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13 October 2022 Research and application of integrated power system based on deep learning
Yuanjie Xu, Jingbo Sun, Hao Yuan
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Proceedings Volume 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022); 122871P (2022) https://doi.org/10.1117/12.2640740
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 2022, Wuhan, China
Abstract
As global electricity consumption continues to grow, carbon-neutral strategies have been proposed by various countries. The integrated power system is an important tool and development direction to assess the power situation in the future. However, most countries today focus on the traditional single-indicator power system, failing to consider the coupling relationship between different indicators in the integrated power system. Considering the disadvantages of traditional big data downscaling with overcrowding among clusters, this paper will use the unified flow approximation and projection (UMAP) method, while considering the coupling relationship among multiple indicators, to build an atomic search optimization method based on chaos algorithm to optimize the kernel function, which can improve the prediction accuracy by more than 50% compared with the traditional BP neural network. Finally, a multivariate load short-term forecasting system for integrated power system management platform is built based on the current popular technical architecture, and a real-time forecasting system with front and back-end separation is constructed based on SpringCloud microservice.
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Yuanjie Xu, Jingbo Sun, and Hao Yuan "Research and application of integrated power system based on deep learning", Proc. SPIE 12287, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2022), 122871P (13 October 2022); https://doi.org/10.1117/12.2640740
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KEYWORDS
System integration
Optimization (mathematics)
Chaos
Data modeling
Neural networks
Visualization
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