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Energies
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Energy Efficiency and Data-Driven Control
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Energy Efficiency and Data-Driven Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G1: Smart Cities and Urban Management".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 36732

Special Issue Editors

Prof. Radu-Emil Precup

E-Mail Website
Guest Editor
Professor of Automation and Applied Informatics, Politehnica University of Timisoara, Timisoara, Romania
Interests: control structures and algorithms (conventional control; fuzzy control; data-driven control; model-free control; sliding mode control; neuro-fuzzy control), theory and applications of soft computing; systems modeling; identification and optimization (including nature-inspired optimization)
Special Issues, Collections and Topics in MDPI journals
Prof. Zhongsheng Hou

E-Mail Website
Guest Editor
Advanced Control Systems Laboratory, Beijing Jiaotong University, No. 3 Shang Yuan Cun, Hai Dian District, Beijing, China
Interests: data-driven control; model free adaptive control; learning control; intelligent transportation systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade has lead to a serious step forward regarding the complexity of processes, and also to high demanding performance, including energy efficiency. Advanced control systems that include intelligent control, adaptive control, data-driven and learning control, have been successfully applied to cope with the uncertainties and disturbances of many processes. The optimization algorithms play an important role in this context as they give, in case of correct formulations, solutions to rather complicated problems in order to meet systematically the performance specifications of control systems.

Nowadays, process control applications are developed in the conditions of optimal performance requirements. However, there is generally no dynamical model available for the process, or the process model is too complex to be used in controller design. Since modeling and system identification tools can be expensive and time-consuming, and models may be time-varying, or nonlinear, or contain delays, data-driven control has been proposed, with the aim to avoid the use of process models in controller tuning and to efficiently use the information in large amounts of process input–output data to design predictors, controllers, and monitoring systems that guarantee the required control system performance.

Energy efficiency deals with hot topics related to energy efficiency, energy savings, energy consumption, energy sufficiency, and energy transition. Since efficiency requires adequate performance indices to define and assess, the intersection of energy efficiency and data-driven control leads to high control system performance. Nevertheless, model-free versus model-based tuning problems have to be treated carefully.

The main objective of this Special Issue is to create a platform for scientists, engineers and practitioners to share their latest theoretical and technological results and to discuss several issues in the research directions of the fields of energy efficiency and data-driven control. The papers to be published in this Special Issue are expected to provide recent results in advanced controller design and tuning techniques, especially for cross-fertilizations between the fields of energy efficiency and data-driven control. Papers containing experimental results regarding advanced control systems and optimization are especially welcome.

Prof. Radu-Emil Precup
Prof. Zhongsheng Hou
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Data-driven control, monitoring and modeling
  • Data-driven optimization, scheduling, decision and simulation
  • Data-driven fault diagnosis and performance evaluation
  • Model-free control
  • Model-free adaptive control
  • Iterative learning control and identification
  • Advanced intelligent techniques for data-driven control and optimization
  • Active disturbance rejection control
  • Learning-based control
  • Reinforcement learning for real-time control and optimization
  • Approximate dynamic programming

Published Papers (9 papers)

