| id |
ecaade2024_167 |
| authors |
Alammar, Ammar; Alymani, Abdulrahman; Jabi, Wassim |
| year |
2024 |
| title |
Building Energy Efficiency Estimations with Random Forest for Single and Multi-Zones |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.2.365
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 2, pp. 365–374 |
| summary |
Surrogate models (SM) present an opportunity for rapid assessment of a building's performance, surpassing the pace of simulation-based methods. Setting up a simulation for a single concept involves defining numerous parameters, disrupting the architect's creative flow due to extended simulation run times. Therefore, this research explores integrating building energy analysis with advanced machine learning techniques to predict heating and cooling loads (KWh/m2) for single and multi-zones in buildings.
To generate the dataset, the study adopts a parametric generative workflow, building upon Chou and Bui's (2014) methodology. This dataset encompasses multiple building forms, each with unique topological connections and attributes, ensuring a thorough analysis across varied building scenarios. These scenarios undergo thermal simulation to generate data for machine learning analysis. The study primarily utilizes Random Forest (RF) as a new technique to estimate the heating and cooling loads in buildings, a critical factor in building energy efficiency. Following that, A random search approach is utilized to optimize the hyperparameters, enhancing the robustness and accuracy of the machine learning models employed later in the research. The RF algorithms demonstrate high performance in predicting heating and cooling loads (KWh/m2), contributing to enhanced building energy efficiency. The study underscores the potential of machine learning in optimizing building designs for energy efficiency. |
| keywords |
Heating and Cooling loads, Topology, Machine learning, Random Forest |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,358,836 bytes) |
| references |
Content-type: text/plain
|
Ahmad, M.W. et al. (2017)
 Random Forests and Artificial Neural Network for Predicting Daylight Illuminance and Energy Consumption
, 5th Conference of International Building Performance Simulation Association , pp. 1-7
|
|
|
|
|
Alammar, A. and Jabi, W. (2023)
Generation of a Large Synthetic Database of Office Towers Energy Demand Using Simulation and Machine Learning
, Springer Nature Singapore. Available at: http://dx.doi.org/10.1007/978-981-99-2217-8_27
|
|
|
|
|
Chou, J.S. and Bui, D.K. (2014)
Modeling heating and cooling loads by artificial intelligence for energy-efficient building design
, Energy and Buildings 82(2014), pp. 437-446. Available at: http://dx.doi.org/10.1016/j.enbuild.2014.07.036
|
|
|
|
|
Jabi, W. et al. (2018)
Topologic A toolkit for spatial and topological modelling
, Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe 2(September), pp. 449-458. doi: 10.52842/conf.ecaade.2018.2.449
|
|
|
|
|
Jabi, W. (2022)
Topologic and BHoM : Enhancing Energy Analysis Workflows Through Topological Modelling
, (October 201
|
|
|
|
|
Liu, Y. et al. (2021)
Enhancing building energy efficiency using a random forest model: A hybrid prediction approach
, Energy Reports 7, pp. 5003-5012. Available at: https://doi.org/10.1016/j.egyr.2021.07.135
|
|
|
|
|
Rashidifar, R. (2020)
Estimation of {Energy} {Performance} of {Buildings} {Using} {Machine} {Learning} {Tools}
, (Cl). Available at: http://dx.doi.org/10.31224/osf.io/4esdw
|
|
|
|
|
Sarmas, E. et al. (2023)
Estimating the Energy Savings of Energy Efficiency Actions with Ensemble Machine Learning Models
, Applied Sciences (Switzerland) 13(4). doi: 10.3390/app13042749
|
|
|
|
|
Seyedzadeh, S. et al. (2018)
Machine learning for estimation of building energy consumption and performance: a review
, Visualization in Engineering 6(1). doi: 10.1186/s40327-018-0064-7
|
|
|
|
|
Tso, G.K.F. and Yau, K.K.W. (2007)
Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks
, Energy 32(9), pp. 1761-1768. doi: 10.1016/j.energy.2006.11.010
|
|
|
|
|
Westermann, P. and Evins, R. (2019)
Surrogate modelling for sustainable building design - A review
, Energy and Buildings 198, pp. 170-186. Available at: https://doi.org/10.1016/j.enbuild.2019.05.057
|
|
|
|
|
Yu, Z. et al. (2010)
A decision tree method for building energy demand modeling
, Energy and Buildings 42(10), pp. 1637-1646. Available at: http://dx.doi.org/10.1016/j.enbuild.2010.04.006
|
|
|
|
| last changed |
2024/11/17 22:05 |
|
