Using Deep Neural Network to Predict Natural Ventilation Behavior in Student Dorms

Pratik Raj Pandey, Bing Dong, Nina Sharifi

Research output: Chapter in Book/Entry/PoemConference contribution

1 Scopus citations


People spend about 87 - 92% of their overall time of day in some form of indoor environment. It is also well established that buildings consume about 40% of the total electricity generated. Studies have shown that operating windows during HVAC use can increase the heating and cooling load. Until now, fixed window opening and closing schedules have been used in Building Energy Simulation engines (BES) like Energy Plus. We studied the window opening behavior of students living in a residential dorm at Syracuse University for over a year. The window opening behavior changed depending on the location of the window and the time of the year. During summer, the bedroom windows were opened substantially for a longer time and living room windows were opened briefly. During transition seasons, all windows were opened very briefly. We created a specific set of neural network models that could predict the state of the window and the duration of the window opening. We modeled the open/close state change for all three windows in three different zones using Artificial Neural Network (ANN) machine learning architecture. The three model, when trained on individual datasets generated a True Positive Rate (TPR) of 0.97, 0.99, and 0.97. The newly released Python API incorporated the models into Energy Plus. The sensible heating/cooling energy for the living room, north-facing bedroom, and south-facing bedroom were calculated based on the window schedule predicted by the ANN model. Compared to the baseline model of complete mechanical ventilation, the heating energy was reduced by 3.96% and 24.78% for north and south-facing bedrooms, respectively, for October.

Original languageEnglish (US)
Title of host publication2023 ASHRAE Winter Conference
Number of pages8
ISBN (Electronic)9781955516471
StatePublished - 2023
Event2023 ASHRAE Winter Conference - Atlanta, United States
Duration: Feb 4 2023Feb 8 2023

Publication series

NameASHRAE Transactions
ISSN (Print)0001-2505


Conference2023 ASHRAE Winter Conference
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Building and Construction
  • Mechanical Engineering


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