瑞科数据集:一个多变量室内和室外HVAC时间序列数据集
Zachari Thiry1, Massimiliano Ruocco2, Alessandro Nocente3
1SINTEF Manufacturing, SP Andersens Veg 3, Trondheim, Norway.
Data in brief
|July 2, 2025
概括
研究人员开发了开源的RICO数据集,用于室内温度预测. 这一数据集有助于机器学习模型培训,以更好地控制HVAC,提高能源效率和乘客舒适度.
科学领域:
- 建筑科学 建筑科学
- 数据科学数据科学数据科学
- 能源系统 能源系统
背景情况:
- 室内温度预测对于优化HVAC系统,减少能源浪费,提高乘客舒适度和健康至关重要.
- 为此任务培训机器学习模型受到缺乏高质量,公开可用的过渡制度数据的阻碍.
- 获取此类数据是昂贵的,通常需要超出正常运行参数的广泛的建筑加热和冷却周期.
研究的目的:
- 介绍RICO数据集,这是一个用于室内温度预测的多变量时间序列数据的开源集合.
- 为研究人员和工程师在HVAC控制和能源效率方面提供宝贵的资源.
- 克服与传统数据采集方法相关的数据稀缺性和高成本的局限性.
主要方法:
- 收集了305个多变量时间序列数据集,每一个持续时间为4小时.
- 使用专用,受控的物理测试构建,以确保数据质量和可靠性.
- 从内部和外部环境传感器捕获了80个丰富的特征,每隔一分钟取样.
主要成果:
- 瑞科数据集提供了现实世界的过渡制度数据的全面收集.
- 该数据集适合用于训练和验证用于室内温度预测的机器学习模型.
- 在受控环境中获取数据可以缓解数字模拟中常见的问题.
结论:
- 瑞科数据集解决了在室内温度预测研究中对可访问,高质量的数据的关键需求.
- 它促进了HVAC控制策略的进步,从而大大节省能源并提高了建筑性能.
- 数据集的开源性质促进了智能建筑技术的更广泛的研究和开发.
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