基于长短期记忆人工神经网络的需求响应业务传输优化

Optimization of Demand Response Service Transmission Based on Long and Short-Term Memory Artificial Neural Network

随着智能电网的快速发展,需求响应通信业务越来越注重高可靠性的信息交互。为了提升需求响应信息交互过程中的信道质量,文章设计了一种基于长短期记忆(long-short-term memory,LSTM)人工神经网络的需求响应业务传输优化算法。首先基于需求响应信息交互通信服务类型,确定不同的通信服务对传输质量的要求。然后根据长短期记忆人工神经网络预测信噪比,并引入带有反馈机制的自适应编码技术,将不稳定的无线时变衰落信道转变成稳定信道,从而提高需求响应通信传输质量。所提出的算法对需求响应通信业务的优化具有实际工程意义,仿真实例验证了所提算法的有效性。

With the rapid development of smart grid, demand response communication services pay more and more attention to highly reliable information interaction. In order to improve the channel quality in the process of demand response information interaction, an optimization algorithm for demand response service transmission based on long short-term memory(LSTM) artificial neural network is designed. Firstly, the requirements of different communication services on transmission quality are determined based on the types of communication services for demand response information interaction. The signal to noise ratio is predicted based on long and short-term memory artificial neural network, and adaptive coding technology with feedback mechanism is introduced to transform the unstable wireless time-varying fading channel into stable channel, which can improve the transmission quality of demand response communication. The proposed algorithm has practical engineering significance for the optimization of demand response communication services, and the effectiveness of the proposed algorithm is verified by a simulation example.