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Title: Occupancy schedules learning process through a data mining framework

Journal Article · · Energy and Buildings
 [1];  [2]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Polytechnic of Turin, Torino (Italy)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
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Building occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and reproduce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understanding of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 minute interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. Furthermore, the identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1253720
Alternate ID(s):
OSTI ID: 1360831
Report Number(s):
LBNL-180204; ir:180204
Journal Information:
Energy and Buildings, Vol. 88, Issue C; ISSN 0378-7788
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 205 works
Citation information provided by
Web of Science

References (20)

A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices journal December 2013
Revealing occupancy patterns in an office building through the use of occupancy sensor data journal December 2013
Statistical analysis and modeling of occupancy patterns in open-plan offices using measured lighting-switch data journal February 2013
Modeling occupancy in single person offices journal February 2005
Methods for the prediction of intermediate activities by office occupants journal June 2010
Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration journal September 2014
Integrating probabilistic methods for describing occupant presence with building energy simulation models journal January 2014
A generalised stochastic model for the simulation of occupant presence journal January 2008
A novel approach for building occupancy simulation journal June 2011
Stochastic models for building energy prediction based on occupant behavior assessment journal October 2012
A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting journal September 2013
Frequent pattern mining: current status and future directions journal January 2007
Behavioural Patterns and User Profiles related to energy consumption for heating journal October 2011
Patterns of occupant interaction with window blinds: A literature review journal August 2012
Patterns of residential energy behavior journal October 1983
A decision tree method for building energy demand modeling journal October 2010
A systematic procedure to study the influence of occupant behavior on building energy consumption journal June 2011
A novel methodology for knowledge discovery through mining associations between building operational data journal April 2012
Simplifying decision trees journal September 1987
A Cluster Separation Measure journal April 1979

Cited By (13)

Influence of household air-conditioning use modes on the energy performance of residential district cooling systems journal March 2016
Hybrid Optimization in Big Data: Error Detection and Data Repairing by Big Data Cleaning Using CSO-GSA book January 2018
Towards occupant-centric simulation-aided building design: a case study journal August 2019
Towards data-driven sustainable design: decision support based on knowledge discovery in disparate building data journal October 2018
Occupant behavior in identical residential buildings: A case study for occupancy profiles extraction and application to building performance simulation journal August 2019
Identificação de perfis de comportamento do usuário para edificações residenciais multifamiliares e naturalmente ventiladas em Florianópolis journal September 2018
Data mining based framework to identify rule based operation strategies for buildings with power metering system journal September 2018
Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations journal September 2018
In Search of Sustainable Design Patterns: Combining Data Mining and Semantic Data Modelling on Disparate Building Data book October 2018
Modeling occupancy and behavior for better building design and operation—A critical review journal June 2018
Long-term monitoring data from a naturally ventilated office building journal November 2019
Long-term monitoring data from a naturally ventilated office building text January 2019
Long-term monitoring data from a naturally ventilated office building text January 2019

Figures / Tables (23)

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Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
occupant behavior
data mining
occupancy schedule
behavioral pattern
office building
building simulation