基于优化的负载预测和智能建筑微电网的需求管理,使用Greylag Goose和Bi级图形模型
B Shamreen Ahamed1, D Dhanya2, M Sivaramkrishnan3
1Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, 600119, India.
Scientific reports
|January 27, 2026
概括
本研究引入了智能建筑微电网的新框架,使用先进的人工智能和优化技术将能源负载预测准确度提高到98.3%,以提高可靠性和电池管理.
科学领域:
- 智能电网技术 智能电网技术
- 能源系统中的人工智能
- 整合可再生能源的整合
背景情况:
- 智能建筑微电网 (SBMGs) 面临能源管理方面的挑战,原因是负载预测不准确,供需不匹配和电池退化.
- 现有的预测和优化方法往往无法在动态的SBMG环境中确保系统可靠性和电池资源耐用性.
研究的目的:
- 为SBMGs提出一个新的领域适应的预测和优化框架.
- 提高负载预测和需求响应管理的准确性.
- 为了提高SBMGs的可靠性和电池耐用性.
主要方法:
- 这是一个混合框架,它结合了关系双级聚合图卷曲网络 (RBAGCN) 和灰色优化 (GGO).
- RBAGCN被重新设计,以整合能源变量的物理和操作相互关系.
- 采用GGO来稳定非静态负载的网络重量趋同.
- 数据预处理包括快速重新采样代过 (FRIF) 和草原狗优化 (PDO) 进行特征选择.
主要成果:
- 拟议的框架实现了98.3%的平均预测准确度,明显优于基准模型 (例如,RNN为82.6%).
- 证明较低的误差指标:平均绝对误差 (MAE) 为0.0164,平均绝对百分比误差 (MAPE) 为0.0128,平均平方误差 (MSE) 为0.0069.
- 降低了26.5%的预测方差和17.8%的收代,表明统计稳定性和学习效率提高.
结论:
- 集成的RBAGCN和GGO框架为SBMG中准确的负载预测和需求管理提供了强大的解决方案.
- 该方法提高了系统可靠性,并通过优化能源利用来延长电池资源寿命.
- 这种方法代表了智能建筑智能能源管理的重大进步.
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