一项关于帮助可再生能源发电的先进机器学习和深度学习技术的调查
1School of Electrical Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, 600 127, India. srirevathi.b@vit.ac.in.
Environmental science and pollution research international
|August 8, 2023
概括
准确的可再生能源预测对于电网稳定性和成本降低至关重要. 本研究回顾了用于预测太阳辐射和可再生能源产量的机器学习和深度学习方法.
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
背景情况:
- 随着可再生能源的日益集成,需要对电网管理进行准确的预测.
- 可靠的能源生产预测对于电网稳定性,成本效益和能源市场风险减轻至关重要.
研究的目的:
- 为太阳辐射预测提供机器学习 (ML) 和深度学习 (DL) 技术的全面审查.
- 分析现有的ML / DL方法和可再生能源预测的metaheuristic优化.
主要方法:
- 对用于可再生能源预测的ML和DL算法的审查和分析.
- 在可再生能源预测的背景下检查元启发式优化技术.
主要成果:
- 该研究对太阳能和可再生能源预测的各种ML/DL方法进行了分类和分析.
- 确定了可再生能源预测中的当前挑战和未解决的问题.
结论:
- 机器学习和深度学习为提高可再生能源预测的准确性提供了强大的工具.
- 进一步研究先进的ML / DL模型和解决未解决的挑战对于优化可再生能源整合至关重要.
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