一种新的交通预测方法,使用机器学习来提高服务提供商网络的能源效率
Francisco Rau1, Ismael Soto1, David Zabala-Blanco2
1CIMTT, Department of Electrical Engineering, Universidad de Santiago de Chile, Santiago 9170124, Chile.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
本研究介绍了神经网络方法用于能源效率预测. 在线序列极端学习机 (OS-ELM) 在数据中心能源管理中表现出卓越的准确性和效率.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 能源系统 能源系统
背景情况:
- 数据中心面临着日益增长的能源需求,需要高效的运营策略.
- 精确预测能源消耗对于优化资源分配和减少环境影响至关重要.
- 传统的预测方法可能无法充分捕捉复杂系统中能源使用的动态性质.
研究的目的:
- 为复杂的预测问题提出一个系统的方法,强调能源效率.
- 评估各种反复和顺序神经网络的性能,用于数据中心的能量预测.
- 为了确定最准确和计算效率高的神经网络模型,用于节能应用.
主要方法:
- 利用循环和顺序神经网络进行预测任务.
- 进行了电信行业的案例研究,重点关注数据中心的能源效率.
- 我们比较了四种神经网络模型:循环神经网络 (RNN),长期短期记忆 (LSTM),门式循环单元 (GRU) 和在线序列极端学习机器 (OS-ELM).
- 基于使用真实交通数据的预测准确度和计算时间的评估模型.
主要成果:
- 在线序列极端学习机 (OS-ELM) 模型与RNN,LSTM和GRU相比,实现了更高的预测准确性.
- 此外,OS-ELM还显示了更高的计算效率,显示了更快的处理时间.
- 使用真实交通数据进行的模拟预计每天的潜在节能可达12.2%.
结论:
- 提出的系统方法,特别是使用OS-ELM,对于数据中心的能源效率预测非常有效.
- 该方法显示了减少能源消耗的巨大潜力,并且可以适应其他行业.
- 技术和数据的进一步进步将提高这种方法对各种预测挑战的适用性.
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