

[11]  Zheng O'Neill,et al.  Development of a probabilistic graphical model for predicting building energy performance , 2016 .

[12]  Jose I. Bilbao,et al.  A review and analysis of regression and machine learning models on commercial building electricity load forecasting , 2017 .

[13]  M. El Mankibi,et al.  Occupant presence and behavior: A major issue for building energy performance simulation and assessment , 2020 .

[14]  Jacek Tejchman,et al.  Comparison of physical performances of the ventilation systems in low-energy residential houses , 2009 .

[15]  Amit Kramer,et al.  The potential impact of the Covid-19 pandemic on occupational status, work from home, and occupational mobility , 2020, Journal of Vocational Behavior.

[16]  Gianfranco Chicco,et al.  Overview and performance assessment of the clustering methods for electrical load pattern grouping , 2012 .

[17]  Chunsheng Yang,et al.  A practical solution for HVAC prognostics: Failure mode and effects analysis in building maintenance , 2018 .

[18]  Yueren Wang,et al.  Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications , 2019, Automation in Construction.

[19]  Murad Khan,et al.  A generic internet of things architecture for controlling electrical energy consumption in smart homes , 2018, Sustainable Cities and Society.

[20]  Fuxin Niu,et al.  Data-driven based estimation of HVAC energy consumption using an improved Fourier series decomposition in buildings , 2018 .

[21]  Karel Macek,et al.  Model−based predictive maintenance in building automation systems with user discomfort , 2017 .

[22]  Gautam S. Thakur,et al.  A Bayesian machine learning model for estimating building occupancy from open source data , 2016, Natural Hazards.

[23]  Stéphane Ploix,et al.  Estimating Occupancy from Measurements and Knowledge Using the Bayesian Network for Energy Management , 2019, J. Sensors.

[24]  Benjamin C. M. Fung,et al.  A decision tree method for building energy demand modeling , 2010 .

[25]  Balaji Rajagopalan,et al.  Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building , 2013 .

[26]  David Flynn,et al.  Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review , 2020, Renewable and Sustainable Energy Reviews.

[27]  F. Al-turjman,et al.  Data-driven dynamic clustering framework for mitigating the adverse economic impact of Covid-19 lockdown practices , 2020, Sustainable Cities and Society.

[28]  Konstantinos Papakostas,et al.  Occupational and energy behaviour patterns in Greek residences , 1997 .

[29]  Manfred Morari,et al.  Learning decision rules for energy efficient building control , 2014 .

[30]  Minxiang Ye,et al.  Non-intrusive load disaggregation solutions for very low-rate smart meter data , 2020, Applied Energy.

[31]  Bernard Ghanem,et al.  ActivityNet: A large-scale video benchmark for human activity understanding , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[32]  Jessica Granderson,et al.  Statistical change detection of building energy consumption: Applications to savings estimation , 2019, Energy and Buildings.

[33]  Anuj Kumar,et al.  Sensing, Controlling, and IoT Infrastructure in Smart Building: A Review , 2019, IEEE Sensors Journal.

[34]  Jin Wen,et al.  A model-based fault detection and diagnostic methodology based on PCA method and wavelet transform , 2014 .

[35]  Peng Xu,et al.  HVAC terminal hourly end-use disaggregation in commercial buildings with Fourier series model , 2015 .

[36]  R. Annie Uthra,et al.  Development and implementation of novel sensor fusion algorithm for occupancy detection and automation in energy efficient buildings , 2019 .

[37]  Arno Schlueter,et al.  A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings , 2018 .

[38]  Marcel Schweiker,et al.  A review of select human-building interfaces and their relationship to human behavior, energy use and occupant comfort , 2020, Building and Environment.

[39]  A. Nešović,et al.  Impact of people's behavior on the energy sustainability of the residential sector in emergency situations caused by COVID-19 , 2020, Energy and Buildings.

[40]  Francesca Stazi,et al.  A literature review on driving factors and contextual events influencing occupants' behaviours in buildings , 2017 .

[41]  Xiang Xie,et al.  Digital twin-enabled anomaly detection for built asset monitoring in operation and maintenance , 2020, Automation in Construction.

[42]  Miriam A. M. Capretz,et al.  Transfer learning with seasonal and trend adjustment for cross-building energy forecasting , 2018 .

[43]  Alessandro Abate,et al.  Efficient probabilistic model checking of smart building maintenance using fault maintenance trees , 2017, BuildSys@SenSys.

[44]  Sean Luke,et al.  Cooperative Multi-Agent Learning: The State of the Art , 2005, Autonomous Agents and Multi-Agent Systems.

[45]  Da Yan,et al.  Quantitative description and simulation of human behavior in residential buildings , 2012 .

[46]  Anita Ramsetty,et al.  Impact of the digital divide in the age of COVID-19 , 2020, J. Am. Medical Informatics Assoc..

