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Hang Wei

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Proceedings Article | 5 June 2024 Paper

Construction of a real-time analysis model for abnormal electrical energy data based on demand-side dynamic response

Jie Li, Yiming Wu, Yong He, Hang Wei

Proceedings Volume 13163, 131634M (2024) https://doi.org/10.1117/12.3030142

KEYWORDS: Power consumption, Data modeling, Machine learning, Data conversion, Random forests, Industry, Data processing, Reflection, Mathematical optimization, Fourier transforms

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During the “14th Five-Year Plan” period, the new energy industry is expected to experience high-quality and leapfrog development, aiming for high quality and significant progress. A significant challenge lies in building a new power system that revolves around new energy sources. To effectively address the issues arising from the rapid growth of new energy sources and the sustained widening gap between load peak-valley differences, it is crucial to support the real-time dynamic response of electricity consumption on the demand side. This paper utilizes techniques such as machine learning to analyze issues related to abnormal electricity consumption. Through real-time intelligent detection methods for abnormal electricity consumption data, it enhances the accuracy of electricity consumption data during dynamic response and constructs an abnormal data fitting model. This model significantly enhances the real-time processing capability of electricity consumption data and effectively improves its support capability during demand-side dynamic response, enabling it to address evolving and changing business requirements.

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