# Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

arXiv: https://arxiv.org/abs/2211.16332 · HTML: https://mlsanity.com/paper/2211.16332/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2022.

## Built on by (most cited first, 4 of 4)

- [BERT-PIN: A BERT-based Framework for Recovering Missing Data Segments in Time-series Load Profiles](https://mlsanity.com/paper/2310.17742/) (2023)
- [Applying Fine-Tuned LLMs for Reducing Data Needs in Load Profile Analysis](https://mlsanity.com/paper/2406.02479/) (2024)
- [Boosting Hyperspectral Image Classification with Gate-Shift-Fuse Mechanisms in a Novel CNN-Transformer Approach](https://mlsanity.com/paper/2406.14120/) (2024)
- [SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models](https://mlsanity.com/paper/2601.21706/) (2026)

## Abstract (arXiv)

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). We propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.