Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control

2016 ◽  
Vol 133 ◽  
pp. 345-358 ◽  
Author(s):  
Saran Salakij ◽  
Na Yu ◽  
Samuel Paolucci ◽  
Panos Antsaklis
Keyword(s):  
Optimal Control ◽  
Energy Management ◽  
Predictive Control ◽  
Building Energy ◽  
Energy Modeling ◽  
Model Based ◽  
Building Energy Management

Model-based predictive control for building energy management: Part II – Experimental validations

2017 ◽  
Vol 146 ◽  
pp. 19-26 ◽  
Author(s):  
Na Yu ◽  
Saran Salakij ◽  
Rafael Chavez ◽  
Samuel Paolucci ◽  
Mihir Sen ◽  
...  
Keyword(s):  
Energy Management ◽  
Predictive Control ◽  
Building Energy ◽  
Model Based ◽  
Building Energy Management ◽  
Experimental Validations

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

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Ji Ke ◽  
Yude Qin ◽  
Biao Wang ◽  
Shundong Yang ◽  
Hao Wu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Internet Of Things ◽  
Energy Management ◽  
Predictive Control ◽  
Control Strategies ◽  
Principal Component ◽  
Building Energy ◽  
Physical Models ◽  
Data Driven ◽  
Building Energy Management

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.

Efficient building energy management using distributed model predictive control

2014 ◽  
Vol 24 (6) ◽  
pp. 740-749 ◽  
Author(s):  
H.F. Scherer ◽  
M. Pasamontes ◽  
J.L. Guzmán ◽  
J.D. Álvarez ◽  
E. Camponogara ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Energy Management ◽  
Predictive Control ◽  
Building Energy ◽  
Distributed Model ◽  
Distributed Model Predictive Control ◽  
Building Energy Management
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