转移学习用于巴西米纳斯吉拉斯州的太阳辐射预测
Samuel da Costa Alves Basílio1,2, Cicero Manoel Dos Santos3, Alfeu Dias Martinho4
1Department of Computing, Federal Center for Technological Education of Minas Gerais, Rua José Peres, 558, Leopoldina, 36700-000, MG, Brazil.
Scientific reports
|November 17, 2025
概括
这项研究开发了一种优化的计算模型,用于预测巴西米纳斯吉拉斯州的太阳辐射. 使用邻近城市数据的转移学习被证明是有效的预测太阳能潜力,即使没有当地气象站.
科学领域:
- 可再生能源系统可再生能源系统
- 计算机建模 计算建模
- 气象学 天气学
背景情况:
- 巴西第三大州米纳斯吉拉斯州 (Minas Gerais) 在光伏能源,特别是分布式发电方面进行了大量投资.
- 现有的太阳辐射预测模型往往是经验性的,区域特定的,或需要本地化数据,限制了它们在全州的适用性.
- 需要一个高效的计算模型来预测整个州的太阳辐射,以便有效地规划光伏能源.
研究的目的:
- 开发和介绍一个优化的计算模型,用于预测整个米纳斯吉拉斯州的太阳辐射.
- 评估地理位置数据对预测准确性的影响.
- 确定在缺乏气象站的地区预测太阳辐射的可行性.
主要方法:
- 利用了20年的太阳辐射数据,来自米纳斯吉拉斯州67个气象站.
- 开发并优化了用于全州太阳辐射预测的计算模型.
- 研究了使用邻近地点数据转移学习的有效性.
主要成果:
- 经过优化后的模型能够有效地预测整个米纳斯吉拉斯州的太阳辐射.
- 地理定位数据并没有显著提高预测准确度.
- 使用邻近城市数据的转移学习产生了与使用本地站数据的模型可比的结果,使得在没有站的地区可以进行预测.
结论:
- 开发的计算模型提供了一个有效的解决方案,用于在米纳斯吉拉斯州全州范围内的太阳辐射预测.
- 转移学习是预测太阳辐射在数据稀缺地区的可行策略.
- 该方法可适应各种机器学习模型和地理位置.
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