在智能城市使用混合ARIMA-TFT模型对多变量时间序列物联网数据进行CO2度预测
Pantelis Linardatos1, Vasilis Papastefanopoulos2, Theodor Panagiotakopoulos3,4
1Department of Mathematics, University of Patras, 265 04, Patras, Greece. p.linardatos@upnet.gr.
Scientific reports
|October 12, 2023
概括
一个新的混合机器学习系统使用物联网数据准确预测智能城市的二氧化碳 (CO2) 水平. 这种透明,可解释的模型优于传统和深度学习方法,用于有效的气候变化管理.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 机器学习 机器学习
背景情况:
- 大气中二氧化碳 (CO2) 水平的上升推动了全球变暖,尽管经济放缓,但仍有历史新高.
- 智慧城市倡议利用物联网 (IoT) 技术实现数据驱动的环境管理和减排.
- 准确的二氧化碳预测对于制定有效的气候变化缓解战略至关重要.
研究的目的:
- 开发和评估一种混合机器学习系统,用于预测智能城市环境中的二氧化碳度.
- 与传统的时间序列和深度学习模型对比拟的系统的性能.
- 确保开发的系统是可解释的,并提供有关预测驱动因素的见解.
主要方法:
- 开发了一个混合机器学习系统,使用从物联网传感器测量二氧化碳和环境因素的多变量时间序列数据集.
- 该系统的预测性能与传统的时间序列方法和最先进的深度学习架构 (例如变压器) 相比进行了评估.
- 在各种场景,用例和预测视野中进行了实证比较.
主要成果:
- 与现有方法相比,混合机器学习系统表现出优异的性能和可解释性.
- 深度学习方法通常优于传统的时间序列方法,特别是在更长的预测时间范围内.
- 在不同指标和设置中观察到混合解决方案的统计学上显著的性能改善.
结论:
- 开发的混合系统为智能城市的二氧化碳度预测提供了高度准确和透明的解决方案.
- 这些发现强调了先进的机器学习技术,特别是深度学习在应对气候变化挑战方面的潜力.
- 该系统的可解释性为了解和有效管理城市碳排放提供了宝贵的见解.
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