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Research on the Improvement of the Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method for the Building Cooling Load Prediction
Yuan, T.; Liu, Z.; Zhang, L.; Fan, D.; Chen, J. Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction. Processes2023, 11, 3389.
Yuan, T.; Liu, Z.; Zhang, L.; Fan, D.; Chen, J. Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction. Processes 2023, 11, 3389.
Yuan, T.; Liu, Z.; Zhang, L.; Fan, D.; Chen, J. Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction. Processes2023, 11, 3389.
Yuan, T.; Liu, Z.; Zhang, L.; Fan, D.; Chen, J. Improvement of an Artificial Intelligence Algorithm Prediction Model Based on the Similarity Method: A Case Study of Office Building Cooling Load Prediction. Processes 2023, 11, 3389.
Abstract
Artificial intelligence algorithms have gained widespread adoption in the field of air conditioning load prediction. However, their prediction accuracy is substantially influenced by the quality of training samples. Training samples that lack relevance to the predicted moments can introduce interference into the neural network's learning process, potentially leading to a state of local convergence during its iterative process. This, in turn, diminishes the robustness and generalization capabilities of the prediction model. To enhance the prediction accuracy of air conditioning load prediction models based on artificial intelligence algorithms, this study presents an artificial intelligence algorithm prediction model based on the method of sample similarity sample screening. Initially, the comprehensive similarity coefficient between samples is computed using the gray correlation analysis method, enriched with enhancements in information entropy. Subsequently, a subset of closely related samples is extracted from the original dataset and employed as the training dataset for the artificial intelligence prediction model. Finally, the trained artificial intelligence algorithm prediction model is deployed for air conditioning load prediction. The results illustrate that the method of screening training samples based on sample similarity effectively improves the prediction accuracy of BP neural network (BPNN) and extreme learning machine (ELM) prediction models. However, it is important to note that this approach may not be suitable for genetic algorithm BPNN (GABPNN) and support vector regression (SVR) models.
Engineering, Architecture, Building and Construction
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
