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Intelligent Algorithms and Big Data Technology in Power Transmission Systems: Efficient, Reliable, and Sustainable Energy Transmission

Published: 31 July 2024 Publication History

Abstract

"West to East Power Transmission" refers to the conversion of energy from water power resources and coal resources abundant western area and regions into electricity resources, and transmission to the eastern coastal areas where electricity is scarce. How to ensure security and stability supply of electricity demand within and outside the province while improving the overall cleanliness of electricity is the biggest problem faced by central and western provinces in the medium to long-term power development planning. This article analyzes historical and real-time power transmission data using big data (BD) technology, selects a model based on long short term memory (LSTM) algorithm for medium to long term prediction of power demand, and predicts future load demand in advance. It adjusts the operation plan of the generator set according to demand to achieve efficient energy transmission. This article compares and analyzes several flexible renewable new energy sources. The optimal renewable energy generation planning and coordination can be determined to ensure the full utilization and maximum efficiency of renewable energy. The experimental results show that the model can achieve an accuracy of 96% in predicting the total electricity demand and 95% in predicting the proportion of energy consumption. The algorithm model has strong robustness. This article proposes a predictive model for electricity compliance, selects the optimal renewable resources combined with coal power resources for clean transformation of electricity, and achieves efficient, reliable, and sustainable green energy development.

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  1. Intelligent Algorithms and Big Data Technology in Power Transmission Systems: Efficient, Reliable, and Sustainable Energy Transmission

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    PEAI '24: Proceedings of the 2024 International Conference on Power Electronics and Artificial Intelligence
    January 2024
    969 pages
    ISBN:9798400716638
    DOI:10.1145/3674225
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 31 July 2024

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