Research Article
The Personalized Thermal Comfort Prediction Using an MH-LSTM Neural Network Method
Table 6
Overview of performance of the experimental model.
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Strategy 1 (S1): Training with collective dataset, Strategy 2 (S2): Training with collective dataset and transfer learning with individual dataset, Strategy 3 (S3): Training with individual dataset. Values indicated in bold highlight the best performance metrics achieved across the different training strategies for the multi-head LSTM model. The significance of these bolded metrics (RMSE: 0.2225, MAE: 0.1620, MRE: 0.1458) under Strategy 3 indicates the highest efficiency and accuracy in prediction when the model is trained with individual datasets. This underscores the effectiveness of using a more tailored approach in dataset training for improving model performance in these specific metrics. |