Temporally Chained Equations: An Interpretable Missing Data Imputation Approach for Smart Meters with Low Data Requirements

Smart grids enable real-time monitoring and optimization of energy generation, transmission, and consumption. Ensuring the integrity of data collected by smart meters is crucial for grid operation and data-driven decision-making, but missing data due to communication failures or device malfunctions can compromise data reliability. Recent studies have proposed deep learning techniques to impute missing points; however, these models require large training data, are computationally expensive, and struggle to scale to a large consumer base. Moreover, the interpretability of the imputation is also a concern, hindering trust and acceptance. To address these issues, this paper proposes Temporally Chained Equations (TCE), an interpretable, computationally lightweight missing data imputation approach for smart meters with low data requirements. TCE forms chained equations across the temporal axis using lead and lag features and imputes missing points in a manner coherent with neighboring and seasonally correlated points. Local normalization reduces the impact of outliers, and an iterative process refines the estimates until convergence. Experiments on a real-world dataset show that TCE outperforms related techniques, particularly for random missing points or short sequences of continuous missing points.