Evaluate the Energy Saving from Window Opening Behavior Through Coupling a Deep Learning Model with EnergyPlus

Pratik Pandey, Bing Dong, Nina Sharifi, Mason Kenneth Malsegna

Research output: Chapter in Book/Entry/PoemConference contribution

Abstract

Buildings consume about 40% of the electricity generated and significantly burden the energy sector. A new concept of net-zero energy building has emerged to ease this burden. The preliminary design relies heavily on the simulation data from building simulation engines like Energy Plus. Research has pointed out about a 30% difference in average between actual and simulated energy consumption. One of the primary reasons for this discrepancy is pointed towards the use of schedule-based occupant behavior (OB) during simulation. This conventional schedule should be replaced by a model that could predict the occupants’ actions reasonably. Binomial Logistic Regression and Neural Network architectures were developed and tested for their prediction accuracy of window's state in the residential dorms of a local university at Syracuse, NY. The neural network models outperformed the Binomial logistic regression model by a considerable margin when both of them were tested in entirely different homes. We found that the Deep Neural Network model with ‘Adam’ optimizer and ridge regularization parameter of 0.01 performed the best to predict the state of the windows when the learning rate was 0.01. These models were then integrated into the Energy Plus (v 9.6) using newly released E+ Python API and tested in a single zone of the campus building. Using the ANN-driven schedules, we observed that the total sensible monthly heating load requirement for the month of September is reduced by 38.56%, and the total sensible cooling load requirement is reduced by 55.52%.

Original languageEnglish (US)
Title of host publicationProceedings of the 5th International Conference on Building Energy and Environment
EditorsLiangzhu Leon Wang, Hua Ge, Mohamed Ouf, Zhiqiang John Zhai, Dahai Qi, Chanjuan Sun, Dengjia Wang
PublisherSpringer Science and Business Media Deutschland GmbH
Pages959-969
Number of pages11
ISBN (Print)9789811998218
DOIs
StatePublished - 2023
Event5th International Conference on Building Energy and Environment, COBEE 2022 - Montreal, Canada
Duration: Jul 25 2022Jul 29 2022

Publication series

NameEnvironmental Science and Engineering
ISSN (Print)1863-5520
ISSN (Electronic)1863-5539

Conference

Conference5th International Conference on Building Energy and Environment, COBEE 2022
Country/TerritoryCanada
CityMontreal
Period7/25/227/29/22

Keywords

  • Artificial neural network
  • EnergyPlus Python API
  • EnergyPlus machine learning integration
  • Window opening schedule

ASJC Scopus subject areas

  • Environmental Engineering
  • Information Systems

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