基于传感器数据，预测美国某智能建筑两周内室内温区温度变化。
Time-series Forecast for Indoor Zone Air Temperature with Long Horizons: A Case Study with Sensor-based Data from a Smart Building
- 融合物理模型与数据驱动方法，构建长时序温度预测模型。
- 实现长达两周的室内温区温度预测，支持楼宇能源系统灵活调控。
- 适用于智能建筑节能控制与混合能效建模，适合能源管理研究者。
在全球气候变化背景下，极端天气和突发天气愈发频繁。为维持室内舒适环境并最大限度降低建筑对气候的影响，对暖通空调（HVAC）系统的运行与控制提出了更高要求，需更高效、更灵活地应对天气快速变化。这促使对建筑区域空气温度进行快速建模与预测。相较于传统的仿真方法（如EnergyPlus、DOE2），结合物理机制与数据驱动的混合方法更具优势。近年来，高质量数据集和算法突破推动了该领域发展，但在短中期与长期预测方面仍存在研究空白。本文旨在构建时间序列预测模型，对位于美国的一栋智能建筑的区域空气温度进行为期两周的预测。研究成果可进一步用于支持智能控制与优化运行（即需求灵活性），也可应用于混合建筑能耗建模。
原文摘要 · Abstract (English)
With the press of global climate change, extreme weather and sudden weather changes are becoming increasingly common. To maintain a comfortable indoor environment and minimize the contribution of the building to climate change as much as possible, higher requirements are placed on the operation and control of HVAC systems, e.g., more energy-efficient and flexible to response to the rapid change of weather. This places demands on the rapid modeling and prediction of zone air temperatures of buildings. Compared to the traditional simulation-based approach such as EnergyPlus and DOE2, a hybrid approach combined physics and data-driven is more suitable. Recently, the availability of high-quality datasets and algorithmic breakthroughs have driven a considerable amount of work in this field. However, in the niche of short- and long-term predictions, there are still some gaps in existing research. This paper aims to develop a time series forecast model to predict the zone air temperature in a building located in America on a 2-week horizon. The findings could be further improved to support intelligent control and operation of HVAC systems (i.e. demand flexibility) and could also be used as hybrid building energy modeling.
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