Multi-source transfer learning guided ensemble LSTM for building multi-load forecasting

Generally, it is difficult to establish an accurate building load forecasting model by using insufficient energy data. Although the transfer of knowledge from similar buildings can effectively solve this problem, there is still a lack of effective methods for both the selection of source domain buildings and the use of transfer knowledge when many candidate buildings are available. In view of this, this paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting (MTE-LSTM). Firstly, a two-stage source-domain building matching method based on dominance comparison is developed to find multiple source-domain buildings similar to the target building. Next, an LSTM modeling strategy combining transfer learning and fine-tune technology is proposed, which uses multiple source-domain data to generate multiple basic load forecasting models for the target building. Following that, a model ensemble strategy based on similarity degree is given to weight the output results of basic forecasting models. Applications in many real buildings shows that the proposed building multi-energy load forecasting method can obtain high-precision load forecasting results when the target building data is relatively few.

Automated summary

The paper proposes a multi-source transfer learning guided ensemble LSTM method for building multi-load forecasting, which includes a two-stage building matching method and an LSTM modeling strategy to achieve high-precision load forecasting results with limited target building data.