基于两个阶段的模型预测光伏发电与物联网传感器数据的结合
Youngju Heo1, Jangkyum Kim2, Seong Gon Choi3
1DGB Financial Holding Company, Seoul 04521, Republic of Korea.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
本研究介绍了一种两阶段的神经网络模型,用于使用物联网 (IoT) 传感器数据进行短期光伏 (PV) 功率预测. 这种新的方法通过预测光伏输出之前预测物联网数据来提高光伏发电预测的准确性.
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
- 传感器网络和物联网物联网
背景情况:
- 气象数据为光伏 (PV) 电力预测提供了广泛的环境洞察力,但缺乏局部准确性.
- 现有的研究往往忽视了物联网 (IoT) 传感器数据采集用于光伏预测的关键时机.
- 在现实世界中,物联网数据的可用性可能不一致,因此需要事先预测,以便准确地预测光伏发电.
研究的目的:
- 开发一个新的短期光伏 (PV) 预测方案,利用物联网 (IoT) 传感器数据.
- 通过整合物联网传感器检测到的局部环境变化来解决气象数据的局限性.
- 通过提出一个两个阶段的神经网络模型来提高光伏预测的准确性,该模型可以考虑物联网数据的时间方面.
主要方法:
- 为PV预测提出了一个两阶段的神经网络模型.
- 第一个阶段使用现有的环境数据预测未来的物联网传感器数据.
- 第二阶段利用预测的物联网数据和环境数据进行光伏发电预测,在每个阶段优化预测方案.
主要成果:
- 拟议的两阶段模型显著提高了短期光伏功率预测的准确性.
- 该方案显示,与仅依赖于气象数据的基线方法相比,预测准确度提高了12%以上.
- 该模型有效地集成预测的物联网传感器数据,以实现更精确的光伏发电预测.
结论:
- 这种新的两阶段预测方案为准确的短期光伏电力预测提供了强大的解决方案.
- 将预测的物联网传感器数据与环境数据相结合,对于克服传统预测方法的局限性至关重要.
- 这种方法为预测光伏发电提供了更可靠的方法,特别是在环境条件动态的地区.
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