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At present, massive distributed resources on the user side are connected to the network, with various types of agents and flexible access forms. Different demand response scenarios have different service communication requirements. This paper proposes a demand response adaptive coding strategy considering channel environment and DR Service characteristics. The SNR of channel is accurately predicted by GRU model, and the pre-coding strategy is switched adaptively based on channel environment changes. According to the different service requirements for information interaction in different DR Periods, the design of an adaptive coding strategy considering service levels can effectively eliminate the interference between different users in the channel, solve the problem that traditional algorithms cannot automatically adapt to changes in the channel environment, and match the best pre-coding strategy for different interactive services in different demand response periods.

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Adaptive Aggregation Strategy of Heavy Overload Distribution Station by Using Machine Learning Algorithm

  • 会议论文

  • 发表日期：

    2025年

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  • Bin Li,
  • Huifang Guo,
  • Yi Sun,
  • Bing Qi

摘要

At present, massive distributed resources on the user side are connected to the network, with various types of agents and flexible access forms. Different demand response scenarios have different service communication requirements. This paper proposes a demand response adaptive coding strategy considering channel environment and DR Service characteristics. The SNR of channel is accurately predicted by GRU model, and the pre-coding strategy is switched adaptively based on channel environment changes. According to the different service requirements for information interaction in different DR Periods, the design of an adaptive coding strategy considering service levels can effectively eliminate the interference between different users in the channel, solve the problem that traditional algorithms cannot automatically adapt to changes in the channel environment, and match the best pre-coding strategy for different interactive services in different demand response periods.