Energy saving potential of a model-predicted frost prevention method for energy recovery ventilators

Research output: Contribution to journalArticlepeer-review

Abstract

This study evaluates the energy-saving potential of a prediction model-based pre-heat coil operation method for frost prevention in energy recovery ventilators, compared to existing approaches. Energy recovery ventilator (ERV) requires pre-heating of the incoming outdoor air to prevent undesirable condensation and frost formation in the enthalpy exchanger during winter. Conventionally, the introduced outdoor air is pre-heated to a certain constant temperature using a pre-heat coil, resulting in unnecessary energy consumption. Maintaining a constant pre-heat temperature during the operation of the ERV is not ideal as the frost threshold temperature varies with the outdoor air temperature and humidity. Therefore, to reduce pre-heating energy consumption, a prediction model-based pre-heat coil operation method is proposed herein. A numerical model predicting the frost threshold temperature based on the outdoor air and exhaust air conditions was developed, and validated using the optimal Latin hypercube design method. Subsequently, a series of energy simulations was performed considering an identical residential model, located in 8 cities with different climatic conditions, to evaluate the energy-saving potential of the proposed pre-heat coil operation method compared to conventional methods. The proposed method consumed 7%–72% less energy in the ERV operation and required a 1%–21% smaller pre-heat coil capacity than the conventional method. Thus, the proposed operation method is superior to the conventional method as it prevents frost formation and consumes a minimal amount of energy.

Original languageEnglish
Article number116450
JournalApplied Thermal Engineering
Volume185
DOIs
StatePublished - 2021 Feb 25

Keywords

  • Condensation
  • Energy recovery ventilator
  • Frost prevention
  • Model-predicted control
  • Temperature control

Fingerprint Dive into the research topics of 'Energy saving potential of a model-predicted frost prevention method for energy recovery ventilators'. Together they form a unique fingerprint.

Cite this