Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings

Bünning, Felix; Huber, Benedikt; Heer, Philipp; Aboudonia, Ahmed; Lygeros, John

doi:10.1016/j.enbuild.2020.109792

Cited by 86 publications

(53 citation statements)

References 18 publications

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“…Beckenbach et al, [24] analyzed model-based predictive controllers used to manage control tasks with constraints on the state. Bünning et al, [25] developed a model predictive control method for room temperature control in buildings. Li et al, [26] explored a hierarchical model predictive control-based energy management strategy for fuel cell hybrid construction vehicles.…”

Section: A Recent Advancements In Crane Spreader and Cargo Stabilizationmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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Smart control systems are mostly applied in industry to control the movements of heavy machinery while optimizing overall operational efficiency. Major shipping companies use large quay cranes to load and unload containers from ships and still rely on the experience of on-site operators to perform transportation control procedures using joysticks and visual contact methods. This paper presents the research results of an EU-funded project for the Klaipeda container terminal to develop a novel container transportation security and cargo safety assurance method and system. It was concluded that many risks arise during the container handling procedures performed by the quay cranes and operators. To minimize these risks, the authors proposed controlling the sway of the spreader using a model predictive control method which applies a multi-layer perceptron (MLP) neural network (NN). The paper analyzes current neural network architectures and case studies and provides the engineering community with a unique case study which applies real operation statistical data. Several key training algorithms were tested, and the initial results suggest that the Levenberg-Marquardt (LM) algorithm and variable learning rate backpropagation perform better than methods which use the multi-layer perceptron neural network structure.

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Section: A Recent Advancements In Crane Spreader and Cargo Stabilizationmentioning

confidence: 99%

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

2021

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“…For DPC, we apply a modelling approach for the system dynamics in equation (1b) based on RF and linear regression, which was adapted in [7] from the original work of [6]. The introduced modelling process is separated into two steps.…”

Section: Random Forest and Linear Regression Modelmentioning

confidence: 99%

“…In [6], the approach was further developed by using random forests (RF) instead of single RT which allows one to reduce the variance in the predictions. In [7], we adapted the DPC approach based on RF and linear models and deployed it in a real-life apartment during cooling season.…”

Section: Introductionmentioning

confidence: 99%

“…Most studies about DPC are simulation-based while the study in [7] is conducted during the cooling season and does not compare with MPC. In this paper, DPC based on RF combined with linear models is applied in the real-life apartment Urban Mining & Recycling (UMAR) of the NEST demonstrator at Empa Dübendorf, Switzerland, during the heating season.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, DPC based on RF combined with linear models is applied in the real-life apartment Urban Mining & Recycling (UMAR) of the NEST demonstrator at Empa Dübendorf, Switzerland, during the heating season. Using the same models as in [7], we demonstrate the flexibility of the approach to be applicable in the heating and cooling season. Furthermore, in four experiments we benchmark the DPC with a hysteresis controller (HC) and an MPC based on an RC-network model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking of data predictive control in a real-life apartment during heating season

Huber¹,

Bünning²,

Decoussemaeker³

et al. 2021

Preprint

Self Cite

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Model Predictive Control is an energy effcient climate control strategy in buildings. However, the effort associated with physics-based modelling seems to prevent widespread application in residential buildings. Applying machine learning algorithms on historical data promises efficient generation of predictive models for control. In a recent experimental study, Data Predictive Control based on random forests and linear models outperformed a baseline controller during cooling season. In this paper, the approach is benchmarked against hysteresis control and conventional Model Predictive Control based on an RC-network model during heating season. Data Predictive Control shows promising results in terms of energy consumption and thermal comfort.

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Emerging methodologies in stability and optimization problems of learning‐based nonlinear model predictive control: A survey

Meng

Shen

Karimi

2022

Circuit Theory & Apps

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Since last 40 years, the theory and technology of model predictive control (MPC) have been developed rapidly. However, nonlinear MPC still faces difficulties such as high online computational complexity and inability to accurately model the system. In order to improve or solve the problems mentioned above of MPC, recent researches have deepened the learning‐based control. The learned method can model unknown or highly uncertain nonlinearities. And the emergence of efficient algorithms has greatly improved the feasibility of computing. Stability is at the heart of control design. Learning‐based nonlinear model predictive control (LB‐NMPC) has achieved systematic research results in the past 10 years. But the stability of LB‐NMPC is still an open question that has not been fully addressed in the literature. This review mainly summarizes the latest research progress of LB‐NMPC. More specifically, the uncertainty and online optimization problems of the considered systems are investigated mainly focusing on the use of learning techniques. At the same time, the research hotspots such as the control stability and constraint satisfaction of LB‐NMPC are briefly discussed. Finally, the application of LB‐NMPC technology in integrated circuits, path tracking control, and other fields is reviewed, which provides a reference for the research and application of LB‐NMPC.

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Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings

Cited by 86 publications

References 18 publications

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Application of Neural Network Predictive Control Methods to Solve the Shipping Container Sway Control Problem in Quay Cranes

Benchmarking of data predictive control in a real-life apartment during heating season

Emerging methodologies in stability and optimization problems of learning‐based nonlinear model predictive control: A survey

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