人工网络如何增强二阶混合扩展卡尔曼滤波用于能量管理？

IF 5.1 3区工程技术 Q2 ENERGY & FUELS

Energy Reports Pub Date : 2025-07-25 DOI:10.1016/j.egyr.2025.07.014

Jianyong Yu , Ahmed Kateb Jumaah Al-Nussairi , Mustafa Habeeb Chyad , Narinderjit Singh Sawaran Singh , Hossein Azarinfar , Luma Sabah Munshid , Yuzhen Liu , Wenti Huang

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引用次数: 0

摘要

准确的状态估计对有效的能量管理至关重要。本文提出了一种将二阶混合扩展卡尔曼滤波（SO-HEKF）与人工神经网络相结合的方法，以提高动态能量环境下的估计精度。仿真结果表明，与标准SO-HEKF相比，该算法的估计精度提高了16.7 %，运行成本降低了12.4 %。自适应学习机制可以在不同的网格条件下进行实时调整。可再生能源整合、负荷预测和需求响应场景的案例研究证实了该方法在改善资源分配、电网稳定性和鲁棒性方面的有效性。这种综合方法支持更可靠和智能的能源系统。

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How can artificial networks enhance second-order hybrid extended kalman filtering for energy management?

Accurate state estimation is vital for effective energy management. This study introduces an integrated approach combining Second-Order Hybrid Extended Kalman Filtering (SO-HEKF) with artificial neural networks to enhance estimation accuracy in dynamic energy environments. Simulation results indicate up to 16.7 % improvement in estimation accuracy and a 12.4 % reduction in operational cost compared to standard SO-HEKF. The adaptive learning mechanism enables real-time adjustments under varying grid conditions. Case studies across renewable integration, load forecasting, and demand response scenarios confirm the method’s effectiveness in improving resource allocation, grid stability, and robustness. This integrated approach supports more reliable and intelligent energy systems.

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来源期刊

Energy Reports Energy-General Energy

CiteScore

8.20

自引率

13.50%

发文量

2608

审稿时长

38 days

期刊介绍： Energy Reports is a new online multidisciplinary open access journal which focuses on publishing new research in the area of Energy with a rapid review and publication time. Energy Reports will be open to direct submissions and also to submissions from other Elsevier Energy journals, whose Editors have determined that Energy Reports would be a better fit.