The rising availability of large volume data has enabled a wide application of statistical Machine Learning (ML) algorithms in the domains of Cyber-Physical Systems (CPS), Internet of Things (IoT) and Smart Building Networks (SBN). This paper proposes a learning-based framework for sequentially applying the data-driven statistical methods to predict indoor temperature and yields an algorithm for controlling building heating system accordingly. This framework consists of a two-stage modelling effort: in the first stage, an univariate time series model (AR) was employed to predict ambient conditions; together with other control variables, they served as the input features for a second stage modelling where an multivariate ML model (XGBoost) was deployed. The models were trained with real world data from building sensor network measurements, and used to predict future temperature trajectories. Experimental results demonstrate the effectiveness of the modelling approach and control algorithm, and reveal the promising potential of the data-driven approach in smart building applications over traditional dynamics-based modelling methods. By making wise use of IoT sensory data and ML algorithms, this work contributes to efficient energy management and sustainability in smart buildings.

中文翻译：

---

智能建筑室内温度预测和供暖控制的序列建模方法

大量数据的可用性不断提高，使得统计机器学习 (ML) 算法在信息物理系统 (CPS)、物联网 (IoT) 和智能建筑网络 (SBN) 领域得到广泛应用。本文提出了一种基于学习的框架，用于顺序应用数据驱动的统计方法来预测室内温度，并相应地产生一种用于控制建筑供暖系统的算法。该框架由两个阶段的建模工作组成：在第一阶段，采用单变量时间序列模型 (AR) 来预测环境条件；它们与其他控制变量一起用作第二阶段建模的输入特征，其中部署了多变量 ML 模型 (XGBoost)。这些模型使用来自构建传感器网络测量的真实世界数据进行训练，并用于预测未来的温度轨迹。实验结果证明了建模方法和控制算法的有效性，并揭示了数据驱动方法在智能建筑应用中优于传统基于动力学的建模方法的潜力。通过明智地利用物联网传感数据和机器学习算法，这项工作有助于智能建筑的高效能源管理和可持续性。