智能城市光伏照明系统的优化能源管理,使用香水优化算法和图集神经网络的神经网络
Zakir Hussain1, Prabu Selvam2, M Sivaramkrishnan3
1Department of Chemical Technology, Loyola Academy, Secunderabad, 500015, Telangana, India.
Scientific reports
|November 17, 2025
概括
本研究介绍了智能城市光伏照明系统中能源管理的混合战略,将预测优化和神经网络集成在一起,以降低成本和提高效率. 新方法显著提高了运营成本效益和系统性能.
科学领域:
- 可再生能源系统可再生能源系统
- 智慧城市基础设施 智慧城市基础设施
- 能源管理 能源管理
背景情况:
- 智能城市越来越多地集成可再生能源 (RES),如光伏电池板和风力轮机 (WT) 用于照明.
- 由于供需波动,在这些系统中平衡成本和效率是具有挑战性的,需要有效的能源存储 (ESS) 和电网集成.
- 优化能源利用和减少对传统能源的依赖是可持续城市发展的关键目标.
研究的目的:
- 为智慧城市的光伏供电照明系统提出混合能源管理 (EM) 战略.
- 为了降低运营成本,提高这些系统的能源效率.
- 为了应对能源供应和需求与日益增长的可再生能源整合相匹配的挑战.
主要方法:
- 开发了一种混合战略,集成预测优化算法 (POA) 和生成神经网络 (GENN).
- POA优化了可再生能源,电网和ESS之间的能源分配,以实现资源利用和供需平衡.
- GENN提高了对能源生产和消费模式的预测准确度.
主要成果:
- 拟议的POA-GENN方法在MATLAB平台上与现有方法进行了实施和评估.
- 该系统实现了非常低的运营成本,为365.24欧元.
- 证明了99.2%的卓越能源效率,验证了该方法的有效性.
结论:
- 混合POA-GENN战略有效地优化了光伏驱动智慧城市照明中的能源管理.
- 这种方法显著提高了复杂的城市能源系统的成本效益和能源利用率.
- 调查结果强调了先进的新兴市场战略的潜力,以支持可持续的智慧城市发展.
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