# A Flow-Based Model for Conditional and Probabilistic Electricity Consumption Profile Generation and Prediction

arXiv: https://arxiv.org/abs/2405.02180 · HTML: https://mlsanity.com/paper/2405.02180/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 52 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 14 of 14)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [Normalizing Flows for Probabilistic Modeling and Inference](https://mlsanity.com/paper/1912.02762/) (2019)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Guided Image Generation with Conditional Invertible Neural Networks](https://mlsanity.com/paper/1907.02392/) (2019)
- [PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Design](https://mlsanity.com/paper/2106.03620/) (2021)
- [Scenario Forecasting of Residential Load Profiles](https://mlsanity.com/paper/1906.07373/) (2019)
- [MultiLoad-GAN: A GAN-Based Synthetic Load Group Generation Method Considering Spatial-Temporal Correlations](https://mlsanity.com/paper/2210.01167/) (2022)
- [Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning](https://mlsanity.com/paper/2305.08885/) (2023)
- [ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles](https://mlsanity.com/paper/2107.09523/) (2021)
- [Generating Multivariate Load States Using a Conditional Variational Autoencoder](https://mlsanity.com/paper/2110.11435/) (2021)
- [Detection of False Data Injection Attacks Using the Autoencoder Approach](https://mlsanity.com/paper/2003.02229/) (2020)
- [Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks](https://mlsanity.com/paper/2102.03360/) (2021)

## Built on by (most cited first, 3 of 3)

- [EnergyDiff: Universal Time-Series Energy Data Generation using Diffusion Models](https://mlsanity.com/paper/2407.13538/) (2024)
- [Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains](https://mlsanity.com/paper/2408.08399/) (2024)
- [SmartMeterFM: Unifying Smart Meter Data Generative Tasks Using Flow Matching Models](https://mlsanity.com/paper/2601.21706/) (2026)

## Abstract (arXiv)

Residential Load Profile (RLP) generation and prediction are critical for the operation and planning of distribution networks, especially as diverse low-carbon technologies (e.g., photovoltaic and electric vehicles) are increasingly adopted. This paper introduces a novel flow-based generative model, termed Full Convolutional Profile Flow (FCPFlow), which is uniquely designed for both conditional and unconditional RLP generation, and for probabilistic load forecasting. By introducing two new layers--the invertible linear layer and the invertible normalization layer--the proposed FCPFlow architecture shows three main advantages compared to traditional statistical and contemporary deep generative models: 1) it is well-suited for RLP generation under continuous conditions, such as varying weather and annual electricity consumption, 2) it demonstrates superior scalability in different datasets compared to traditional statistical models, and 3) it also demonstrates better modeling capabilities in capturing the complex correlation of RLPs compared with deep generative models.