Abstract
This research was motivated by the need to identify the most effective Data Envelopment Analysis (DEA) model and associated data analytics software for measuring, comparing, and optimizing building energy efficiency. By analyzing literature sources, the authors identified several gaps in the existing DEA approaches that were resolved in this research. In particular, the authors introduced energy efficiency indices like energy consumption per square foot and per occupant as a part of DEA models’ outputs. They also utilized inverse and min-max normalized output variables to resolve the issue of undesirable outputs in the DEA models. The evaluation of these models was done by utilizing various data analytics software including Python, R, Matlab, and Excel. The authors identified that the CCR DEA model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model.
Highlights
The consumption of energy in the building sector has been an important part of the overall energy usage in the United States and on the global scale
The authors identified that the CCR data envelopment analysis (DEA) model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model
The analysis of literature sources demonstrated that both CCR and BCC models are being applied in measuring energy efficiency in buildings
Summary
The consumption of energy in the building sector has been an important part of the overall energy usage in the United States and on the global scale. A very commonly used metric in the U.S and globally for building energy performance is the energy use intensity (EUI), which is applied for Energy Star Certification (Energy Star, 2018) This certification shows that comparing the energy use of buildings with others nationwide helps to effectively identify the opportunities for potential savings and best practices that can be replicated (Energy Star, 2018). Another common index for measuring energy efficiency is the energy efficiency index (EEI), known as the building energy index.
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