A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption

Xinran Yu, Semiha Ergan, Gokmen Dedemen

Research output: Contribution to journalArticle

Abstract

The electricity consumption of Heating Ventilating and Air Conditioning (HVAC) systems has a significant share in the energy consumption of buildings, which account for 75% of total electricity produced in the US. Therefore, improving the energy efficiency in HVAC systems is an essential goal in facility management (FM) industry. Building Automation Systems (BASs) deployed in buildings provide an enormous amount of data on HVAC operations, which can be leveraged to extract hidden knowledge and insights about operational signatures of these systems (i.e., parameter-value pairs set for running the equipment) and their relationship to energy profiles. This study aims to identify critical parameters of HVAC systems that drive the changes in the building energy-use profiles and develop an automated approach for identifying HVAC operational signatures and their energy profiles in buildings. The approach relies on data-driven methodologies and is composed of three major steps: data preprocessing, feature selection, and signature discovery and analysis. The approach was tested on four air handling units (AHUs) in different buildings. The results showed that it is possible to define operational signatures for facility operators to run AHUs at these custom settings and achieve about 30% saving in electric power, given the profiles across the operational signatures.

Original languageEnglish (US)
Article number113497
JournalApplied Energy
Volume253
DOIs
StatePublished - Nov 1 2019

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Keywords

  • Building automation system
  • Building energy performance
  • Energy efficiency
  • Machine learning
  • Operational signatures

ASJC Scopus subject areas

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

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