A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A neural network-based surrogate model to predict building features from heating and cooling load signatures
Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis.
A neural network-based surrogate model to predict building features from heating and cooling load signatures
Addressing the challenges of scalable and cost-effective energy performance analysis in mid to high-rise office buildings, this paper introduces a novel approach utilizing an inverse-based artificial neural network (ANN). This ANN was trained on synthetically generated heating and cooling load parameters – derived from simulations conducted in EnergyPlus – to predict characterization parameters, including the building envelope, internal heat gains, and HVAC operational parameters. Diverging from traditional forward surrogate models, this inverse surrogate model fills a critical gap in current building energy modeling approaches that are hindered by data and resource limitations. Its effectiveness is verified with a testing dataset of 3000 buildings and is further demonstrated through a case study in Ottawa, Ontario. Proving to be an efficient, cost-effective tool for energy retrofit screening, the model is enhanced by a user-friendly web-based application (Ferreira and Gunay), marking a significant advancement in accessible building energy analysis.
A neural network-based surrogate model to predict building features from heating and cooling load signatures
Ferreira, Shane (author) / Gunay, Burak (author) / Wills, Adam (author) / Rizvi, Farzeen (author)
Journal of Building Performance Simulation ; 17 ; 631-654
2024-09-02
24 pages
Article (Journal)
Electronic Resource
English
One-Day Building Cooling Load Prediction Based on Bidirectional Recurrent Neural Network
| Springer Verlag | 2018
Building signatures: A holistic approach to the evaluation of heating and cooling concepts
| British Library Online Contents | 2010
Building signatures: A holistic approach to the evaluation of heating and cooling concepts
| Online Contents | 2010
Building signatures: A holistic approach to the evaluation of heating and cooling concepts
| Online Contents | 2010