风力发电厂能源潜力的概率评估,通过决策树中的配合深度学习方法
Kübra Nur Şahi N1, Muhammed Sutcu2
1Abdullah Gül University, Faculty of Engineering, Industrial Engineering, Department, Kayseri, Turkiye.
Heliyon
|April 8, 2024
概括
本研究引入了Copula-Deep Learning框架,通过分析气象数据来改进风能评估. 该方法提高了预测风力发电潜力的准确性和可靠性,用于可持续能源解决方案.
科学领域:
- 可再生能源系统可再生能源系统
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 对可持续能源解决方案的日益增长的需求需要在可再生能源,特别是风力发电方面取得进展.
- 风能的变化和间歇性质需要复杂的评估方法来有效地利用和选择地点.
- 精确的风能潜力评估对于明智的决策,财务风险评估和能源部门的战略规划至关重要.
研究的目的:
- 引入一个创新的框架,以提高风力发电潜力的评估.
- 利用复杂的,使用copula和深度学习在气象变量之间的非线性依赖.
- 提高风能场地评估的准确性和可靠性.
主要方法:
- 开发了一个框架,将Copula-Deep Learning技术集成到决策树上.
- 2021年利用土耳其风力发电厂的每小时发电量和气象数据.
- 采用基于Copula-LSTM的决策树方法来建模复杂变量相互依赖.
主要成果:
- 拟议的框架大大提高了风电潜力评估的准确性和可靠性.
- 通过先进的建模,证明了处理非独立气象变量的有效性.
- 该研究提供了一种可用于现实世界风能数据场景的可靠方法.
结论:
- 配方和深度学习的融合为分析复杂的风能气象数据提供了一种强大的方法.
- 准确的风能资源评估对于实现全球可再生能源目标和可持续能源未来至关重要.
- 这项研究为利益相关者和政策制定者提供了优化风力发电发展的宝贵见解.
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