设计一种混合学习模型,以在智能电网环境中建立一致性
1Department of Electronics and Communication Engineering, KGiSL Institute of Technology, Coimbatore, Tamil Nadu, India. mahendran.s@kgkite.ac.in.
Scientific reports
|December 29, 2025
概括
智能电网为能源需求产生了大量数据. 一种混合长短期记忆 (LSTM) 和神经模糊自适应干扰模型 (NFADIM) 改进了稳定电力系统的消费者负载预测.
科学领域:
- 电气工程 电气工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 智能电网从消费者能源需求中生成大量数据集.
- 准确的需求预测对于电网稳定性和高效的电力传输至关重要.
- 传统的方法难以应对智能电网数据的规模和复杂性.
研究的目的:
- 为智能电网需求预测开发先进的数据驱动方法.
- 使用深度学习提高短期负载预测的准确性.
- 通过更好的需求响应管理,提高电力系统的稳定性和组织性.
主要方法:
- 利用深度学习技术来识别消费者数据中的模式.
- 提出了一种混合模型,将长短期记忆 (LSTM) 网络与神经模糊自适应干扰模型 (NFADIM) 结合起来.
- 专注于智能电网负载预测和相关因素的NFADIM.
主要成果:
- 与传统技术相比,拟议的混合LSTM-NFADIM模型在需求预测方面表现优越.
- 实现了准确的短期负载预测,这对于平衡能源供需至关重要.
- 该模型有效地识别了消费者数据模式,以加强电网管理.
结论:
- 深度学习,特别是混合LSTM-NFADIM,为智能电网需求预测提供了强大的替代方案.
- 准确的短期负载预测对于保持电力系统稳定性和效率至关重要.
- 建议对先进的预测模型进行进一步的研究和工业关注,以优化智能电网.
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