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Journal articles on the topic 'Supervisory model predictive control'

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Published: 4 June 2021
Last updated: 7 February 2022

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1

Tatjewski, Piotr. "Supervisory predictive control and on-line set-point optimization." International Journal of Applied Mathematics and Computer Science 20, no. 3 (September 1, 2010): 483–95. http://dx.doi.org/10.2478/v10006-010-0035-1.

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Supervisory predictive control and on-line set-point optimizationThe subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
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2

Jin, Jionghua, Huairui Guo, and Shiyu Zhou. "Statistical Process Control Based Supervisory Generalized Predictive Control of Thin Film Deposition Processes." Journal of Manufacturing Science and Engineering 128, no. 1 (December 15, 2004): 315–25. http://dx.doi.org/10.1115/1.2114912.

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This paper presents a supervisory generalized predictive control (GPC) by combining GPC with statistical process control (SPC) for the control of the thin film deposition process. In the supervised GPC, the deposition process is described as an ARMAX model for each production run and GPC is applied to the in situ thickness-sensing data for thickness control. Supervisory strategies, developed from SPC techniques, are used to monitor process changes and estimate the disturbance magnitudes during production. Based on the SPC monitoring results, different supervisory strategies are used to revise the disturbance models and the control law in the GPC to achieve a satisfactory control performance. A case study is provided to demonstrate the developed methodology.
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Meyer, Kristian, Thomas Bisgaard, Jakob K. Huusom, and Jens Abildskov. "Supervisory Model Predictive Control of the Heat Integrated Distillation Column." IFAC-PapersOnLine 50, no. 1 (July 2017): 7375–80. http://dx.doi.org/10.1016/j.ifacol.2017.08.1506.

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4

Kobayashi, Takahiro, and Tetsuji Tani. "Application of Cooperative Control to Petroleum Plants Using Fuzzy Supervisory Control and Model Predictive Multi-variable Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (November 20, 2001): 333–37. http://dx.doi.org/10.20965/jaciii.2001.p0333.

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This paper describes hierarchical control with fuzzy supervisory control and model predictive multivariable control (MPC) in a petroleum plant. MPC is effective in time delay, interference, and handling constraints. Fuzzy logic controllers are effective for plants with large time delay and non-linearity. Our proposed hierarchical control combines their advantages. Fuzzy supervisory control, which determines set points for MPC, consists of an estimation block and a compensation block. We use a statistical model with multi-regression analysis for the estimation block to estimate parameters of plant operation, and fuzzy logic for the compensation block to correct output of the statistical model. Hierarchical control has been applied to an actual plant in an oil refinery, and showed satisfactory performance.
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Papangelis, Lampros, Marie-Sophie Debry, Patrick Panciatici, and Thierry Van Cutsem. "Coordinated Supervisory Control of Multi-Terminal HVDC Grids: A Model Predictive Control Approach." IEEE Transactions on Power Systems 32, no. 6 (November 2017): 4673–83. http://dx.doi.org/10.1109/tpwrs.2017.2659781.

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Creemers, Falco, Alejandro Ivan Morales Medina, Erjen Lefeber, and Nathan van de Wouw. "Design of a supervisory controller for Cooperative Intersection Control using Model Predictive Control." IFAC-PapersOnLine 51, no. 33 (2018): 74–79. http://dx.doi.org/10.1016/j.ifacol.2018.12.096.

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7

Li, Su Zhen, Xiang Jie Liu, and Gang Yuan. "Application of Supervisory Predictive Control Based on T-S Model in pH Neutralization Process." Applied Mechanics and Materials 511-512 (February 2014): 867–70. http://dx.doi.org/10.4028/www.scientific.net/amm.511-512.867.

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T-S model is linearized at sampling points into the form of linear time-invariant state space , and using supervisory predictive control and muti-step predictive control strategy, which reduces amount of calculation and improves the control performance. Introduction
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8

Grosso, J. M., C. Ocampo-Martínez, and V. Puig. "Learning-based tuning of supervisory model predictive control for drinking water networks." Engineering Applications of Artificial Intelligence 26, no. 7 (August 2013): 1741–50. http://dx.doi.org/10.1016/j.engappai.2013.03.003.

