人工智能的应用用于短期光伏发电预测
Helder R O Rocha1, Rodrigo Fiorotti1,2, Jussara F Fardin1
1Department of Electrical Engineering, Federal University of Espírito Santo, Av. Fernando Ferrari, 514, Vitória 29075-910, ES, Brazil.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
准确的光伏电力预测至关重要. 时间卷积网络 (TCN) 在预测光伏功率,电压和15分钟和24小时时间的效率方面显著优于长期短期内存 (LSTM) 和双向LSTM.
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
- 光伏发电是光伏发电的发电方式.
背景情况:
- 有效利用太阳能需要精确的光伏发电预测.
- 准确的估计对于电网整合和能源管理至关重要.
研究的目的:
- 评估和比较长短期内存 (LSTM),双向LSTM和时间卷积网络 (TCN) 对光伏功率,电压和效率的预测性能.
- 确定用于短期光伏发电预测的最有效的深度学习技术.
主要方法:
- 应用了包括LSTM,双向LSTM和TCN在内的深度学习模型来预测PV参数.
- 来自西班牙马德里1320Wp无形光伏发电厂的一年多的实验数据被用于培训和验证.
- 对15分钟和24小时的时间进行了预测.
主要成果:
- 与LSTM和双向LSTM相比,时间卷积网络 (TCN) 在所有预测变量和视野中表现出优异的表现.
- 在15分钟的预测中,TCN实现了0.0024的平均平方误差 (MSE),在24小时的预测中达到0.0058.
- 灵敏度分析表明,随着预测时间的延长,TCN的准确性下降,但6个月的数据证明足以获得适当的结果.
结论:
- TCN是一种高效的深度学习模型,用于准确预测光伏功率,电压和效率.
- 这些发现支持采用TCN来提高太阳能发电系统的运行效率.
- 即使有延长的预测时间,TCN也提供了足够的培训数据,可靠的预测.
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