使用人工神经网络和分数加权模型优化负载调度
Prabakaran Raghavendran1, Saikat Gochhait2, Tharmalingam Gunasekar3
1Department of Mathematics, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology; Symbiosis Institute of Digital and Telecom Management, Constituent of Symbiosis International Deemed University.
Journal of visualized experiments : JoVE
|October 27, 2025
概括
本研究介绍了一种混合人工智能 (AI) 和分量加权负载调度 (FWLD) 协议,以优化印度的实时电力负载数据. 这种新方法提高了能源分配效率和电网稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 准确的实时负载数据对于高效的电力系统运行至关重要.
- 传统的货物调度方法与供应,传输,需求,成本和时间 (S,T,D,C,T) 的动态波动作斗争.
- 将内存和非局部性纳入负载预测模型对于提高准确性至关重要.
研究的目的:
- 开发和验证一种混合协议,以优化来自印度区域负载调度中心的实时负载数据.
- 通过使用新的人工神经网络 (ANN) 和分量加权负载调度 (FWLD) 方法,提高能源分配效率和电网稳定性.
- 为了提高电力系统优化的可预测性和可扩展性.
主要方法:
- 开发了一种混合协议,将人工神经网络 (ANN) 学习和分数加权负载调度 (FWLD) 结合起来.
- 使用微积分计算将α记忆和时间非局部性纳入负载调度计算中.
- 使用S,T,D,C,T (供应,传输,需求,成本和时间) 模型来跟踪实时波动,以优化能源分配.
主要成果:
- 与传统方法相比,混合ANN-FWLD方法在负载分配和灵活性方面表现出更高的性能.
- 实验研究报告测试期间的平均平方误差 (MSE) 为260.95 MW2,根平均平方误差 (RMSE) 为15.68 MW.
- 平均绝对百分比误差 (MAPE) 记录在10.35%以下,表明可预测性提高.
结论:
- 混合ANN-FWLD协议为电力系统优化提供了更好的稳定性,效率和可扩展性.
- 该方法通过跟踪实时S,T,D,C,T波动,有效地优化能量分配.
- 这种方法为管理电网振荡和提高电力系统整体性能提供了更强大的解决方案.
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