PhD Defense by Jun Wang

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  • Date/Time:
    • Monday November 28, 2016
      12:00 pm - 2:00 pm
  • Location: Mason Conference Room 2228
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Summary Sentence: Benchmarking Building Energy in the Multifamily Industry: A Data Envelopment Analysis (DEA) Model

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Ph.D. Thesis Defense Announcement

Benchmarking Building Energy in the Multifamily Industry: A Data Envelopment Analysis (DEA) Model

 

By

Jun Wang

 

Advisor:

Dr. Baabak Ashuri (CEE)

 

Committee Members:

Dr. Iris Tien (CEE), Dr. Xinyi Song (BC)

Dr. Eric Marks (University of Alabama), Dr. Mohsen Shahandashti (University of Texas)

 

 

Date & Time: Monday, November 28th, 2016, 12:00PM

 

Location: Mason Conference Room 2228

Building energy benchmarking, offering initial building energy performance assessment, is a crucial tool for decision makers and facility managers to promoting the efficient use of energy among different properties. Traditional benchmarking models are mostly constructed in a simple benchmark table, comparing basic statistics of energy use of different properties. But they are very often subject to human judgement and are not capable of dealing with complex situations when multiple inputs and outputs are involved. Later on, linear regression model is utilized for building energy benchmarking, but it is still limited due to its various assumptions and the uncertainty of its prediction power. Recently, data envelopment analysis (DEA) has been utilized for benchmarking building energy, but existing DEA models have not been utilized to its optimum potential and are subject to limitations such as high sensitivity to outliers. This research intends to propose an integrated approach for building energy benchmarking analysis in the multifamily industry. DEA model will be chosen in this research as it has been understudied despite its possibilities. A systematic peer-wise multifamily building energy benchmarking model based on the DEA method is the expected outcome of this research. The proposed model is expected to be capable of selecting appropriate variables to be included in the model, remediating errors in the dataset, considering weather impact on building energy consumption, and detecting outliers that may distort the final efficiency score. This research intends to build on and contribute to the existing body of knowledge for building energy benchmarking, filling in the gaps of the knowledge in the existing DEA building energy benchmarking method. The scope of this research is multifamily properties from different geographical regions in the United States. The proposed research has the potential to improve energy consumption by ranking properties based on different efficiency scores. Research deliverables are expected to provide decision makers and facility managers the crucial information for building energy improvement.

 

 

 

 

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  • Created By: Tatianna Richardson
  • Workflow Status: Published
  • Created On: Nov 9, 2016 - 3:55pm
  • Last Updated: Nov 9, 2016 - 3:55pm