Abstract
Building energy performance assessments are essential in High-Performance Building Design (HPBD) in order to reduce energy consumption and carbon emissions. With advancement in data analytics, rapid and accurate machine learning-based building energy consumption prediction models have emerged. These models can be used by non-professionals as an alternative to time-consuming energy simulation software, offering benefits in HPBD. Therefore, the main objective of the present study is to develop a prediction model using data generated by physics-based simulations of a typical open-plan office space. The model predicts annual energy consumption, CO2 emissions, and percentage of comfort hours during the design phase. Various configurations of Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forrest (RF), and K-Nearest Neighbors (KNN) algorithms were trained and tested with the generated data via the DesignBuilder software. The technical parameters considered as inputs include U-values of envelope constructions, Window to Wall Ratio (WWR), orientation, and Heating, Ventilation, and Air-Conditioning (HVAC) systems. The results indicate that there is no clear linear relationship between individual inputs and the target indicators. However, ANN, with its ability to handle non-linear relationships, performed the best, achieving a maximum Coefficient of Determination (R2) value of 0.997 for predicting percentage of comfort hours and outperforms the other algorithms. Furthermore, the results show that RF is the next best algorithm, with 0.96 ≤ R2Test ≤ 0.98 for predicting the various target variables. SVM with Radial Basis Function (SVM-RBF) follows, with 0.89 ≤ R2Test ≤ 0.95. Contrary to ANN, SVM, and RF algorithms with high abilities to learn complex pattern between various independent parameters and the target variable, KNN exhibits the poorest performance, with 0.88 ≤ R2Test ≤ 0.91. Additionally, it is observed that with a maximum time cost of 619 s, ANN with three layers is able to learn the relationships between the inputs and target indicators at a convenient speed. Since knowledge-based decision making in the early design stages is crucial for achieving the optimum solutions to reduce energy consumption and related CO2 emissions while ensuring occupants’ comfort and minimizing future modifications and costs, high-speed and accurate prediction methods for design stage evaluation are essential.
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Abbreviations
- AI:
-
Artificial Intelligence
- ANN:
-
Artificial Neural Network
- BR:
-
Bagging Regressor
- BT:
-
Boosted Tree
- DQL:
-
Deep Q-Learning
- DRL:
-
Deep Reinforcement Learning
- DT:
-
Decision Tree
- EA:
-
Evolutionary Algorithm
- ERT:
-
Extremely Randomized Trees
- GA:
-
Genetic Algorithm
- HVAC:
-
Heating, Ventilation, and Air-Conditioning
- KNN:
-
K-Nearest Neighbors
- MAE:
-
Mean Absolute Error
- ML:
-
Machine Learning
- MLP:
-
Multi-layer Perceptron
- MOGA:
-
Multi-Objective Genetic Algorithm
- MSE:
-
Mean Square Error
- PFP:
-
Parallel Fan Power
- PPD:
-
Predicted Percentage of Dissatisfied
- PTAC:
-
Packaged Terminal Air Conditioning
- PTHP:
-
Packaged Terminal Heat Pump
- R 2 :
-
Coefficient of Determination
- ReLU:
-
Rectified Linear Unit
- RBFS:
-
Recursive Brute-Force Search
- RF:
-
Random Forest
- RMSE:
-
Root Mean Square Error
- RL:
-
Reinforcement Learning
- RT:
-
Regression Tree
- SVM:
-
Support Vector Machine
- U-value:
-
Thermal Transmittance
- VAV:
-
Variable Air Volume
- VRF:
-
Variable Refrigerant Flow
- WWR:
-
Window to Wall Ratio
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Roodkoly, S.H., Fard, Z.Q., Tahsildoost, M. et al. Development of a simulation-based ANN framework for predicting energy consumption metrics: a case study of an office building. Energy Efficiency 17, 5 (2024). https://doi.org/10.1007/s12053-024-10185-1
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DOI: https://doi.org/10.1007/s12053-024-10185-1