REDf:用于短期负载预测的深度学习模型,以促进可再生能源的整合和实现SDG7,9和13的实现
Md Saef Ullah Miah1, Junaida Sulaiman2, Md Imamul Islam3
1Department of Computer Science, American International University-Bangladesh, Dhaka, Dhaka, Bangladesh.
PeerJ. Computer science
|June 26, 2025
概括
一个新的深度学习模型准确地预测智能电网中的能源需求,改善可再生能源的整合. 这支持可持续能源目标,并增强电网稳定性,以实现更清洁的未来.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 可持续能源系统 可持续能源系统
背景情况:
- 整合可再生能源对全球可持续能源目标 (联合国可持续发展目标7) 至关重要.
- 可再生能源的间歇性对电网稳定性和管理构成挑战 (联合国可持续发展目标9).
- 准确的能源需求预测对于高效的电网运行和可再生能源整合至关重要.
研究的目的:
- 为智能电网中准确的短期能源需求预测提出一个深度学习模型.
- 通过改善需求预测,加强可再生能源的整合.
- 通过改进电网管理来支持联合国可持续发展目标7,9和13.
主要方法:
- 开发一个深度学习模型,特别是长短期记忆 (LSTM) 网络,用于时间序列预测.
- 用美国主要公用事业公司的四个历史能源需求数据集对拟议模型的评估.
- 将模型的性能与最先进的算法进行比较:Facebook Prophet,支持向量回归和随机森林回归.
主要成果:
- 拟议的REDf模型在能源需求预测中实现了1.4%的平均绝对误差 (MAE).
- 该模型与Facebook Prophet,支持向量回归和随机森林回归相比,显示出更高的准确性.
- 实验结果证实了该模型能够准确预测短期能源需求的能力.
结论:
- REDf深度学习模型为智能电网中的能源需求预测提供了高度准确的解决方案.
- 该模型可以显著提高电网的稳定性和效率,并具有高可再生能源透率.
- 这种方法通过促进可再生能源整合和气候行动,有效支持实现联合国可持续发展目标7,9和13.
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