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9

Rosen, C., M. Larsson, U. Jeppson, and Z. Yuan. "A framework for extreme-event control in wastewater treatment." Water Science and Technology 45, no. 4-5 (February 1, 2002): 299–308. http://dx.doi.org/10.2166/wst.2002.0610.

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In this paper an approach to extreme event control in wastewater treatment plant operation by use of automatic supervisory control is discussed. The framework presented is based on the fact that different operational conditions manifest themselves as clusters in a multivariate measurement space. These clusters are identified and linked to specific and corresponding events by use of principal component analysis and fuzzy c-means clustering. A reduced system model is assigned to each type of extreme event and used to calculate appropriate local controller set points. In earlier work we have shown that this approach is applicable to wastewater treatment control using look-up tables to determine current set points. In this work we focus on the automatic determination of appropriate set points by use of steady state and dynamic predictions. The performance of a relatively simple steady-state supervisory controller is compared with that of a model predictive supervisory controller. Also, a look-up table approach is included in the comparison, as it provides a simple and robust alternative to the steady-state and model predictive controllers. The methodology is illustrated in a simulation study.
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10

Morales-Rodelo, Keidy, Mario Francisco, Hernan Alvarez, Pastora Vega, and Silvana Revollar. "Collaborative Control Applied to BSM1 for Wastewater Treatment Plants." Processes 8, no. 11 (November 16, 2020): 1465. http://dx.doi.org/10.3390/pr8111465.

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This paper describes a design procedure for a collaborative control structure in Plant Wide Control (PWC), taking into account the existing controllable parameters as a novelty in the procedure. The collaborative control structure includes two layers, supervisory and regulatory, which are determined according to the dynamics hierarchy obtained by means of the Hankel matrix. The supervisory layer is determined by the main dynamics of the process and the regulatory layer comprises the secondary dynamics and controllable parameters. The methodology proposed is applied to a wastewater treatment plant, particularly to the Benchmark Simulation Model No 1 (BSM1) for the activated sludge process, comparing the results with the use of a Model Predictive Controller in the supervisory layer. For determining controllable parameters in the BSM1 control, a new specific oxygen mass transfer model in the biological reactor has been developed, separating the kLa volumetric mass transfer coefficient into two controllable parameters, kL and a.
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Kontes, Georgios, Georgios Giannakis, Víctor Sánchez, Pablo de Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios Rovas, Simone Steiger, Christopher Mutschler, and Gunnar Gruen. "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings." Energies 11, no. 12 (December 2, 2018): 3376. http://dx.doi.org/10.3390/en11123376.

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Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.
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Ouammi, Ahmed, Yasmine Achour, Driss Zejli, and Hanane Dagdougui. "Supervisory Model Predictive Control for Optimal Energy Management of Networked Smart Greenhouses Integrated Microgrid." IEEE Transactions on Automation Science and Engineering 17, no. 1 (January 2020): 117–28. http://dx.doi.org/10.1109/tase.2019.2910756.

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13

Flor Unda, Omar. "Adaptive control systems for solar collectors." Athenea 2, no. 4 (June 15, 2021): 19–25. http://dx.doi.org/10.47460/athenea.v2i4.18.

