MBDiff: Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation

Researchers propose MBDiff, a multi-view behavior-aware diffusion model for probabilistic utility data imputation, which improves imputation accuracy by 7.04% on electricity and 29.1% on water usage datasets for block missingness, outperforming state-of-the-art baselines. The model, developed in collaboration with a major municipal utility provider in Florida, extracts user behavior from global, local, and instance-level views and uses a conditional diffusion process to guide imputation.

arXiv:2607.29177v1 Announce Type: new Abstract: Utility data e.g., electricity, water, and gas consumption , collected by ubiquitous sensors and embedded devices, often contains substantial missing values due to various factors such as device failures and data transmission issues. The data missingness can severely impact utility billing accuracy, hinder demand forecasting, and disrupt efficient utility supply management. As a result, utility data imputation has attracted much interest from both industry and academia. While many studies have attempted to address this issue, most of them rely on aggregated datasets for training, overlooking rich user behavior information, which could provide valuable insights for more accurate imputation. However, learning comprehensive user behavior from long-term, diverse, and incomplete utility data remains a significant challenge. Moreover, leveraging user behavior information to guide imputation is nontrivial due to the indirect nature of the correlations. To address these challenges, we propose MBDiff, a Multi-view Behavior-aware Diffusion Model for Probabilistic Utility Data Imputation. MBDiff incorporates two key technical components: i a multi-view User Behavior Extraction module that learns comprehensive user behavior from multiple perspectives, including global, local, and instance-level views; and ii a behavior-aware conditional diffusion model consisting of a reference selection module and a conditional attentional denoising network to impute utility data in a computationally efficient manner. We implement and evaluate MBDiff by collaborating with one of the largest municipal utility providers in Florida. Experimental results demonstrate our proposed MBDiff effectively outperforms state-of-the-art baselines, e.g., it improves 7.04% and 29.1% on the electricity and water usage datasets for block missingness imputation, respectively.