A cross-modal generative adversarial network for scenarios generation of renewable energy
Abstract
Scenarios data of renewable energy resources plays an essential role in the study of mitigating the risk in the power system due to their intermittent nature. Existing researches have mainly focused on how to generate high-quality time series based scenarios with corrupted or missing observed data. However, the multimodality of renewable energy resources is largely ignored from the perspective of the spatial-temporal correlation which can significantly strengthen the renewable energy prediction and optimization. To make full use of these multi-modal data and further improve the data quality of scenarios, a cross-modal Generative Adversarial Network (cGAN) model is proposed with the setting of two spatial-temporal transformer models. In cGAN, the task of scenarios generation is formulated as a probability approximation problem. Then, a cross-modal scenarios generation framework is constructed to process data from multi-modal observations. Theoretically, it can generate infinite number of uncertain scenarios data via the repeated random noise sampling technique. Extensive experiments are carried out at a NREL photovoltaic power dataset and a real-world wind power dataset, respectively. The results validate the state-of-the-art performance of cGAN, even though the validation dataset is missing at random. Moreover, the results also show that the setting of spatial-temporal transformer clearly explains the contributions of different modal data, and stabilize the training process of cGAN. Finally, a stochastic day-head economic dispatch is also studied to show the practical value of cGAN.
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GPT-3 scaled to 175 billion parameters and introduced in-context learning, allowing adaptation to new tasks with minimal fine-tuning.
“GPT-3 [194] further scaled up to 175 billion parameters and introduced incontext learning (ICL), allowing models to adapt to new tasks with minimal fine-tuning.”
From Multimodal fusion and vision–language models: A survey for robot vision (2026) · cites this paper as [194]
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Self-citation
- Authors
- Kang, M., Zhu, R., Chen, D., Li, C., Gu, W., Qian, X., & Yu, W.
- Affiliations
- Southeast University (CN)
- UNSW Sydney (AU)
- State Grid Jiangsu Electric Power (China) (CN)
- Purple Mountain Laboratories (CN)
- Venue
- IEEE Transactions on Power Systems
- Published
- 2024-03
- Volume
- 39
- Issue
- 2
- Pages
- 2630-2640
- ISSN
- 0885-8950 (ISSN-L)
- 1558-0679
- OpenAlex
- W4377086326
- Semantic Scholar
- d70ebe7cb6690f49d8483885afc88edc7b197673
- Cited by
- 90
- References
- 43
- Citations by year
- 2026: 36 · 2025: 36 · 2024: 16 · 2023: 2
- FWCI
- 6.66
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Power Systems (publishedVersion)
- Funders
- National Key R&D Program of China (2022ZD0120004)
- National Natural Science Foundation of China (62233004, 62273090, 62073076)
- Jiangsu Provincial Key Laboratory of Networked Collective Intelligence (BM2017002)
- Australian Research Council (DE210100274)
- Fundamental Research Funds for the Central Universities (2242023K40010)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Integrated Energy Systems Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Power System Optimization
- Keywords
- renewable energy scenarios, scenario generation, multimodal data, spatio-temporal transformer, photovoltaic power, wind energy, missing data, uncertainty quantification, day-ahead economic dispatch
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