Data driven occupancy information for energy simulation and energy use assessment in residential buildings

Abstract Occupant's schedules and their energy-use behavior are substantial inputs for building energy simulations and energy management in buildings. In practice, most of the research studies consider default occupant schedules from the standards. The temporal variations associated with occupancy is often missed out, leading to uncertainties in simulation results. This study aims to address two research problems in terms of occupancy: 1) upon the availability of the data, how to systematically extract the different occupant schedules, 2) when the occupancy data is not available, what are the other commonly logged parameters (such as plug load, lighting energy consumption, indoor carbon dioxide (CO2) concentration, and indoor relative humidity data) that shall be used to represent the occupancy in buildings. Regarding the first objective, a generic data-driven framework with the combination of shape-based clustering and change-point detection method is proposed to extract the distinct occupancy in residential buildings in terms of occupant activity schedule and presence probability. To demonstrate the outcomes of the framework, it was applied to the dataset collected from eight apartments located in Lyon, France. The results show the existence of different occupant patterns in buildings with respect to day of the week and season of the year. To achieve the second objective, linear and logistic regression models were developed to represent the occupant activity level and occupant presence/absence state, respectively. The linear regression model results show that among the examined variables, the lighting, and plug load consumption data along with the hour of the day show better prediction results in terms of adjusted R2 and mean absolute percentage error. For the occupant presence/absence state, the logistic regression model developed using CO2 concentration and plug load energy consumption dataset shows better results in misclassification error, confusion matrix, and receiver operating characteristic curve.

[1]  Tianzhen Hong,et al.  Advances in research and applications of energy-related occupant behavior in buildings ☆ , 2016 .

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

[3]  Donal P. Finn,et al.  A high-temporal resolution residential building occupancy model to generate high-temporal resolution heating load profiles of occupancy-integrated archetypes , 2020 .

[4]  Mohamed El Mankibi,et al.  Systematic data mining-based framework to discover potential energy waste patterns in residential buildings , 2019, Energy and Buildings.

[5]  Benjamin C. M. Fung,et al.  Development of a ranking procedure for energy performance evaluation of buildings based on occupant behavior , 2019, Energy and Buildings.

[6]  Chandra Sekhar,et al.  k-Shape clustering algorithm for building energy usage patterns analysis and forecasting model accuracy improvement , 2017 .

[7]  Edmundas Kazimieras Zavadskas,et al.  Importance of occupancy information when simulating energy demand of energy efficient house: A case study , 2015 .

[8]  Rouzbeh Razavi,et al.  Occupancy detection of residential buildings using smart meter data: A large-scale study , 2019, Energy and Buildings.

[9]  Scott Sanner,et al.  A longitudinal study of thermostat behaviors based on climate, seasonal, and energy price considerations using connected thermostat data , 2018, Building and Environment.

[10]  Hiroshi Yoshino,et al.  IEA EBC annex 53: Total energy use in buildings—Analysis and evaluation methods , 2017 .

[11]  Scott Sanner,et al.  Comparison of machine learning models for occupancy prediction in residential buildings using connected thermostat data , 2019, Building and Environment.

[12]  Zhun Yu,et al.  Standby energy use and saving potentials associated with occupant behavior of chinese rural homes , 2017 .

[13]  Benjamin C. M. Fung,et al.  A methodology for identifying and improving occupant behavior in residential buildings , 2011 .

[14]  Yaping Zhou,et al.  A novel model based on multi-grained cascade forests with wavelet denoising for indoor occupancy estimation , 2020 .

[15]  Nils Brandt,et al.  Smart homes, home energy management systems and real-time feedback: Lessons for influencing household energy consumption from a Swedish field study , 2018, Energy and Buildings.

[16]  L. Gosselin,et al.  Robustness of energy consumption and comfort in high-performance residential building with respect to occupant behavior , 2019 .

[17]  Hiroshi Yoshino,et al.  Definition of occupant behavior in residential buildings and its application to behavior analysis in case studies , 2015 .

[18]  Tianzhen Hong,et al.  Occupancy schedules learning process through a data mining framework , 2015 .

[19]  Nicholas A. Steinmetz,et al.  Typical occupancy profiles and behaviors in residential buildings in the United States , 2020 .

[20]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[21]  Francesco Asdrubali,et al.  Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies , 2014 .

[22]  Donal Finn,et al.  Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes , 2019, Energy and Buildings.

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

[24]  Ardeshir Mahdavi,et al.  Prediction of plug loads in office buildings: Simplified and probabilistic methods , 2016 .

[25]  Chandra Sekhar,et al.  Energy saving estimation for plug and lighting load using occupancy analysis , 2019, Renewable Energy.

[26]  Francesco Causone,et al.  A data-driven procedure to model occupancy and occupant-related electric load profiles in residential buildings for energy simulation , 2019, Energy and Buildings.

[27]  Franklin P. Mills,et al.  Rethinking the role of occupant behavior in building energy performance: A review , 2018, Energy and Buildings.

[28]  Benjamin C. M. Fung,et al.  Development of building energy saving advisory: A data mining approach , 2018, Energy and Buildings.

[29]  Geoffrey Qiping Shen,et al.  Occupancy data analytics and prediction: A case study , 2016 .

[30]  Benjamin C. M. Fung,et al.  A systematic procedure to study the influence of occupant behavior on building energy consumption , 2011 .

[31]  Natasa Nord,et al.  Influence of occupant behavior and operation on performance of a residential Zero Emission Building in Norway , 2018 .