一小时前的光伏电力预测结合了BiLSTM和贝叶斯优化算法,并进行了间隔预测的引导重新抽样
Reinier Herrera-Casanova1, Arturo Conde1, Carlos Santos-Pérez2
1Faculty of Mechanical and Electrical Engineering, Autonomous University of Nuevo León, San Nicolás de los Garza 66455, Mexico.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究介绍了一种双向长期短期记忆 (BiLSTM) 深度学习模型,用于提前一小时的光伏电力预测. 与传统方法相比,BiLSTM模型显著提高了预测准确度.
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
- 对于电网的机器学习
背景情况:
- 准确的光伏 (PV) 功率预测对于将可再生能源整合到电网中至关重要.
- 现有的预测模型在不同的条件下面临着准确性和稳定性的挑战.
- 深度学习为增强光伏功率预测提供了潜力.
研究的目的:
- 开发和评估一种新的深度学习模型,以准确预测一小时前的光伏电力.
- 通过超参数调整和强大的数据预处理来优化模型的性能.
- 将拟议模型的有效性与既有预测技术进行比较.
主要方法:
- 实施双向长期短期记忆 (BiLSTM) 深度学习模型.
- 应用一个强大的数据预处理算法用于历史的光伏数据.
- 协同组合BiLSTM与贝叶斯优化算法 (BOA) 进行超参数调整.
- 使用引导重新抽样来进行间隔预测.
主要成果:
- 拟议的BiLSTM-BOA模型显示出卓越的决定性预测性能.
- 实现了正常化平均绝对误差 (nMAE) 的显著降低:75.03%与MLP相比,77.01%与RF相比.
- 间隔预测提供了可靠和灵活的预测,根据所需的信心水平进行调整.
结论:
- BiLSTM-BOA模型为短期光伏电力预测提供了一个非常有效的解决方案.
- 该模型的性能在各种气象和季节条件下得到验证.
- 这种方法提高了光伏电力集成到能源网的可靠性.
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