# Generating Contextual Load Profiles Using a Conditional Variational Autoencoder

arXiv: https://arxiv.org/abs/2209.04056 · HTML: https://mlsanity.com/paper/2209.04056/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Generating Multivariate Load States Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2110.11435/) (2021)

## Built on by (most cited first, 6 of 6)

- [Generating Synthetic Net Load Data with Physics-informed Diffusion Model](https://mlsanity.com/paper/2406.01913/) (2024)
- [GUIDE-VAE: Advancing Data Generation with User Information and Pattern Dictionaries](https://mlsanity.com/paper/2411.03936/) (2024)
- [Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets](https://mlsanity.com/paper/2506.11281/) (2025)
- [Optimal Power Flow in Highly Renewable Power System Based on Attention Neural Networks](https://mlsanity.com/paper/2311.13949/) (2023)
- [One Model to Forecast Them All and in Entity Distributions Bind Them](https://mlsanity.com/paper/2501.15499/) (2025)
- [Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder](https://mlsanity.com/paper/2303.11410/) (2023)

## Abstract (arXiv)

Generating power system states that have similar distribution and dependency to the historical ones is essential for the tasks of system planning and security assessment, especially when the historical data is insufficient. In this paper, we described a generative model for load profiles of industrial and commercial customers, based on the conditional variational autoencoder (CVAE) neural network architecture, which is challenging due to the highly variable nature of such profiles. Generated contextual load profiles were conditioned on the month of the year and typical power exchange with the grid. Moreover, the quality of generations was both visually and statistically evaluated. The experimental results demonstrate our proposed CVAE model can capture temporal features of historical load profiles and generate `realistic' data with satisfying univariate distributions and multivariate dependencies.