A general multi-source ensemble transfer learning framework integrate of LSTM-DANN and similarity metric for building energy prediction

Transfer learning can improve building energy prediction performance by utilizing the knowledge learned from source domain. However, most studies focus on the single-source transfer learning and may lead to model performance degradation when there exists large domain shift between the single source domain and target domain. To address this issue, this study proposes a multi-source ensemble transfer learning (Multi-LSTM-DANN) framework integrate of LSTM-DANN neural network and similarity metric, which can enhance the prediction performance of target building power consumption by using multi-source building data (domain). LSTM-DANN is first used to extract the domain invariant features between each pair of source domain and target domain. Then maximum mean discrepancy (MMD) is applied to metric the distance between each pair of the extracted domain invariant features. Finally, the reciprocal of MMD is used as similarity metric index to calculate the regression weight and prediction value of the proposed Multi-LSTM-DANN model. Experiments with different number of source domains are conducted to demonstrate the effectiveness of the proposed Multi-LSTM-DANN framework. Results demonstrate that most multi-source transfer learning models can enhance the prediction performance of the target building power consumption compared to the corresponding single-source transfer learning models. The proposed Multi-LSTM-DANN framework can provide guiding significance for the application of multi-source building data in the future. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study proposes a multi-source ensemble transfer learning framework to enhance building energy prediction performance. Experimental results show that multi-source transfer learning models can improve the prediction performance of target building power consumption compared to single-source models.