2020/09/21 by Yongchao Huang, Huang, Yongchao, Hugh Miles +3
Agricultural and Biological Sciences · Engineering · Environmental Science · #Air Quality Monitoring and Forecasting #Artificial Intelligence (cs.AI) #Building Energy and Comfort Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #Greenhouse Technology and Climate Control #Signal Processing (eess.SP) #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2009.09847
openalex publication_date 2020/09/21 · openalex created_date 2020/09/25 · openalex updated_date 2026/07/28
The rising availability of large volume data, along with increasing computing power, 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 mixed data-driven approach in smart building applications. By making wise use of IoT sensory data and ML algorithms, this work contributes to efficient energy management and sustainability in smart buildings.