Building energy consumption (BEC) prediction often requires constructing a corresponding model for each building based historical data. However, the constructed model for one building is difficult to be reused in other buildings. Recent approaches have shown that cloud-edge collaboration architecture is promising in realizing model reuse. How to complete the reuse of cloud energy consumption prediction models at the edge and reduce the computational cost of the model training is one of the key issues that need to be solved. To handle the above problems, a cloud-edge collaboration based transferring prediction method for BEC is proposed in this paper. Specifically, a model library stored prediction models for different types of buildings is constructed based the historical energy consumption data and the long short-term memory (LSTM) network in the cloud firstly; then, the similarity measurement strategies of time series with different granularity are given, and the model to be transferred from the model library is matched by analyzing the similarity between observation data uploaded to the cloud and the historical data collected in the cloud; finally, the fine-tuning strategy of the matching prediction model is given, and this model is fine-tuned at the edge to achieve its reuse in concrete application scenarios. Experiments on practical datasets reveal that compared with the prediction model which doesn’t utilize the transfer strategy, the proposed prediction model has better performance according to MAE and RMSE. Experimental results also confirm that the proposed method effectively reduces the computational cost of the network training at the edge.

中文翻译：

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基于云边协同的建筑能耗传递预测

建筑能耗 (BEC) 预测通常需要为每个基于历史数据的建筑构建相应的模型。然而，为一栋建筑构建的模型很难在其他建筑中重复使用。最近的方法表明，云边缘协作架构在实现模型重用方面很有前景。如何在边缘完成云能耗预测模型的复用，降低模型训练的计算成本是需要解决的关键问题之一。针对上述问题，本文提出了一种基于云边协作的BEC迁移预测方法。具体来说，首先根据历史能耗数据和云端的长短期记忆（LSTM）网络构建不同类型建筑的模型库存储预测模型；然后给出不同粒度时间序列的相似性度量策略，通过分析上传到云端的观测数据与云端采集的历史数据之间的相似性，匹配模型库中待转移的模型；最后给出匹配预测模型的微调策略，并在边缘对该模型进行微调，以实现其在具体应用场景中的复用。在实际数据集上的实验表明，与不使用迁移策略的预测模型相比，根据 MA​​E 和 RMSE，所提出的预测模型具有更好的性能。实验结果也证实了所提出的方法有效地降低了边缘网络训练的计算成本。