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En este trabajo se presentan las estrategias de control del flujo de aceite mediante la técnica de Control Predictivo basado en Modelo, para el mecanismo de control del campo de colectores solares cilindros parabólicos. Se analiza el comportamiento dinámico del sistema con el uso del modelo matemático, una técnicade control self-tunning y controlador predictivo basado en modelo para el control de plantas tipo ACUREX. Keywords: Automation, Modernization, ControlLogix, Supervisory System, Mimic Panel. References [1]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1997. Nonlinear neural model-based predictive control of a solar plant. In Proc. European Control Conf. ECC'97. Brussels, Belgium, Volumen TH-E I2, p. paper 264. [2]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998a. Comparison of RBF algorithms for output temperature prediction of a solar plant.. In Proc. CONTROLO'98, 9-11 September. [3]Arahal, M. R., Berenguel, M. & Camacho, E. F., 1998b. Neural identification applied to predictive control of solar plant. Control Engineering Practice, Volumen 6, pp. pp. 333-344. [4]Aström, K. J. & Wittenmark, B., 1989. Adaptative Control. Aström, K. J. & Wittermark, B., 1984. Computed controlles Systems, Theory and Design. Englewood Cliffs, NJ: Prentice Hall. [5]Barão, M., 2000. Dynamic and no-linear control of a solar collector field. Thesis (in Portuguese). Universidade Técnica de Lisboa, Instituto Superior Técnico. [6]Barão, M., Lemos, J. M. & Silva, R. N., 2002. Reduced complexity adaptative nonlinear control of a distribuited collector solar field. J. of Process Control, Volumen 12(1), pp. pp. 131-141. [7]Berenguel, M., Arahal, M. R. & Camacho, E. F., 1998. Modeling free responses of a solar plant for predictive control. Control Engineering Practice, Volumen 6, pp. pp. 1257-1266. [8]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1994. Simulation software package for the Acurex field.. Departamento de Ingeniería y Automática. [9]Berenguel, M., Camacho, E. F. & Rubio, F. R., 1997. Advanced Control of Solar Plants. Londres: Springer-Verlag.
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14

May-Ostendorp, Peter T., Gregor P. Henze, Balaji Rajagopalan, and Charles D. Corbin. "Extraction of supervisory building control rules from model predictive control of windows in a mixed mode building." Journal of Building Performance Simulation 6, no. 3 (May 2013): 199–219. http://dx.doi.org/10.1080/19401493.2012.665481.

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15

Löhr, Yannik, Daniel Wolf, Clemens Pollerberg, Alexander Hörsting, and Martin Mönnigmann. "Supervisory model predictive control for combined electrical and thermal supply with multiple sources and storages." Applied Energy 290 (May 2021): 116742. http://dx.doi.org/10.1016/j.apenergy.2021.116742.

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16

Wagenpfeil, J., E. Arnold, H. Linke, and O. Sawodny. "Modelling and optimized water management of artificial inland waterway systems." Journal of Hydroinformatics 15, no. 2 (December 18, 2012): 348–65. http://dx.doi.org/10.2166/hydro.2012.163.

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A decision support system (DSS) for optimized operational water management of artificial inland waterways is presented. It will be deployed as part of a supervisory control and data acquisition (SCADA) system of the Mittellandkanal (MLK), a large canal structure in northern Germany, and relies on experience gained from a similar system. The DSS uses a model predictive controller with a 48 h prediction horizon to calculate optimal pump and discharge strategies that will ensure navigable water levels and at the same time minimize operational costs. The internal process model for the model predictive controller is obtained from a numerical integration of the Saint Venant equations using Godunov's method. The initial state needed for an accurate prediction is estimated using moving horizon state estimation (MHE) or unscented Kalman filtering. Additionally, the state estimation methods are used to estimate non-measurable disturbance inflows, which may have a strong impact on the control performance if not compensated for by the model predictive controller. The optimal control strategy is transformed into discrete-valued pump and discharge jobs that account for technical and operational input constraints. Closed-loop simulations with a high-resolution hydrodynamic numerical model of the MLK illustrate the ability of the control algorithm to adapt to model uncertainties and non-controllable inputs.
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17

Boussemart, Yves, and Mary L. Cummings. "Predictive models of human supervisory control behavioral patterns using hidden semi-Markov models." Engineering Applications of Artificial Intelligence 24, no. 7 (October 2011): 1252–62. http://dx.doi.org/10.1016/j.engappai.2011.04.008.

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18

Breslow, Leonard A., Daniel Gartenberg, J. Malcolm McCurry, and J. Gregory Trafton. "Dynamic Operator Overload: A Model for Predicting Workload During Supervisory Control." IEEE Transactions on Human-Machine Systems 44, no. 1 (February 2014): 30–40. http://dx.doi.org/10.1109/tsmc.2013.2293317.

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19

Ramamurthi, K., and C. L. Hough. "Intelligent Real-Time Predictive Diagnostics for Cutting Tools and Supervisory Control of Machining Operations." Journal of Engineering for Industry 115, no. 3 (August 1, 1993): 268–77. http://dx.doi.org/10.1115/1.2901660.

