集体学习方法用于未来智能电网中的先进计量基础设施.
Muhammad Irfan1, Nasir Ayub2, Faisal Althobiani3
1Electrical Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia.
PloS one
|October 18, 2023
概括
本研究介绍了一种改进的在线学习框架,用于电力负载和价格预测 (ELPF). 这种新的方法提高了预测准确性和效率,优于智能电网稳定性的现有方法.
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:
- 传统的批量学习用于负载预测,与实时数据集成作斗争.
- 现有的电力负载和价格预测方法 (ELPF) 面临着大数据集,非线性,高方差和高维度的挑战.
- 在线学习为改进预测提供了对新数据的高效适应.
研究的目的:
- 为同步电力负载和价格预测 (ELPF) 开发一个紧和改进的算法.
- 提高智能电网负载和价格预测的准确性和效率.
- 解决处理复杂,高维和非线性能源数据的现有方法的局限性.
主要方法:
- 一个新的ELPF框架,包含数据分离 (高/低消费者),缺失/非标准化数据处理以及功能选择/减少.
- 实施了改进的剩余网络 (ResNet-152) 和改进的支持矢量机器 (SVM) 进行预测.
- 应用不同的机制,包括规范化,基础学习者选择和超参数调整,以优化ResNet-152和SVM性能.
主要成果:
- 与现有方案相比,拟议的方法在绩效指标上显示出8%的改进.
- 该框架有效地处理复杂的消费者数据,通过减少时间复杂性和减轻过度装配.
- 对各种ResNet-152和SVM结构的探索增强了规范化,基础学习者选择和参数调整,以获得更高的装配能力.
结论:
- 开发的ELPF框架在智能电网的预测准确性和效率方面取得了重大进展.
- 拟议的在线学习方法有效地与ELPF中的各种数据程序同步.
- 该方法适用于基于行业的应用,改善能源电网的稳定性和管理.
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