Missing Data Imputation for Smart Meters: Conditional Denoising Diffusion Model & Temporally Chained Equations
Abstract
Smart meter data are crucial for power grid management. However, missing data caused by communication and device failures reduce the quality of the data. While machine learning- based solutions have been proposed for missing data imputation, recent developments in generative models have created opportunities for improvements. This thesis proposes two approaches for missing data imputation in smart meter data. The first approach, Conditional Denoising Diffusion Model, leverages diffusion models to generate coherent imputations based on the daily load profile together with a guidance mechanism that captures histor- ical context. Our approach outperforms existing techniques, especially for a substantial number of random or consecutive missing points by achieving 11.33% lower normalized root mean square error than the compared methods when 40% of the points are missing. However, since it is a deep learning technique, it requires abundant data and computational resources. The second approach, Temporally Chained Equations, reduces computational and data requirements by imputing missing points iteratively using lag and lead features, local normalization, and linear regression. It outperforms compared baselines in random missing points scenarios by achieving 6.32% lower normalized root mean square error in random missing scenarios. However, its performance reduces when many consecutive points are missing.