[47]  Sadia Din,et al.  A deep learning-based social distance monitoring framework for COVID-19 , 2020, Sustainable Cities and Society.

[48]  Bjarne W. Olesen,et al.  Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models , 2012 .

[49]  Federico Milano,et al.  Demand response algorithms for smart-grid ready residential buildings using machine learning models , 2019, Applied Energy.

[50]  Guoqiang Hu,et al.  Optimal Sensor Configuration and Feature Selection for AHU Fault Detection and Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[51]  K. Armel,et al.  Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .

[52]  Elad Hoffer,et al.  Train longer, generalize better: closing the generalization gap in large batch training of neural networks , 2017, NIPS.

[53]  Lynne E. Parker,et al.  Energy and Buildings , 2012 .

[54]  Verena Marie Barthelmes,et al.  Exploration of the Bayesian Network framework for modelling window control behaviour , 2017 .

[55]  Fan Zhang,et al.  Time series forecasting for building energy consumption using weighted Support Vector Regression with differential evolution optimization technique , 2016 .

[56]  Chien-fei Chen,et al.  Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19 , 2020, Energy Research & Social Science.

[57]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[58]  Vorpat Inkarojrit,et al.  Balancing comfort: occupants' control of window blinds in private offices , 2005 .

[59]  A. Carlsson-kanyama,et al.  Efficient and inefficient aspects of residential energy behaviour: What are the policy instruments for change? , 2006 .

[60]  K. K. Andersen,et al.  Survey of occupant behaviour and control of indoor environment in Danish dwellings , 2007 .

[61]  Fiorella Lauro,et al.  Fault detection analysis using data mining techniques for a cluster of smart office buildings , 2015, Expert Syst. Appl..

[62]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[63]  Chanjuan Sun,et al.  The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission , 2020, Sustainable Cities and Society.

[64]  Junaid Qadir,et al.  Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey , 2019, IEEE Access.

[65]  Naglaa A. Megahed,et al.  Antivirus-built environment: Lessons learned from Covid-19 pandemic , 2020, Sustainable Cities and Society.

[66]  Shen Wei,et al.  Identifying informative energy data in Bayesian calibration of building energy models , 2016 .

[67]  Tarek Zayed,et al.  A review of sustainable facility management research , 2020 .

[68]  Moon Keun Kim,et al.  Impact of correlation of plug load data, occupancy rates and local weather conditions on electricity consumption in a building using four back-propagation neural network models , 2020 .

[69]  Steven L. Scott,et al.  Predicting the Present with Bayesian Structural Time Series , 2013, Int. J. Math. Model. Numer. Optimisation.

[70]  Jack Kelly,et al.  Does disaggregated electricity feedback reduce domestic electricity consumption? A systematic review of the literature , 2016, ArXiv.

[71]  Marcel Schweiker,et al.  Occupancy and occupants’ actions , 2018 .

[72]  Zheng O'Neill,et al.  A review of smart building sensing system for better indoor environment control , 2019, Energy and Buildings.

[73]  V. Stanković,et al.  An electrical load measurements dataset of United Kingdom households from a two-year longitudinal study , 2017, Scientific Data.

[74]  Zhe Wang,et al.  Reinforcement learning for building controls: The opportunities and challenges , 2020, Applied Energy.

[75]  Moncef Krarti,et al.  An Overview of Artificial Intelligence-Based Methods for Building Energy Systems , 2003 .

[76]  Xiaodong Cao,et al.  Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade , 2016 .

[77]  Elias B. Kosmatopoulos,et al.  A roadmap towards intelligent net zero- and positive-energy buildings , 2011 .

[78]  Steven L. Scott,et al.  Inferring causal impact using Bayesian structural time-series models , 2015, 1506.00356.

[79]  Xiaobo Liu,et al.  Ensemble Transfer Learning Algorithm , 2018, IEEE Access.

[80]  Zhenjun Ma,et al.  Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering , 2018, Applied Energy.

[81]  Ming Jin,et al.  Advanced Building Control via Deep Reinforcement Learning , 2019, Energy Procedia.

[82]  Miguel Molina-Solana,et al.  Data science for building energy management: A review , 2017 .

[83]  Weisheng Lu,et al.  ‘Cognitive facility management’: Definition, system architecture, and example scenario , 2019, Automation in Construction.

[84]  Fu Xiao,et al.  A framework for knowledge discovery in massive building automation data and its application in building diagnostics , 2015 .

[85]  Madeleine Gibescu,et al.  Unsupervised energy prediction in a Smart Grid context using reinforcement cross-building transfer learning , 2016 .

[86]  M. Togeby,et al.  Demand for space heating in apartment blocks: measuring effects of policy measures aiming at reducing energy consumption , 2001 .

[87]  Claudio Del Pero,et al.  Adaptive-predictive control strategy for HVAC systems in smart buildings – A review , 2020, Sustainable Cities and Society.