Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method

TL;DR

This paper introduces ERGAN, an ensemble recurrent GAN framework that generates high-quality synthetic residential load data, addressing privacy and data collection challenges while outperforming existing benchmarks in realism and diversity.

Contribution

The paper presents a novel ERGAN framework combining ensemble recurrent GANs with a specialized loss function for improved synthetic load data generation.

Findings

ERGAN outperforms benchmarks in diversity and similarity metrics.

Synthetic data generated by ERGAN closely matches real load patterns.

The approach enhances privacy and data availability for power system applications.

Abstract

Generating synthetic residential load data that can accurately represent actual electricity consumption patterns is crucial for effective power system planning and operation. The necessity for synthetic data is underscored by the inherent challenges associated with using real-world load data, such as privacy considerations and logistical complexities in large-scale data collection. In this work, we tackle the above-mentioned challenges by developing the Ensemble Recurrent Generative Adversarial Network (ERGAN) framework to generate high-fidelity synthetic residential load data. ERGAN leverages an ensemble of recurrent Generative Adversarial Networks, augmented by a loss function that concurrently takes into account adversarial loss and differences between statistical properties. Our developed ERGAN can capture diverse load patterns across various households, thereby enhancing the realism and diversity of the synthetic data generated. Comprehensive evaluations demonstrate that our method consistently outperforms established benchmarks in the synthetic generation of residential load data across various performance metrics including diversity, similarity, and statistical measures. The findings confirm the potential of ERGAN as an effective tool for energy applications requiring synthetic yet realistic load data. We also make the generated synthetic residential load patterns publicly available.