基于相对应分析和模型融合的多能负载的短期预测
Min Xie1, Shengzhen Lin1, Kaiyuan Dong1
1School of Electric Power, South China University of Technology, Guangzhou 510641, China.
Entropy (Basel, Switzerland)
|September 28, 2023
概括
准确的短期能源负载预测需要考虑历史数据,负载相关性和天气. 这项研究提出了一种新的两阶段模糊优化方法,使用分析和先进的机器学习来改进综合能源系统预测.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 预测科学 预测科学
背景情况:
- 准确的短期负载预测对于集成能源系统 (IES) 至关重要.
- 现有的模型经常与复杂的负载相关性和天气等外部因素作斗争.
- 提高预测准确度可以提高IES的运营效率和稳定性.
研究的目的:
- 开发IES中短期多能负载的先进预测模型.
- 为了增强功能选择和识别,使用双阶段模糊优化方法.
- 提高多能负载预测的准确性和稳定性.
主要方法:
- 为特征选择和负载识别提出了两阶段的模糊优化.
- 引入了联相关性分析,以提取多能负载之间的动态联相关性.
- 使用Akaike信息标准 (AIC) 进行型模型选择.
- 结合NARX神经网络与贝叶斯优化和GA优化的ELM进行特征融合.
主要成果:
- 拟议的方法有效地提取多能负载之间的复杂动态合相关性.
- 集成模型显示了功能融合和预测性能的改进.
- 使用现实世界IES数据的实验结果验证了该模型的有效性.
结论:
- 开发的双阶段模糊优化方法与分析显著提高了短期多能负载预测准确度.
- 结合NARX和GA优化的ELM,增强了特征融合能力.
- 拟议的模型为集成能源系统的负载预测提供了可靠的解决方案.
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