基于人工智能的混合太阳能能源系统与智能材料和适应式光伏用于可持续发电
Udit Mamodiya1, Indra Kishor2, Ramakrishna Garine3
1Faculty of Engineering and Technology, Poornima University, Sitapura, Jaipur, Rajasthan, 303905, India.
Scientific reports
|May 19, 2025
概括
这项研究介绍了一个人工智能增强的太阳能系统,具有改进的预测,自适应控制和分散交易. 新的框架显著提高了能源产量和效率,同时提高了系统的弹性和可持续性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 智能电网技术 智能电网技术
背景情况:
- 太阳能技术的进步需要智能化,适应动态条件的解决方案.
- 目前的系统往往缺乏综合预测,实时控制和高效的能源交易.
- 可扩展性和响应性是现代太阳能发电的关键挑战.
研究的目的:
- 开发一个新的AI增强的混合太阳能能源框架.
- 提高太阳能发电的效率,响应能力和可扩展性.
- 整合时空预测,自适应控制和分散的能源交易.
主要方法:
- 使用CNN-LSTM模型进行太阳辐射预测.
- 使用强化学习用于双轴跟踪和边缘人工智能进行控制.
- 嵌入式混合纳米涂料,相位变换材料和适应性矿-光伏电池.
- 实施了一个基于区块链的智能电网,用于点对点的能源交易.
主要成果:
- 实现了每年41.4%的能量产量增加和18.7%的光谱吸收效率.
- 将面板的平均温度降低了11.9°C.
- 将能量调度延迟降低到48ms,并将电池寿命提高了60%以上.
结论:
- 人工智能增强的框架显示了显著的性能改进和实时适应性.
- 该系统提供了通往智能,弹性和可持续太阳能的可行途径.
- 这项研究在优化太阳能系统方面迈出了转型性的步骤.
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