期刊
ADVANCED ENGINEERING INFORMATICS
卷 58, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.aei.2023.102208
关键词
Energy-efficient control; Energy-intensive system; Model Predictive Control (MPC); Data-driven system; Internet of Things (IoT)
This paper proposes a data-driven learning-based Model Predictive Control (MPC) method for the integrated control of various devices in energy-intensive systems. The method integrates a hybrid prediction model and MPC scheme to learn and predict system dynamics based on time-series sensing data, and develops an efficient tree-based prioritized group control model for heterogeneous devices.
Efficient control of energy-intensive systems is essential for reducing energy consumption and realizing sustainable development. However, considering the complex inter-dependent energy-consumption devices, numerous control parameters, and dynamic environments, the energy-efficient control of energy-intensive system is always challenging. To address such problems, this paper proposes a data-driven learning-based Model Predictive Control (MPC) method for the integrated control of various devices in energy-intensive systems. Specifically, a hybrid prediction model based on two variants of RNN is integrated with the MPC scheme to learn and predict the system dynamics based on massive time-series sensing data. Then an efficient tree-based prioritized group control model for heterogeneous devices is developed with a rolling optimization and feedback correction mode. A real-life case study is provided to evaluate the performance of the proposed method, which demonstrates its superiority over existing methods on saving the energy consumption.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据