分析变量以确定它们对可再生能源预测的影响,使用组合方法
Carlos M Travieso-González1,2, Sergio Celada-Bernal1, Alejandro Lomoschitz3
1Institute for Technological Development and Innovation in Communications, IDeTIC, University of Las Palmas de Gran Caanria, ULPGC, Las Palmas de Gran Canaria, E35017, Spain.
Heliyon
|May 22, 2024
概括
准确的太阳能预测对于高效的可再生能源管理至关重要. 这项研究表明,使用短时间间隔是最佳预测的关键,无论使用什么模型或集合方法.
科学领域:
- 可再生能源系统可再生能源系统
- 计算智能是一种计算智能.
- 数据科学数据科学数据科学
背景情况:
- 有效的能源预测对于管理可再生能源和确保电网稳定至关重要.
- 选择最佳的太阳能预测系统是复杂的,因为能源基础设施的变化.
- 集成方法提供了一种强大的方法,可以在可变条件下提高预测准确性.
研究的目的:
- 调查采样频率,神经网络架构和集成方法深度对太阳能预测的影响.
- 为不同地理位置和能源基础设施确定最有效的太阳能预测模型.
- 为选择最佳系统提供一个框架,以便准确和高效地预测太阳能.
主要方法:
- 利用集体方法在多个地点进行太阳能预测.
- 分析了太阳能电池板系统不同采样频率的影响.
- 评估了不同的神经网络架构和每个模型的集合块数量.
主要成果:
- 确定了特定位置的最佳太阳能预测模型.
- 证明使用短时间间隔是准确预测的关键因素.
- 发现短时间间隔的有效性与预测模型类型和整体方法无关.
结论:
- 短时间间隔对于准确的太阳能预测至关重要,无论选择的预测模型或组合技术如何.
- 该研究为选择最适合特定基础设施需求的最佳太阳能预测系统提供了一个框架.
- 优化预测可以提高太阳能资源的有效管理.
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