走向自主能源管理:用于有效审计和优化的机器学习
Sherif Ashraf1, Mira M Zarie1, Sameh O Abdellatif2
1The Electrical Engineering Department and FabLab, Centre of Emerging Learning Technologies CELT, British University in Egypt (BUE), Cairo, 11387, Egypt.
Scientific reports
|November 10, 2025
概括
本研究介绍了用于能源管理的自动机器学习模型. 它优化了住宅和商业负载的消耗,实现了显著的节能和降低成本.
科学领域:
- 能源管理 能源管理
- 机器学习 机器学习
- 可持续发展 可持续性 可持续性
背景情况:
- 能源需求的增加需要有效的管理策略.
- 传统的能源审计方法可能耗时且不太准确.
- 技术进步为自动化能源优化提供了机会.
研究的目的:
- 开发和评估一个完全自动化的能源管理和审计程序.
- 利用机器学习进行负载分类,基准测试和智能监控.
- 在各种应用中展示技术经济优化和显著的能源节约.
主要方法:
- 利用机器学习来根据消费模式对能源负载进行分类.
- 使用历史数据分析建立了绩效基准.
- 实现实时监控以识别低效率和预测未来使用情况.
主要成果:
- 实现了大幅度的能源节约:34.73 MWh/年 (基本负载,埃及),215.67 MWh/年 (大学空调),0.9 MWh/年 (银行照明),0.9 MWh/年 (住宅).
- 证明了该模型以技术经济的方式优化能源消耗的能力.
- 在四个不同的案例研究中验证了有效性.
结论:
- 自动化机器学习模型对能源效率和可持续性非常有效.
- 该程序为适应不断变化的能源需求提供了变革性的潜力.
- 通过优化能源使用,可以大大节省成本.
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