优化电力负载预测与支持向量回归/LSTM优化灵活的大猩猩部队算法和神经网络一个案例研究
Zhirong Zhang1, Qiqi Zhang2, Haitao Liang3
1Medical Imaging Department, Shanxi Provincial General Hospital of the Chinese People's Armed Police Force, Taiyuan, 030006, Shanxi, China. zhangzr090427@163.com.
Scientific reports
|September 27, 2024
概括
本研究引入了一个优化的支持向量回归和长短期记忆 (SVR/LSTM) 模型,通过大猩猩部队算法增强,用于准确的电荷预测. 这种新的方法显著提高了使用真实世界德克萨斯州住宅数据的预测准确性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的电荷预测对于高效的能源管理和电网稳定性至关重要.
- 现有的预测模型在精度和适应动态能源消耗模式方面面临挑战.
研究的目的:
- 提高电力负载预测模型的精度和有效性.
- 将大猩猩部队优化算法的适应能力集成到支持向量回归和长短期记忆 (SVR/LSTM) 框架中.
主要方法:
- 一个混合SVR/LSTM模型被开发和优化,使用灵活的大猩猩部队算法.
- 该方法使用德克萨斯州200个住宅物业的综合数据集进行了验证,包括电力消耗和气象数据.
- 性能与已建立的当代负载预测技术进行了基准测试.
主要成果:
- 与现有方法相比,修改后的SVR/LSTM模型表现出优越的性能.
- 提出的方法在预测电荷需求方面实现了更高的准确性和稳定性.
- 经验发现通过使用真实,多样化的数据集来增强.
结论:
- 大猩猩部队优化的SVR/LSTM模型在电力负载预测方面取得了重大进展.
- 该方法为预测能源需求提供了更准确,更强大的解决方案.
- 这项研究为优化能源管理策略提供了有价值的工具.
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