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Research

17 pages, 1405 KiB  
Article
Data-Driven Model-Free Adaptive Control Based on Error Minimized Regularized Online Sequential Extreme Learning Machine
by Xiaofei Zhang and Hongbin Ma
Energies 2019, 12(17), 3241; https://doi.org/10.3390/en12173241 - 22 Aug 2019
Cited by 5 | Viewed by 2125
Abstract
Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of [...] Read more.
Model-free adaptive control (MFAC) builds a virtual equivalent dynamic linearized model by using a dynamic linearization technique. The virtual equivalent dynamic linearized model contains some time-varying parameters, time-varying parameters usually include high nonlinearity implicitly, and the performance will degrade if the nonlinearity of these time-varying parameters is high. In this paper, first, a novel learning algorithm named error minimized regularized online sequential extreme learning machine (EMREOS-ELM) is investigated. Second, EMREOS-ELM is used to estimate those time-varying parameters, a model-free adaptive control method based on EMREOS-ELM is introduced for single-input single-output unknown discrete-time nonlinear systems, and the stability of the proposed algorithm is guaranteed by theoretical analysis. Finally, the proposed algorithm is compared with five other control algorithms for an unknown discrete-time nonlinear system, and simulation results show that the proposed algorithm can improve the performance of control systems. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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19 pages, 3648 KiB  
Article
Combination of Data-Driven Active Disturbance Rejection and Takagi-Sugeno Fuzzy Control with Experimental Validation on Tower Crane Systems
by Raul-Cristian Roman, Radu-Emil Precup, Emil M. Petriu and Florin Dragan
Energies 2019, 12(8), 1548; https://doi.org/10.3390/en12081548 - 24 Apr 2019
Cited by 50 | Viewed by 3658
Abstract
In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). [...] Read more.
In this paper a second-order data-driven Active Disturbance Rejection Control (ADRC) is merged with a proportional-derivative Takagi-Sugeno Fuzzy (PDTSF) logic controller, resulting in two new control structures referred to as second-order data-driven Active Disturbance Rejection Control combined with Proportional-Derivative Takagi-Sugeno Fuzzy Control (ADRC–PDTSFC). The data-driven ADRC–PDTSFC structure was compared with a data-driven ADRC structure and the control system structures were validated by real-time experiments on a nonlinear Multi Input-Multi Output tower crane system (TCS) laboratory equipment, where the cart position and the arm angular position of TCS were controlled using two Single Input-Single Output control system structures running in parallel. The parameters of the data-driven algorithms were tuned in a model-based way using a metaheuristic algorithm in order to improve the efficiency of energy consumption. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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19 pages, 2497 KiB  
Article
Multi-Agent-Based Data-Driven Distributed Adaptive Cooperative Control in Urban Traffic Signal Timing
by Haibo Zhang, Xiaoming Liu, Honghai Ji, Zhongsheng Hou and Lingling Fan
Energies 2019, 12(7), 1402; https://doi.org/10.3390/en12071402 - 11 Apr 2019
Cited by 35 | Viewed by 3874
Abstract
Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal [...] Read more.
Data-driven intelligent transportation systems (D2ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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21 pages, 2543 KiB  
Article
Integrated Optimization Design of Combined Cooling, Heating, and Power System Coupled with Solar and Biomass Energy
by Lizhi Zhang, Fan Li, Bo Sun and Chenghui Zhang
Energies 2019, 12(4), 687; https://doi.org/10.3390/en12040687 - 20 Feb 2019
Cited by 26 | Viewed by 3742
Abstract
The combined cooling, heating, and power (CCHP) systems coupled with solar energy and biomass energy can meet the needs of island or rural decentralized and small-scale integrated energy use, which have become increasingly popular in recent years. This study presents a renewable energy [...] Read more.
The combined cooling, heating, and power (CCHP) systems coupled with solar energy and biomass energy can meet the needs of island or rural decentralized and small-scale integrated energy use, which have become increasingly popular in recent years. This study presents a renewable energy sources integrated combined cooling, heating, and power (RES-CCHP) system, driven by a biogas fueled internal combustion engine (ICE) and photovoltaic (PV) panels, which is different from the traditional natural gas CCHP system. Owing to the solar energy volatility and the constraint of biomass gas production, the traditional optimization design method is no longer applicable. To improve the energetic, economic and environmental performances of the system, an integrated design method with renewable energy capacity, power equipment capacity and key operating parameters as optimization variables is proposed. In addition, a case study of a small farm in Jinan, China, is conducted to verify the feasibility of the proposed RES–CCHP system structure and the corresponding optimal operation strategy. The results illustrate that the implementation of the optimal design is energy-efficient, economical and environmentally-friendly. The values of primary energy saving ratio, annual total cost saving rate and carbon emission reduction ratio are 20.94%, 11.73% and 40.79%, respectively. Finally, the influence of the volatility of renewable energy sources on the optimization method is analyzed, which shows that the RES–CCHP system and the method proposed are robust. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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11 pages, 624 KiB  
Article
Energy Efficiency Solutions for Buildings: Automated Fault Diagnosis of Air Handling Units Using Generative Adversarial Networks
by Chaowen Zhong, Ke Yan, Yuting Dai, Ning Jin and Bing Lou
Energies 2019, 12(3), 527; https://doi.org/10.3390/en12030527 - 07 Feb 2019
Cited by 50 | Viewed by 4989
Abstract
Automated fault diagnosis (AFD) for various energy consumption components is one of the main topics for energy efficiency solutions. However, the lack of faulty samples in the training process remains as a difficulty for data-driven AFD of heating, ventilation and air conditioning (HVAC) [...] Read more.
