Improving the energy efficiency of the buildings is a worldwide hot topic nowadays. To assist comprehensive analysis and smart management, high-quality historical data records of the energy consumption is one of the key bases. However, the energy data records in the real world always contain different kinds of problems. The most common problem is missing data. It is also one of the most frequently reported data quality problems in big data/machine learning/deep learning related literature in energy management. However, limited studied have been conducted to comprehensively discuss different kinds of missing data situations, including random missing, continuous missing, and large proportionally missing. Also, the methods used in previous literature often rely on linear statistical methods or traditional machine learning methods. Limited study has explored the feasibility of advanced deep learning and transfer learning techniques in this problem. To this end, this study proposed a methodology, namely the hybrid Long Short Term Memory model with Bi-directional Imputation and Transfer Learning (LSTM-BIT). It integrates the powerful modeling ability of deep learning networks and flexible transferability of transfer learning. A case study on the electric consumption data of a campus lab building was utilized to test the method. Results show that LSTM-BIT outperforms other methods with 4.24% to 47.15% lower RMSE under different missing rates.

如今，提高建筑物的能效是世界范围内的热门话题。为了帮助进行综合分析和智能管理，高质量的能耗历史数据记录是关键依据之一。但是，现实世界中的能源数据记录总是包含各种问题。最常见的问题是缺少数据。它也是能源管理中与大数据/机器学习/深度学习相关的文献中最常报告的数据质量问题之一。但是，已经进行了有限的研究来全面讨论各种类型的丢失数据情况，包括随机丢失，连续丢失和大比例丢失。而且，先前文献中使用的方法通常依赖于线性统计方法或传统的机器学习方法。有限的研究探索了高级深度学习和转移学习技术在此问题中的可行性。为此，本研究提出了一种方法，即具有双向插补和转移学习（LSTM-BIT）的混合长期短期记忆模型。它集成了深度学习网络的强大建模能力和迁移学习的灵活可迁移性。以某校园实验室建筑用电量数据为例，对该方法进行了验证。结果表明，在不同丢失率下，LSTM-BIT优于其他方法，RMSE降低了4.24％至47.15％。即具有双向插补和转移学习（LSTM-BIT）的混合长期短期记忆模型。它集成了深度学习网络的强大建模能力和迁移学习的灵活可迁移性。以某校园实验室建筑用电量数据为例，对该方法进行了验证。结果表明，在不同丢失率下，LSTM-BIT优于其他方法，RMSE降低了4.24％至47.15％。即具有双向插补和转移学习（LSTM-BIT）的混合长期短期记忆模型。它集成了深度学习网络的强大建模能力和迁移学习的灵活可迁移性。以某校园实验室建筑用电量数据为例，对该方法进行了验证。结果表明，在不同丢失率下，LSTM-BIT优于其他方法，RMSE降低了4.24％至47.15％。