在不确定性下,利用人工神经网络的积极学习,对能源中心进行强有力的技术经济优化
Aya M A Heikal1,2, Shady H E Abdel Aleem3, Ragab A El-Sehiemy4,5
1Electrical Power & Machines Department, Faculty of Engineering, Ain Shams University, Cairo, 11517, Egypt.
Scientific reports
|July 27, 2025
概括
本研究介绍了使用人工神经网络 (ANN) 和主动学习 (AL) 的能源中心 (EHs) 的优化框架. 该方法显著降低了运营成本和能源供应风险,提高了效率和可持续性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能在能源中的作用
- 运营研究 运营研究
背景情况:
- 能源中心 (EHs) 提供综合的多能源解决方案,但在技术经济优化和供需不确定性方面面临挑战.
- 在EH中平衡运营效率,成本效益和弹性,需要先进的管理策略.
- 波动的能源需求和间歇性的可再生能源使可靠的EH运作复杂化.
研究的目的:
- 开发一个多目标优化框架,以在不确定性下对能源中心运营进行优化.
- 通过人工神经网络 (ANN) 和主动学习 (AL) 来增强EH调度和规划能力.
- 提高技术经济性能,尽量减少损失,成本和排放,提高系统可靠性.
主要方法:
- 实施能源枢纽运营的多目标优化框架.
- 利用人工神经网络 (ANN) 与主动学习 (AL) 结合起来,用于动态模型增强.
- 集成预测能力,以管理在不确定性下波动的能源需求和系统约束.
主要成果:
- 运营成本下降57.9%,能源供应损失概率 (LESP) 降至0.010682.2.
- 在系统可靠性,成本效益和操作灵活性方面取得了显著的改进.
- 在能源损失,成本和排放方面实现了80.3%的降低,每日输出13687.8千瓦.
结论:
- 基于ANN的积极学习有效地优化了能源中心管理,以实现可持续和弹性运营.
- 拟议的框架成功地平衡了能源效率,系统灵活性和成本效益在不确定性中.
- 该方法表现出强大的预测能力,适应动态负载和间歇性的可再生能源供应.
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