Title: Occupancy data analytics and prediction: A case study

Abstract

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Furthermore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.

Authors:

Liang, Xin ^[1]; Hong, Tianzhen  ^[2];

• Search DOE PAGES for author "Hong, Tianzhen"
• Search DOE PAGES for ORCID "0000-0003-1886-9137"

Shen, Geoffrey Qiping ^[3]

---

+ Show Author Affiliations

1. Hong Kong Polytechnic Univ., Hong Kong (China); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
3. Hong Kong Polytechnic Univ., Hong Kong (China)

Publication Date:

Mon Mar 28 00:00:00 EDT 2016

Research Org.:

Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Org.:

USDOE Office of Science (SC); USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI Identifier:

1532190

Alternate Identifier(s):

OSTI ID: 1358854

Grant/Contract Number:  

AC02-05CH11231

Resource Type:

Accepted Manuscript

Journal Name:

Building and Environment

Additional Journal Information:

Journal Volume: 102; Journal Issue: C; Journal ID: ISSN 0360-1323

Publisher:

Elsevier

Country of Publication:

United States

Language:

English

Subject:

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; Occupancy prediction; Occupant presence; Data mining; Machine learning