在智能电网中使用机器学习技术进行需求侧负载预测
Muhammad Yasir Masood1, Sana Aurangzeb2, Muhammad Aleem2
1The University of Lahore, Lahore, Pakistan.
PeerJ. Computer science
|May 3, 2024
概括
精确的电力负载预测通过新的三层架构和先进的天气数据利用得到了改进. 支持向量回归在预测能源消耗方面表现出卓越的性能.
科学领域:
- 电气工程 电气工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 由于天气等动态环境因素,电力负载预测面临挑战.
- 传统方法在物联网设备和智能电表的大数据管理方面遇到了困难.
- 现有的两层架构经常受到低精度和过度装配的影响.
研究的目的:
- 开发一个先进的,强大的电力负载预测系统.
- 利用大数据和物联网来改善能源消耗预测.
- 通过结合未充分利用的天气特征来提高预测准确度.
主要方法:
- 使用每日消耗电网 (DCEN) 和内部负载预测网络 (ILFN) 的双层预测方法.
- 实现三层架构:云层,雾层和边缘层.
- 利用传统的神经网络来缓解过度拟合和采用支持向量回归 (SVR).
主要成果:
- 支持向量回归在实验评估中表现优于其他方法.
- 获得了5.055.5的平均绝对百分比误差 (MAPE).
- 获得0.69的根平均平方误差 (RMSE) 和0.86.8的R2得分.
结论:
- 拟议的三层架构和增强的功能集显著改善了电荷预测.
- 这项研究证实了支持向量回归对此任务的有效性.
- 这些发现表明,能源消耗预测的方法更准确,更可靠.
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