Predictive maintenance scheduling optimization of building heating, ventilation, and air conditioning systems

Yaqing Wu, Christos T. Maravelias, Michael J. Wenzel, Mohammad N. ElBsat, Robert T. Turney

Research output: Contribution to journalArticlepeer-review

Abstract

We present optimization methods for predictive maintenance scheduling of building heating, ventilation, and air conditioning (HVAC) systems via mixed-integer programming. The optimization framework we introduce is composed of optimization models and parameter generation methods. Optimization models with time discretized into daily time periods are developed to enable the consideration of slow operation-dependent degradation during long horizons while maintaining computational efficiency. To improve the quality of the obtained maintenance schedules, system operation is simultaneously optimized. Parameter generation methods are introduced to provide parameters for constructing operation-related constraints. The proposed optimization framework can account for various HVAC systems with complex configurations. We show the quality of the generated operation-related parameters, and we provide medium-horizon case studies of central plants to show the model performance.

Original languageEnglish (US)
Article number110487
JournalEnergy and Buildings
Volume231
DOIs
StatePublished - Jan 15 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering

Keywords

  • Central plants
  • Maintenance
  • Mixed-integer programming
  • Scheduling
  • Thermal energy storage

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