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- Title
Appraising the Building Cooling Load via Hybrid Framework of Machine Learning Techniques.
- Authors
Longlong Yue; Xiangli Liu; Shiliang Chang
- Abstract
The overarching objective of this study lies in the thorough evaluation of the effectiveness of K-nearest neighbors (KNN) models in the precise estimation of building cooling load consumption. This assessment holds significant importance as it pertains to the feasibility and reliability of implementing machine learning techniques, particularly the KNN algorithm, within the domain of building energy management. This evaluation process centers on scrutinizing five distinct spatial metrics closely associated with the KNN algorithm. To refine and enhance the algorithm's predictive capabilities, this endeavor incorporates utilizing test samples drawn from an extensive database. These test samples serve as valuable resources for augmenting the overall predictive accuracy of the model, ultimately leading to more robust and reliable predictions of cooling load consumption within the building systems. Ultimately, the research endeavors to contribute substantially to advancing more energy-efficient and automated cooling system control strategies. Developed models encompass a single base model, another model optimized through the application of African Vultures Optimization, and a third model optimized using the Sand Cat Swarm Optimization technique. The training dataset includes 70% of the data, with eight input variables relating to the geometric and glazing characteristics of the buildings. After validating 15% of the dataset, the performance of the remaining 15% is tested. An analysis of various evaluation metrics reveals that KNSC (K-Nearest Neighbors optimized with the Sand Cat Swarm Optimization) demonstrates remarkable accuracy and stability among the three candidate models. It achieves a substantial reduction in the prediction Root Mean Square Error (RMSE) of 32.8% and 21.5% in comparison to the other two models (KNN and KNAV) and attains a maximum R2 value of 0.985 for cooling load prediction.
- Subjects
COOLING loads (Mechanical engineering); MACHINE learning; K-nearest neighbor classification; ENERGY management; PREDICTION models
- Publication
International Journal of Advanced Computer Science & Applications, 2024, Vol 15, Issue 6, p1360
- ISSN
2158-107X
- Publication type
Article
- DOI
10.14569/ijacsa.2024.01506137