Imputing the long-term missing heating load data using a generative network

Accurately filling in missing heating data is essential for ensuring data quality in applications such as energy management optimization and building efficiency analysis. Traditional machine learning methods use historical heating data as an input feature to predict the following missing data. However, when the duration of missing data is long, previous estimated values are inevitably used for further imputation, leading to error accumulation and a growing deviation from true values. To overcome this problem, this paper proposes a generative network that can fill missing data solely based on weather and temporal data, without using previous imputed values for further imputation. Our method outperformed the state of the art such as Seq2seq and Transformer, achieving relative normalized root mean square error (NRMSE) reductions of 1.65 % to 41.38 %, 0.30 % to 66.43 %, and 14.84 % to 50.22 % across three different data sources. In addition, with our proposed method, the effect of selecting different weather variables on model performance, and the benefits of transfer learning under limited data were also demonstrated. The relative NRMSE reduction is between 3.88 % to 15.85 % in cold months and from 7.49 % to 12.29 % in warm months when applying transfer learning. • A novel 2D generative network for imputing long-term missing heat load data. • Superior accuracy versus state-of-the-art models across diverse seasons. • Improved interpretability via analysis of key weather variable impacts. • Effective with limited data through multiple transfer learning strategies.