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使用机器学习和气候变化预测进行太阳能潜力评估,用于长期能源规划
B Nishant Sree Reddy1, Kumar Gautam2, Nikhil Pachauri3
1Department of Mechatronics, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, 576104, Karnataka, India.
Scientific reports
|November 14, 2025
概括
一种新方法利用地缘空间,气象和基础设施数据预测太阳能潜力. 一个XGBoost模型预测全球太阳能容量,优于其他机器学习算法,用于高效的太阳能发电系统开发.
科学领域:
- 可再生能源系统可再生能源系统
- 地理空间分析是什么
- 气象预报 气象预报
背景情况:
- 准确的太阳能潜力评估对于太阳能发电系统的开发,安装和运行至关重要.
- 现有的方法可能缺乏对综合性场地评估所需的精度和全球范围.
- 整合多种数据源是改善太阳能潜力预测的关键.
研究的目的:
- 提出一种用于评估太阳能潜力的新方法.
- 开发一个用于评估太阳能容量的全球应用程序.
- 提高太阳能站点评估的准确性和效率.
主要方法:
- 利用综合的地理空间,气象和基础设施多维数据.
- 在历史太阳辐射和气象数据 (1980-2015) 上训练了一个 XGBoost 机器学习模型.
- 使用模拟天气数据 (2015-2099) 验证了该模型,并在菲律宾,蒙古和希腊进行了案例研究.
主要成果:
- XGBoost模型在处理复杂的非线性相互作用和时间天气模式方面表现出卓越的性能.
- 实现了低误差指标:根平均平方误差 (RMSE) = 0.97 kWh/m2和平均绝对误差 (MAE) = 0.76 kWh/m2.
- 开发的应用程序在几分钟内提供精确的太阳能估计和财务可行性评估.
结论:
- 拟议的方法有效地预测全球太阳能潜力.
- 对于长期太阳能规划和场地评估,XGBoost模型非常有效.
- 该应用程序可以在全球范围内进行快速,轻松和准确的网站评估.
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