Pattern recognition in building energy performance over time using energy benchmarking data

Sokratis Papadopoulos, Bartosz Bonczak, Constantine Kontokosta

Research output: Contribution to journalArticle

Abstract

In recent years, many cities have adopted energy disclosure policies to better understand how energy is consumed in the urban built environment and how energy use and carbon emissions can be reduced. The diffusion of such policies has generated large-scale streams of building energy data, creating new opportunities to develop the fundamental science of urban energy dynamics. Nevertheless, there is limited research that rigorously analyzes building energy performance patterns over time. This paper provides a comprehensive framework to analyze building energy time series data and identify buildings with similar temporal energy performance patterns. We use data from approximately 15,000 properties in New York City, covering a six-year reporting period from 2011 to 2016. After pre-processing and merging the data for each constituent year, we use an unsupervised learning algorithm to optimally cluster the energy time series and statistical tests and supervised learning methods to infer how building characteristics vary between clusters. Our results show that energy reductions in New York City are mainly driven by its commercial building stock, with larger, newer, and higher-value buildings demonstrating the largest improvements in energy intensity over the study period. Moreover, voluntary energy conservation schemes are found to be more effective in boosting energy performance of commercial properties, compared to residential buildings. Our results suggest two distinct temporal patterns of energy performance for commercial and residential buildings, characterized by energy use reductions and increases. This finding highlights the differential response to energy reporting and disclosure, and presents a more complex picture of energy use dynamics over time when compared to previous studies. In order to realize significant energy use improvements over time and reach energy and carbon reduction goals, cities need to design and implement comprehensive energy policy frameworks, bringing together information transparency and reporting with targeted mandates and incentives.

Original languageEnglish (US)
Pages (from-to)576-586
Number of pages11
JournalApplied Energy
Volume221
DOIs
StatePublished - Jul 1 2018

Fingerprint

benchmarking
pattern recognition
Benchmarking
Pattern recognition
Energy policy
energy
Time series
energy use
Unsupervised learning
Carbon
Statistical tests
Supervised learning
Merging
Transparency
Learning algorithms
Energy conservation
energy policy
Processing
time series
energy conservation

Keywords

  • Building energy performance
  • Energy benchmarking
  • Energy disclosure
  • Machine learning
  • New York City
  • Time series clustering

ASJC Scopus subject areas

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

Cite this

Pattern recognition in building energy performance over time using energy benchmarking data. / Papadopoulos, Sokratis; Bonczak, Bartosz; Kontokosta, Constantine.

In: Applied Energy, Vol. 221, 01.07.2018, p. 576-586.

Research output: Contribution to journalArticle

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