Synthesis of realistic load data: adversarial networks for learning and generating residential load patterns

Xinyu Liang, Hao Wang

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


Responsible energy consumption plays a key role in reducing carbon footprint and CO2 emissions to tackle climate change. A better understanding of the residential consumption behavior using smart meter data is at the heart of the mission, which can inform residential demand flexibility, appliance scheduling, and home energy management. However, access to high-quality residential load data is still limited due to the cost-intensive data collection process and privacy concerns of data shar- ing. In this paper, we develop a Generative Adversarial Network (GAN)-based method to model the complex and diverse residential load patterns and generate synthetic yet realistic load data. We adopt a generation-focused weight selection method to select model weights to address the mode collapse problem and generate diverse load patterns. We evaluate our method using real-world data and demon- strate that it outperforms three representative state-of-the-art benchmark models in better preserving the sequence level temporal dependencies and aggregated level distributions of load patterns.
Original languageEnglish
Title of host publicationNeurIPS 2022 Workshop
Subtitle of host publicationTackling Climate Change with Machine Learning
EditorsPeetak Mitra, Maria João Sousa, Mark Roth, Ján Drgoňa, Emma Strubell, Yoshua Bengio
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Number of pages8
Publication statusPublished - 2022
EventTackling Climate Change with Machine Learning 2022 - Barcelona, Spain
Duration: 9 Dec 20229 Dec 2022


ConferenceTackling Climate Change with Machine Learning 2022
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