Research on a forecasted load-and time delay-based model predictive control (MPC) district energy system model

Zhao, Jing; Li, Jiayu; Yu, Sophia

doi:10.1016/j.enbuild.2020.110631

Cited by 27 publications

(4 citation statements)

References 48 publications

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“…In the past decade, with the substantial improvement of computer capabilities, LB‐NMPC has attracted the attention of many scholars. Learning‐based predictive control algorithms have been systematically investigated to leverage machine learning and statistical learning tools to control complex and challenging dynamical systems 18–25,149 …”

Section: Learning‐based Nonlinear Model Predictive Controlmentioning

confidence: 99%

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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Section: Learning‐based Nonlinear Model Predictive Controlmentioning

confidence: 99%

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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“…Therefore, it is imperative to construct a novel control model to address the issue of long-time delays in RAC systems, enhance control precision, and ensure low energy consumption. Over the past few years, there has been an increase in the popularity of model predictive control (MPC) in the area of HVAC system control [25,26] since it is capable of solving nonlinear [27,28], multivariate [27], and time-delayed [29,30] optimum control problems. MPC generally consists of a predictive model, rolling optimization, and a feedback correction mechanism [31].…”

Section: System Prediction Model Constitute Of Cost Function Mpc Impl...mentioning

confidence: 99%

A model predictive control regulation model for radiant air conditioning system based on delay time

Zhao

Shi

et al. 2022

Journal of Building Engineering

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“…Accurate heating load forecasting is a precondition to the optimization and control of district heating systems. Zhao et al (Zhao, Li and Shan, 2021) have used SVM in their study to forecast heat load to optimize the DH system using Model predictive control (MPC). They have shown a reduction in energy peak and total energy consumption of the system.…”

Section: Introductionmentioning

confidence: 99%

Machine learning assisted adaptive heat load consumption forecasting in district heating network

Renuke,

Vouros,

Kyprianidis

2023

Linköping Electronic Conference Proceedings

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District heating system often consists of a long, complex network of piping carrying heat from a power plant to the consumers. The supply temperature from the plant is either controlled by the operator from experience or a predefined curve based on the outdoor temperature. An optimized supply temperature which would be lower than the one obtained traditionally would lead to lower heat loss and reduced peak load on the power plant. In this paper, we investigate the machine learning models for heat load forecasting which is a crucial parameter in the optimizing process. Models are generated using supervised machine learning algorithms: Linear models (Linear Regression, Ridge and Gaussian Process Regressor), Random Forest Regressor, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) recurrent neural network (RNN). Data-driven models are used extensively in the literature to predict heat load prediction based on the weather and the time effect on a fixed training set, however, in this study, we model the heat load in the network in real-time scenarios i.e., adaptive training and forecasting. The model is adaptively updated as well as the training of the machine learning model in real time. It provides a “plug-and-play” solution for real-time prediction without significant pre-tuning requirements. The results of all the models are compared with various time horizons i.e., 6 hrs, 10 hrs, 24 hrs and 1 week, using the district heating data obtained for the city of Vasteras in Sweden. The performance of the prediction algorithms is evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). An algorithm with the best accuracy is selected based on the performance comparison. Also, models suitable for short-term and long-term forecasting are discussed towards the end of the article

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Research on a forecasted load-and time delay-based model predictive control (MPC) district energy system model

Cited by 27 publications

References 48 publications

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

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

A model predictive control regulation model for radiant air conditioning system based on delay time

Machine learning assisted adaptive heat load consumption forecasting in district heating network

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