Scenario Generation by Physical Model-Free Approaches for Multiple Renewables

Hao Chen*, Ailing Jin*, Wei Zhao, Haoran Yi, Qixia Zhang

*Corresponding author for this work

Research output: Contribution to journalConference articleAcademicpeer-review

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Abstract

The augmentation of renewable energy sources within the global energy portfolio is imperative for mitigating the impacts of climate change. Nonetheless, the inherent variability, intermittency, and unpredictability associated with certain forms of renewable energy present significant challenges. Effective integration of these energy sources into existing grids is contingent upon accurate predictions and robust scenario planning. To address this, we introduce a novel data-driven framework that facilitates the generation of energy scenarios without relying on intricate physical models or extensive assumptions. This framework is underpinned by an innovative combination of a grey neural network, which is fine-tuned using a genetic algorithm, and a Gaussian Copula to enhance the prediction accuracy. Extensive experimental analyses validate the effectiveness and advanced capabilities of our proposed model. Moreover, the adaptable nature of this data-driven approach allows for its potential application across various sectors within the sustainable industry, further underscoring its versatility and utility.

Original languageEnglish
Article number012003
JournalJournal of physics: Conference series
Volume2754
Issue number1
DOIs
Publication statusPublished - 2024
Event18th International Conference on Technologies and Materials for Renewable Energy, Environment and Sustainability, TMREES 2023 - Hybrid, Athens, Greece
Duration: 27 Nov 202329 Nov 2023
Conference number: 18

Keywords

  • Data-driven modelling
  • Machine learning
  • Model-free method
  • Renewable energy
  • Scenario generation

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