Synthetic Time-Series Load Data via Conditional Generative Adversarial Networks
Andrea Pinceti, Lalitha Sankar, Oliver Kosut
Abstract
A framework for the generation of synthetic time-series transmission-level load data is presented. Conditional generative adversarial networks are used to learn the patterns of a real dataset of hourly-sampled week-long load profiles and generate unique synthetic profiles on demand, based on the season and type of load required. Extensive testing of the generative model is performed to verify that the synthetic data fully captures the characteristics of real loads and that it can be used for downstream power system and/or machine learning applications.
Topics & Concepts
Computer scienceGenerative grammarAdversarial systemSynthetic dataSeries (stratigraphy)Time seriesMachine learningArtificial intelligenceGenerative modelGenerative adversarial networkData miningDeep learningPaleontologyBiologyEnergy Load and Power ForecastingGenerative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods