Data-Driven Predictive Control of Building Energy Consumption under the IoT Architecture

• Published in: Wireless communications and mobile computing Vol. 2020; no. 2020; pp. 1 - 20
• Main Authors: Zhao, Xing, Wu, Hao, Yang, Shundong, Wang, Biao, Qin, Yude, Ke, Ji, Yang, Hang
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc
• Subjects: Algorithms; Communication; Complexity; Computer architecture; Cost function; Emissions; Energy consumption; Energy efficiency; Energy management; Green buildings; HVAC equipment; Internet of Things; Neural networks; Office buildings; Optimal control; Optimization; Prediction models; Predictive control; Principal components analysis; Protocol (computers); Regression analysis; Simulation; Standardization; Supervisory control and data acquisition; Thermal comfort
• DOI: 10.1155/2020/8849541
• ISSN: 1530-8669

Model predictive control is theoretically suitable for optimal control of the building, which provides a framework for optimizing a given cost function (e.g., energy consumption) subject to constraints (e.g., thermal comfort violations and HVAC system limitations) over the prediction horizon. However, due to the buildings’ heterogeneous nature, control-oriented physical models’ development may be cost and time prohibitive. Data-driven predictive control, integration of the “Internet of Things”, provides an attempt to bypass the need for physical modeling. This work presents an innovative study on a data-driven predictive control (DPC) for building energy management under the four-tier building energy Internet of Things architecture. Here, we develop a cloud-based SCADA building energy management system framework for the standardization of communication protocols and data formats, which is favorable for advanced control strategies implementation. Two DPC strategies based on building predictive models using the regression tree (RT) and the least-squares boosting (LSBoost) algorithms are presented, which are highly interpretable and easy for different stakeholders (end-user, building energy manager, and/or operator) to operate. The predictive model’s complexity is reduced by efficient feature selection to decrease the variables’ dimensionality and further alleviate the DPC optimization problem’s complexity. The selection is dependent on the principal component analysis (PCA) and the importance of disturbance variables (IoD). The proposed strategies are demonstrated both in residential and office buildings. The results show that the DPC-LSBoost has outperformed the DPC-RT and other existing control strategies (MPC, TDNN) in performance, scalability, and robustness.