# Generating Multivariate Load States Using a Conditional Variational Autoencoder

arXiv: https://arxiv.org/abs/2110.11435 · HTML: https://mlsanity.com/paper/2110.11435/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 35 references that are in the map.
- First posted on arXiv 21 Oct 2021.

## Builds on (most cited first, 13 of 13)

- [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)
- [Tutorial on Variational Autoencoders](https://mlsanity.com/paper/1606.05908/) (2016)
- [Understanding disentangling in β-VAE](https://mlsanity.com/paper/1804.03599/) (2018)
- [Spherical Latent Spaces for Stable Variational Autoencoders](https://mlsanity.com/paper/1808.10805/) (2018)
- [Simple and Effective VAE Training with Calibrated Decoders](https://mlsanity.com/paper/2006.13202/) (2020)
- [Dirichlet Variational Autoencoder](https://mlsanity.com/paper/1901.02739/) (2019)
- [A Tutorial on VAEs: From Bayes' Rule to Lossless Compression](https://mlsanity.com/paper/2006.10273/) (2020)
- [Balancing Reconstruction Quality and Regularisation in ELBO for VAEs](https://mlsanity.com/paper/1909.03765/) (2019)
- [Detection of False Data Injection Attacks Using the Autoencoder Approach](https://mlsanity.com/paper/2003.02229/) (2020)
- [Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders](https://mlsanity.com/paper/2006.07115/) (2020)
- [Assessing Energy Storage Requirements Based on Accepted Risks](https://mlsanity.com/paper/2003.00123/) (2020)

## Built on by (most cited first, 8 of 8)

- [Generating Contextual Load Profiles Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2209.04056/) (2022)
- [A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction](https://mlsanity.com/paper/2405.02180/) (2024)
- [Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets](https://mlsanity.com/paper/2506.11281/) (2025)
- [Targeted Analysis of High-Risk States Using an Oriented Variational Autoencoder](https://mlsanity.com/paper/2303.11410/) (2023)
- [Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective](https://mlsanity.com/paper/2310.03556/) (2023)
- [Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2504.17210/) (2025)
- [Machine-Learning-Assisted Photonic Device Development: A Multiscale Approach from Theory to Characterization](https://mlsanity.com/paper/2506.20056/) (2025)
- [SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models](https://mlsanity.com/paper/2601.21706/) (2026)

## Abstract (arXiv)

For planning of power systems and for the calibration of operational tools, it is essential to analyse system performance in a large range of representative scenarios. When the available historical data is limited, generative models are a promising solution, but modelling high-dimensional dependencies is challenging. In this paper, a multivariate load state generating model on the basis of a conditional variational autoencoder (CVAE) neural network is proposed. Going beyond common CVAE implementations, the model includes stochastic variation of output samples under given latent vectors and co-optimizes the parameters for this output variability. It is shown that this improves statistical properties of the generated data. The quality of generated multivariate loads is evaluated using univariate and multivariate performance metrics. A generation adequacy case study on the European network is used to illustrate model's ability to generate realistic tail distributions. The experiments demonstrate that the proposed generator outperforms other data generating mechanisms.