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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2024-03-01
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| Series: | Journal of Artificial Intelligence and System Modelling |
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| Online Access: | https://jaism.bilijipub.com/article_193318_57be23d6132acad9c046072f1d73b4d2.pdf |
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| author | Honglei Yao Andrew Topper |
| author_facet | Honglei Yao Andrew Topper |
| author_sort | Honglei Yao |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c8f6a7dddda4ac8a12074fd570dbbef |
| institution | Kabale University |
| issn | 3041-850X |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Bilijipub publisher |
| record_format | Article |
| series | Journal of Artificial Intelligence and System Modelling |
| spelling | doaj-art-2c8f6a7dddda4ac8a12074fd570dbbef2025-08-20T03:36:53ZengBilijipub publisherJournal of Artificial Intelligence and System Modelling3041-850X2024-03-010201668210.22034/jaism.2024.445961.1027193318Cooling Load Prediction via Support Vector Regression in Individual and Hybrid ApproachesHonglei Yao0Andrew Topper1State Grid Shandong Electric Power Research Institute, ChinaUniversity of Grand Valley State, Grand Rapids, Michigan, 49504, United StatesOptimizing 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.https://jaism.bilijipub.com/article_193318_57be23d6132acad9c046072f1d73b4d2.pdfbuilding energycooling loadsupport vector regressionreptile search algorithmcrystal structure algorithm |
| spellingShingle | Honglei Yao Andrew Topper Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches Journal of Artificial Intelligence and System Modelling building energy cooling load support vector regression reptile search algorithm crystal structure algorithm |
| title | Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches |
| title_full | Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches |
| title_fullStr | Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches |
| title_full_unstemmed | Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches |
| title_short | Cooling Load Prediction via Support Vector Regression in Individual and Hybrid Approaches |
| title_sort | cooling load prediction via support vector regression in individual and hybrid approaches |
| topic | building energy cooling load support vector regression reptile search algorithm crystal structure algorithm |
| url | https://jaism.bilijipub.com/article_193318_57be23d6132acad9c046072f1d73b4d2.pdf |
| work_keys_str_mv | AT hongleiyao coolingloadpredictionviasupportvectorregressioninindividualandhybridapproaches AT andrewtopper coolingloadpredictionviasupportvectorregressioninindividualandhybridapproaches |