A Sequential Modelling Approach for Indoor Temperature Prediction and Heating Control in Smart Buildings

TL;DR

This paper introduces a sequential data-driven framework combining time series and machine learning models to predict indoor temperature and optimize heating control in smart buildings, enhancing energy efficiency.

Contribution

It presents a novel two-stage modeling approach integrating AR and XGBoost models trained on real sensor data for improved temperature prediction and heating control.

Findings

Effective temperature prediction with real-world data

Improved heating control algorithms demonstrated

Potential for energy savings in smart buildings

Abstract

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.