一个基于技术经济和人工智能的优化框架,用于供应农村电信基站的混合能源系统
Aruna Rajendran1, Raja J2, Moorthi K3
1Department of Electronics and Communication Engineering, Adhiparasakthi Engineering College, Melmaruvathur, Tamilnadu, India. arunarajendran41@gmail.com.
Scientific reports
|March 9, 2026
概括
本研究介绍了用于为远程电信塔供电的混合可再生能源系统 (HRES) 的人工智能框架,大大减少了化石燃料的使用. 智能预测和最佳调度提高了效率和可持续性.
科学领域:
- 可再生能源系统可再生能源系统
- 能源管理中的人工智能
- 电信基础设施 电信基础设施
背景情况:
- 远程基地收发电站 (BTS) 通常依赖于传统的,燃料密集型的电源.
- 整合混合可再生能源系统 (HRES) 为分散式电力带来了技术和经济挑战.
- 优化能源管理对于远程电信基础设施的可靠性和可持续性至关重要.
研究的目的:
- 引入基于人工智能的严格框架,用于分析远程BTS技术和经济方面的HRES.
- 评估HRES中各种能源管理预测算法的性能.
- 展示智能能源管理的潜力,以减少对化石燃料的依赖,提高系统效率.
主要方法:
- 开发基于人工智能的HRES分析框架.
- 使用一年的每小时模拟数据来训练和验证预测算法 (线性回归,决策树,SVM,GPR,KARMA,NN).
- 能源管理系统 (EMS) 模拟以评估负载服务能力和燃料消耗.
主要成果:
- 拟议的HRES系统在-48V和23A下提供1.2kW,满足电信负载要求.
- 混合太阳能和风能平均占每日服务总负载的78.6%.
- 与传统系统相比,基于燃料的系统使用量减少了76%以上.
结论:
- 智能预测和最佳的调度策略显著提高了HRES的效率.
- 由人工智能驱动的框架有效地减少了分散的电信塔的化石燃料依赖.
- 该研究证实了HRES通过先进的能源管理来提高远程BTS应用的可持续性.
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