Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods

Room energy demand and thermal comfort predictions in early stages of design based on the Machine Learning methods

Forouzandeh Shahraki, N. (TU Delft Building Physics)
Zomorodian, Zahra Sadat (Shahid Beheshti University)
Tahsildoost, Mohammad (Shahid Beheshti University)
Shaghaghian, Zohreh (Texas A and M University)

2022

Recent studies have focused on data-driven methods for building energy efficiency, by using simulated or empirical data, for energy-based design assessment rather than the common physics-based techniques, which are mostly time-consuming. In this paper, the feasibility of using seven different Machine Learning models, including three single models and four ensemble ones, is studied to predict annual energy demand and thermal comfort of the model. For this purpose, 3024 synthetic samples of a single zone model with seven input features are simulated through the EnergyPlus engine for training in addition to 360 unseen samples as testing data for accuracy reporting. Heating and cooling demands, in addition to five annual thermal comfort indices, are calculated for each data point and used as target indices. Results show Extremely Randomized Trees and Random Forest models had the highest R2 of 0.99 and 0.85 for cooling and heating demands respectively. Also, the R2 of these models for predicting annual comfort was between 0.71 and 0.95. Results are then used to develop a prediction framework of thermal comfort and energy demand performance in the early stages of building design, where most of the information about building characteristics is not yet known.

Artificial intelligence
Machine Learning
Tehran
energy efficiency
energy simulation
data-driven model
early design phase
thermal comfort

http://resolver.tudelft.nl/uuid:bccd1a93-40a1-4c60-bbfc-dfe178ecc1b7

https://doi.org/10.1080/17508975.2022.2049190

2023-07-01

1756-6932

Intelligent Buildings International, 15 (1), 3-20

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2022 N. Forouzandeh Shahraki, Zahra Sadat Zomorodian, Mohammad Tahsildoost, Zohreh Shaghaghian