Improving the accuracy of energy baseline models for commercial buildings with occupancy data

Xin Liang, Tianzhen Hong, Qiping Shen

Research output: Journal article publicationJournal articleAcademic researchpeer-review

45 Citations (Scopus)


The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.
Original languageEnglish
Pages (from-to)247-260
Number of pages14
JournalApplied Energy
Publication statusPublished - 1 Oct 2016


  • Baseline model
  • Building energy use
  • Energy efficiency retrofit
  • Measurement and verification
  • Occupancy

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Energy(all)
  • Mechanical Engineering
  • Management, Monitoring, Policy and Law

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