基于滑动模糊颗粒和平衡优化器的短期负载预测系统.
Shoujiang Li1, Jianzhou Wang1, Hui Zhang2
1Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau, 999078 China.
概括
本研究介绍了一种先进的短期电力负载预测系统. 它结合了新的数据处理,元启发学和深度神经网络,以提高电源管理的准确性和稳定性.
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:
- 短期电力负载预测对于电力系统运行和规划至关重要.
- 现有模型在负载数据中的噪声和非静止性方面扎,导致不确定性.
- 准确的预测对于高效的能源管理和电网稳定性至关重要.
研究的目的:
- 开发一个强大的短期负载预测系统,解决噪声和非静止性问题.
- 通过混合方法提高预测准确性和稳定性.
- 为全面的负载预测提供点和间隔预测.
主要方法:
- 一种经过修改的信息处理技术,使用滑动模糊颗粒处理来消除负载数据和捕捉不确定性.
- 深度神经网络 (DNN) 用于模拟电荷中的复杂非线性模式.
- 一个先进的元启发算法来优化DNN权重系数以提高稳定性.
主要成果:
- 与现有方法相比,拟议的系统显示出更高的有效性和稳定性.
- 实验结果验证了系统处理噪音和非静止负载数据的能力.
- 使用多个指标的全面评估证实了预测业绩增长.
结论:
- 混合预测系统有效地减轻了电力负载数据中的噪声和非静止性.
- 滑动模糊颗粒,DNN和元启发学的整合显著提高了预测的准确性和可靠性.
- 这种方法为电力管理中的短期电力负载预测提供了更稳定,更全面的解决方案.
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