Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches
Optimizing energy efficiency and minimizing environmental impact in buildings depends critically on managing cooling requirements. The application of Support Vector Regression Model to the prediction of cooling load is explored in this study. It enhances these models with two cutting-edge optimizati...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Bilijipub publisher
2024-03-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
| Subjects: | |
| Online Access: | https://jaism.bilijipub.com/article_193318_57be23d6132acad9c046072f1d73b4d2.pdf |
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| Summary: | Optimizing energy efficiency and minimizing environmental impact in buildings depends critically on managing cooling requirements. The application of Support Vector Regression Model to the prediction of cooling load is explored in this study. It enhances these models with two cutting-edge optimization methods: Crystal Structure Algorithm and Reptile Search Algorithm. SVR, a machine learning technique renowned for its flexibility and interpretability, is used in this study to capture complex relationships between various building parameters and cooling load. The dependent variable, CL, is analyzed about various factors, including relative compactness, wall area, roof area, orientation, surface area, overall height, glazing area, and glazing area distribution. SVR proves to be adept at understanding non-linear relationships, making it suitable for these kinds of applications. The modelling process gains intelligence from the combination of RSA and CSA optimizers. Building management systems stand to benefit greatly from this advancement, which will allow for more precise control over cooling systems and efficient use of energy. Moreover, the hybrid SVCS model, with its minimal RMSE value of 0.747 and remarkable R2 value of 0.994, consistently yields reliable results for CL prediction. This study advances the field of energy-efficient building management by demonstrating how machine learning methods and clever optimization algorithms can be used to predict cooling loads accurately. |
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| ISSN: | 3041-850X |