A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Evaluate the Energy Saving from Window Opening Behavior Through Coupling a Deep Learning Model with EnergyPlus
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%.
Evaluate the Energy Saving from Window Opening Behavior Through Coupling a Deep Learning Model with EnergyPlus
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%.
Evaluate the Energy Saving from Window Opening Behavior Through Coupling a Deep Learning Model with EnergyPlus
Environ Sci Eng
Wang, Liangzhu Leon (editor) / Ge, Hua (editor) / Zhai, Zhiqiang John (editor) / Qi, Dahai (editor) / Ouf, Mohamed (editor) / Sun, Chanjuan (editor) / Wang, Dengjia (editor) / Pandey, Pratik (author) / Dong, Bing (author) / Sharifi, Nina (author)
International Conference on Building Energy and Environment ; 2022
Proceedings of the 5th International Conference on Building Energy and Environment ; Chapter: 102 ; 959-969
2023-09-05
11 pages
Article/Chapter (Book)
Electronic Resource
English
Heat transfer through window frames in EnergyPlus: model evaluation and improvement
| Taylor & Francis Verlag | 2019