智能建筑基础设施中的基于物联网的能源和环境参数数据集
Adel Oulefki1, Abbes Amira1, Fatih Kurugollu1
1Smart Sustainable Cities Research Group (S2C), University of Sharjah, United Arab Emirates.
Data in brief
|December 16, 2024
概括
这个数据集捕获了智能校园计划中的能源使用和环境数据. 它包括设备功率指标和传感器读数,可视化使用马科夫过渡场用于先进的AI研究.
科学领域:
- 计算机科学 计算机科学
- 环境科学 环境科学
- 电气工程 电气工程
背景情况:
- 沙迦大学正在到2030年转变为一个智能校园.
- 智能校园计划需要详细的能源消耗和环境因素数据.
研究的目的:
- 在智能校园环境中提供有关能源消耗和环境条件的综合数据集.
- 促进能源管理,智能系统和深度学习应用领域的研究.
主要方法:
- 从2024年1月1日到2024年6月20日,在校园设施中收集数据.
- 利用物联网 (IoT) 传感器来监控特定设备的功率数据 (瓦特,电压,kWh).
- 记录环境参数,包括温度,湿度和占用率.
- 从时间序列数据生成马尔科夫过渡场 (MTF) 可视化,用于高级分析.
主要成果:
- 一个详细的数据集,包括能源消耗指标和环境条件.
- 包括设备特定的功率数据和关键环境参数.
- 该数据集具有独特的马尔科夫过渡场 (MTF) 可视化,用于时间序列数据解释.
结论:
- 该数据集为正在过渡的智能校园的运营动态提供了有价值的见解.
- 它支持能源管理的研究,智能系统的开发,以及深度和转移学习的应用.
- 既有原始数据和加工数据可用于满足各种研究需求.
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