# Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders

arXiv: https://arxiv.org/abs/2006.07115 · HTML: https://mlsanity.com/paper/2006.07115/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2020.

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

- [Generating Multivariate Load States Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2110.11435/) (2021)
- [Functional Model of Residential Consumption Elasticity under Dynamic Tariffs](https://mlsanity.com/paper/2111.11875/) (2021)
- [Coherent Load Profile Synthesis with Conditional Diffusion for LV Distribution Network Scenario Generation](https://mlsanity.com/paper/2510.12832/) (2025)
- [Towards Overcoming Data Scarcity in Nuclear Energy: A Study on Critical Heat Flux with Physics-consistent Conditional Diffusion Model](https://mlsanity.com/paper/2511.16207/) (2025)

## Abstract (arXiv)

The implementation of efficient demand response (DR) programs for household electricity consumption would benefit from data-driven methods capable of simulating the impact of different tariffs schemes. This paper proposes a novel method based on conditional variational autoencoders (CVAE) to generate, from an electricity tariff profile combined with exogenous weather and calendar variables, daily consumption profiles of consumers segmented in different clusters. First, a large set of consumers is gathered into clusters according to their consumption behavior and price-responsiveness. The clustering method is based on a causality model that measures the effect of a specific tariff on the consumption level. Then, daily electrical energy consumption profiles are generated for each cluster with CVAE. This non-parametric approach is compared to a semi-parametric data generator based on generalized additive models and that uses prior knowledge of energy consumption. Experiments in a publicly available data set show that, the proposed method presents comparable performance to the semi-parametric one when it comes to generating the average value of the original data. The main contribution from this new method is the capacity to reproduce rebound and side effects in the generated consumption profiles. Indeed, the application of a special electricity tariff over a time window may also affect consumption outside this time window. Another contribution is that the clustering approach segments consumers according to their daily consumption profile and elasticity to tariff changes. These two results combined are very relevant for an ex-ante testing of future DR policies by system operators, retailers and energy regulators.