A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A Weight Clustering-Based Pattern Recognition Method for Improving Building’s Cooling Load Prediction Reliability
Accurate building cooling load prediction can effectively guide the start-stop strategies and capacities matching of chillers and is also the basis of model predictive control of the heating, ventilation, and air conditioning (HVAC). Most of the existing literature focused on the structural optimization or selection of cooling load prediction models, and rarely in-depth studies on the matching between data and models. However, the data features determine the upper limit of model prediction performances, thus leading the unsatisfactory prediction accuracy in the existing methods. Aiming at this, the paper proposed a novel weight clustering-based pattern recognition method for improving building cooling load prediction reliability. Firstly, after the outliers were removed, the Pearson correlation analysis was used to select the key input variables for the models. Secondly, the sensitivity analysis was utilized to obtain the weights of input variables on the cooling load, and then the weights were introduced into the K-means clustering algorithm. Finally, the training data of models were classified by the clustering, and the corresponding training set was matched according to the predicted sample’s features. The case study showed that the weight clustering-based pattern recognition method has a significant improvement in prediction accuracies to the multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) models (e.g., 35%, 36%, and 15% reduction in mean absolute percentage error (MAPE), respectively), In addition, the optimal clustering number, the clustering effects with or without the weights, etc. were also investigated. This paper’s method can provide a novel idea for the models’ data preprocessing.
A Weight Clustering-Based Pattern Recognition Method for Improving Building’s Cooling Load Prediction Reliability
Accurate building cooling load prediction can effectively guide the start-stop strategies and capacities matching of chillers and is also the basis of model predictive control of the heating, ventilation, and air conditioning (HVAC). Most of the existing literature focused on the structural optimization or selection of cooling load prediction models, and rarely in-depth studies on the matching between data and models. However, the data features determine the upper limit of model prediction performances, thus leading the unsatisfactory prediction accuracy in the existing methods. Aiming at this, the paper proposed a novel weight clustering-based pattern recognition method for improving building cooling load prediction reliability. Firstly, after the outliers were removed, the Pearson correlation analysis was used to select the key input variables for the models. Secondly, the sensitivity analysis was utilized to obtain the weights of input variables on the cooling load, and then the weights were introduced into the K-means clustering algorithm. Finally, the training data of models were classified by the clustering, and the corresponding training set was matched according to the predicted sample’s features. The case study showed that the weight clustering-based pattern recognition method has a significant improvement in prediction accuracies to the multiple linear regression (MLR), multiple nonlinear regression (MNR), and artificial neural network (ANN) models (e.g., 35%, 36%, and 15% reduction in mean absolute percentage error (MAPE), respectively), In addition, the optimal clustering number, the clustering effects with or without the weights, etc. were also investigated. This paper’s method can provide a novel idea for the models’ data preprocessing.
A Weight Clustering-Based Pattern Recognition Method for Improving Building’s Cooling Load Prediction Reliability
Environ Sci Eng
Wang, Liangzhu Leon (editor) / Ge, Hua (editor) / Zhai, Zhiqiang John (editor) / Qi, Dahai (editor) / Ouf, Mohamed (editor) / Sun, Chanjuan (editor) / Wang, Dengjia (editor) / Chen, Sihao (author) / Wang, Liangzhu Leon (author) / Li, Jing (author)
International Conference on Building Energy and Environment ; 2022
Proceedings of the 5th International Conference on Building Energy and Environment ; Chapter: 26 ; 233-242
2023-09-05
10 pages
Article/Chapter (Book)
Electronic Resource
English
Passive cooling effect of traditional Japanese building's features
| Online Contents | 2007
Prediction of building's temperature using neural networks models
| Elsevier | 2005
PRODUCTS - Cooling a building's interior starts at the top
Online Contents | 1998