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.

A class of nonlinear networked systems with external interference is designed in this paper. Currently, we have witnessed that networked control technology has played a key role in the Internet of Things (IoT). However, the amount of big data in the Internet of Things will cause network congestion in the data transmission of the network control system. In order to solve this problem, event-driven control scheme can effectively save the network resources of the network control system. But when there is interference in the system, the conventional constant threshold parameter is difficult to achieve the expected energy-saving effect. In order to solve this challenge, this paper proposes a design with a continuously variable threshold. After each trigger to transmit data, the threshold gets changed accordingly, and the sliding mode approach rate is changed simultaneously. Compared with the constant threshold event drive, the number of transmissions in this design can be greatly reduced, while sliding mode jitter is suppressed. The simulation results show that the scheme can achieve higher resource utilization efficiency and better robustness.