Automated fault diagnosis (AFD) for various energy consumption components is one of the main topics for energy efficiency solutions. However, the lack of faulty samples in the training process remains as a difficulty for data-driven AFD of heating, ventilation and air conditioning (HVAC) subsystems, such as air handling units (AHU). Existing works show that semi-supervised learning theories can effectively alleviate the issue by iteratively inserting newly tested faulty data samples into the training pool when the same fault happens again. However, a research gap exists between theoretical AFD algorithms and real-world applications. First, for real-world AFD applications, it is hard to predict the time when the same fault happens again. Second, the training set is required to be pre-defined and fixed before being packed into the building management system (BMS) for automatic HVAC fault diagnosis. The semi-supervised learning process of iteratively absorbing testing data into the training pool can be irrelevant for industrial usage of the AFD methods. Generative adversarial network (GAN) is well-known as an unsupervised learning technique to enrich the training pool with fake samples that are close to real faulty samples. In this study, a hybrid generative adversarial network (GAN) is proposed combining Wasserstein GAN with traditional classifiers to perform fault diagnosis mimicking the real-world scenarios with limited faulty training samples in the training process. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach for fault diagnosis problems of AHU subsystem. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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23 pages, 4474 KiB  
Article
Maximum Sensitivity-Constrained Data-Driven Active Disturbance Rejection Control with Application to Airflow Control in Power Plant
by Ting He, Zhenlong Wu, Rongqi Shi, Donghai Li, Li Sun, Lingmei Wang and Song Zheng
Energies 2019, 12(2), 231; https://doi.org/10.3390/en12020231 - 13 Jan 2019
Cited by 21 | Viewed by 4603
Abstract
The increasing energy demand and the changing of energy structure have imposed higher requirements on the conventional large-scale power plants control. Complexity of the power plant processes and the frequent change of operation condition make the accurate physical models hard to obtain for [...] Read more.
The increasing energy demand and the changing of energy structure have imposed higher requirements on the conventional large-scale power plants control. Complexity of the power plant processes and the frequent change of operation condition make the accurate physical models hard to obtain for control design. To this end, a data-driven control strategy, the active disturbance rejection control (ADRC) has received much attention for the estimation and mitigation of uncertain dynamics beyond the canonical form of cascaded integrators. However, the robustness of ADRC is seldom discussed in a quantitative manner. In this study, the maximum sensitivity is used to evaluate and then constrain the robustness of ADRC applied to high-order processes. Firstly, by using the new idea of the vertical asymptote of the Nyquist curve, a preliminary one-parameter-tuning method is developed. Secondly, a quantitative relationship between the maximum sensitivity and the tuning parameter is established using optimization methods. Then, the feasibility and effectiveness of the proposed method is initially verified in the total air flow control of a power plant simulator. Finally, field tests on the secondary airflow control in a 330 MWe circulating fluidized bed confirm the merit of the proposed maximum sensitivity-constrained ADRC tuning. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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21 pages, 603 KiB  
Article
Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid
by Rasool Bukhsh, Nadeem Javaid, Zahoor Ali Khan, Farruh Ishmanov, Muhammad Khalil Afzal and Zahid Wadud
Energies 2018, 11(12), 3345; https://doi.org/10.3390/en11123345 - 30 Nov 2018
Cited by 22 | Viewed by 3570
Abstract
The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can [...] Read more.
The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids. Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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14 pages, 1220 KiB  
Article
Adaptive-Observer-Based Data Driven Voltage Control in Islanded-Mode of Distributed Energy Resource Systems
by Yan Xia, Yuchen Dai, Wenxu Yan, Dezhi Xu and Chengshun Yang
Energies 2018, 11(12), 3299; https://doi.org/10.3390/en11123299 - 26 Nov 2018
Cited by 5 | Viewed by 2307
Abstract
In this paper, an adaptive observer based data driven control scheme is proposed for the voltage control of dispatchable distributed energy resource (DER) systems which work in islanded operation. In the design procedure of the proposed control scheme, we utilize the novel transformation [...] Read more.
In this paper, an adaptive observer based data driven control scheme is proposed for the voltage control of dispatchable distributed energy resource (DER) systems which work in islanded operation. In the design procedure of the proposed control scheme, we utilize the novel transformation and linearization technique for the islanded DER system dynamics, which is proper for the proposed data driven control algorithm. Moreover, the pseudo partial derivative (PPD) parameter matrix can be estimated online by multiple adaptive observers. Then, the adaptive constrained controller is designed only based on the online identification results derived from the input/output (I/O) data of the controlled DER system. It is theoretically proven that all the signals in the closed-loop control system are uniformly ultimately bounded based on the Lyapunov stability analysis approach. In addition, the results of the simulation comparison are given to verify the voltage control effect of the proposed control scheme. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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12 pages, 3988 KiB  
Article
An Artificial Intelligence Method for Energy Efficient Operation of Crude Distillation Units under Uncertain Feed Composition
by Muhammad Amin Durrani, Iftikhar Ahmad, Manabu Kano and Shinji Hasebe
Energies 2018, 11(11), 2993; https://doi.org/10.3390/en11112993 - 01 Nov 2018
Cited by 20 | Viewed by 6341
Abstract
The crude distillation unit (CDU) is one of the most energy-intensive processes of a petroleum refinery. The composition of crude is subject to change on regular basis. The uncertainty in crude oil composition causes wastage of a substantial amount of energy in the [...] Read more.
The crude distillation unit (CDU) is one of the most energy-intensive processes of a petroleum refinery. The composition of crude is subject to change on regular basis. The uncertainty in crude oil composition causes wastage of a substantial amount of energy in the CDU operation. In this study, a novel approach based on a multi-output artificial neural networks (ANN) model was devised to cope with variations (uncertainty) in crude composition. The proposed method is an extended version of another method of cut-point optimization based on hybridization of Taguchi and genetic algorithm. A data comprised of several hundred variations of crude compositions and their optimized cut point temperatures, derived from the hybrid approach, was used to train the ANN model. The proposed method was validated on a simulated CDU flowsheet for a Pakistani crude, i.e., Zamzama. The proposed method is faster and computationally less expensive than the hybrid method. In addition, it can efficiently predict optimum cut point temperatures for any variant of the crude composition. Full article
(This article belongs to the Special Issue Energy Efficiency and Data-Driven Control)
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