Using Machine Learning to Predict Retrofit Effects for a Commercial Building Portfolio

Yujie Xu, Vivian Loftness and Edson Severnini
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Yujie Xu: Center for Building Performance & Diagnostics, School of Architecture, Carnegie Mellon University, Pittsburgh, PA 15213, USA
Vivian Loftness: Center for Building Performance & Diagnostics, School of Architecture, Carnegie Mellon University, Pittsburgh, PA 15213, USA

Energies, 2021, vol. 14, issue 14, 1-24

Abstract: Buildings account for 40% of the energy consumption and 31% of the CO 2 emissions in the United States. Energy retrofits of existing buildings provide an effective means to reduce building consumption and carbon footprints. A key step in retrofit planning is to predict the effect of various potential retrofits on energy consumption. Decision-makers currently look to simulation-based tools for detailed assessments of a large range of retrofit options. However, simulations often require detailed building characteristic inputs, high expertise, and extensive computational power, presenting challenges for considering portfolios of buildings or evaluating large-scale policy proposals. Data-driven methods offer an alternative approach to retrofit analysis that could be more easily applied to portfolio-wide retrofit plans. However, current applications focus heavily on evaluating past retrofits, providing little decision support for future retrofits. This paper uses data from a portfolio of 550 federal buildings and demonstrates a data-driven approach to generalizing the heterogeneous treatment effect of past retrofits to predict future savings potential for assisting retrofit planning. The main findings include the following: (1) There is high variation in the predicted savings across retrofitted buildings, (2) GSALink, a dashboard tool and fault detection system, commissioning, and HVAC investments had the highest average savings among the six actions analyzed; and (3) by targeting high savers, there is a 110–300 billion Btu improvement potential for the portfolio in site energy savings (the equivalent of 12–32% of the portfolio-total site energy consumption).

Keywords: building energy retrofits; energy savings evaluation; data-driven energy analysis; causal forest; heterogeneous treatment effect (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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