时间序列分析算法的比较研究 适合实施基于AMI的需求响应的短期预测.
Myung-Joo Park1, Hyo-Sik Yang1
1Department of Computer Science and Engineering, Sejong University, 209, Neungdong-ro, Gwangjin-gu, Seoul 05006, Republic of Korea.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
本研究将ARIMA,SARIMA,LSTM和SVM与使用高级计量基础设施 (AMI) 数据进行短期负载预测进行比较. SVM和SARIMA在处理波动性和季节性方面表现出强大优势,分别指导能源管理的最佳模型选择.
科学领域:
- 能源系统工程 能源系统工程
- 数据科学数据科学数据科学
- 电气工程 电气工程
背景情况:
- 准确的短期负载预测对于智能电网的有效需求响应 (DR) 策略至关重要.
- 先进计量基础设施 (AMI) 提供了改善预测模型所必需的实时数据.
- 评估各种时间序列算法是必要的,以优化能源管理和电网稳定性.
研究的目的:
- 为了比较四个时间序列预测算法的性能:ARIMA,SARIMA,LSTM和SVM.
- 用AMI数据评估它们在短期负载预测中的适用性和有效性.
- 根据数据特征和应用要求,为选择最佳预测模型提供准则.
主要方法:
- 对ARIMA,SARIMA,LSTM和SVM算法的比较分析.
- 基于预测准确度,计算效率和可扩展性的评估.
- 使用来自AMI系统的实时电力消耗数据集.
主要成果:
- 支持矢量机器 (SVM) 在预测非线性模式和高波动性方面表现出色.
- 季节性自动回归集成移动平均线 (SARIMA) 有效地捕捉了季节性电力消费趋势.
- 长期短期记忆 (LSTM) 展示了复杂的时间依赖的潜力,但需要大量的数据和调整.
结论:
- 预测模型的选择对需求响应策略的效率产生重大影响.
- 每个算法都有独特的优缺点,需要根据特定的数据和应用需求仔细选择.
- 将先进的预测技术集成到智能电网中可以提高可靠性,并支持动态能源管理.
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