A bi-directional missing data imputation scheme based on LSTM and transfer learning for building energy data

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.