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Machining economics may be improved by automating the replacement of cutting tools. In-process diagnosis of the cutting tool using multiple sensors is essential for such automation. In this study, an intelligent real-time diagnostic system is developed and applied towards that objective. A generalized Machining Influence Diagram (MID) is formulated for modeling different modes of failure in conventional metal cutting processes. A faster algorithm for this model is developed to solve the diagnostic problem in real-time applications. A formal methodology is outlined to tune the knowledge base during training with a reduction in training time. Finally, the system is implemented on a drilling machine and evaluated on-line. The on-line response is well within the desired response time of actual production lines. The instance and the accuracy of diagnosis are quite promising. In cases where drill wear is not diagnosed in a timely manner, the system predicts wear induced failure and vice versa. By diagnosing at least one of the two failure modes, the system is able to prevent any abrupt failure of the drill during machining.
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20

Milla, Freddy, Manuel A. Duarte-Mermoud, and Noreys Aguila-Camacho. "Hierarchical MPC Secondary Control for Electric Power System." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/397567.

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Although in electric power systems (EPS) the regulatory level guarantees a bounded error between the reference and the corresponding system variables, to keep its availability in time, optimizing the system operation is required for operational reasons such as, economic and/or environmental. In order to do this, there are the following alternative solutions: first, replacing the regulatory system with an optimized control system or simply adding an optimized supervisory level, without modifying the regulatory level. However, due to the high cost associated with the modification of regulatory controllers, the industrial sector accepts more easily the second alternative. In addition, a hierarchical supervisory control system improves the regulatory level through a new optimal signal support, without any direct intervention in the already installed regulatory control system. This work presents a secondary frequency control scheme in an electric power system, through a hierarchical model predictive control (MPC). The regulatory level, corresponding to traditional primary and secondary control, will be maintained. An optimal additive signal is included, which is generated from a MPC algorithm, in order to optimize the behavior of the traditional secondary control system.
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21

Aboelhassan, Ahmed, M. Abdelgeliel, Ezz Eldin Zakzouk, and Michael Galea. "Design and Implementation of Model Predictive Control Based PID Controller for Industrial Applications." Energies 13, no. 24 (December 14, 2020): 6594. http://dx.doi.org/10.3390/en13246594.

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Advanced control approaches are essential for industrial processes to enhance system performance and increase the production rate. Model Predictive Control (MPC) is considered as one of the promising advanced control algorithms. It is suitable for several industrial applications for its ability to handle system constraints. However, it is not widely implemented in the industrial field as most field engineers are not familiar with the advanced techniques conceptual structure, the relation between the parameter settings and control system actions. Conversely, the Proportional Integral Derivative (PID) controller is a common industrial controller known for its simplicity and robustness. Adapting the parameters of the PID considering system constraints is a challenging task. Both controllers, MPC and PID, merged in a hierarchical structure in this work to improve the industrial processes performance considering the operational constraints. The proposed control system is simulated and implemented on a three-tank benchmark system as a Multi-Input Multi-Output (MIMO) system. Since the main industrial goal of the proposed configuration is to be easily implemented using the available automation technology, PID controller is implemented in a PLC (Programable Logic Controller) controller as a lower controller level, while MPC controller and the adaptation mechanism are implemented within a SCADA (Supervisory Control And Data Acquisition) system as a higher controller level.
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22

Yahya, Olfa, Zeineb Lassoued, and Kamel Abderrahim. "Predictive Control Based on Fuzzy Supervisor for PWARX Hybrid Model." International Journal of Automation and Computing 16, no. 5 (October 1, 2018): 683–95. http://dx.doi.org/10.1007/s11633-018-1148-5.

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23

Howland, Michael F., and John O. Dabiri. "Influence of Wake Model Superposition and Secondary Steering on Model-Based Wake Steering Control with SCADA Data Assimilation." Energies 14, no. 1 (December 24, 2020): 52. http://dx.doi.org/10.3390/en14010052.

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Methods for wind farm power optimization through the use of wake steering often rely on engineering wake models due to the computational complexity associated with resolving wind farm dynamics numerically. Within the transient, turbulent atmospheric boundary layer, closed-loop control is required to dynamically adjust to evolving wind conditions, wherein the optimal wake model parameters are estimated as a function of time in a hybrid physics- and data-driven approach using supervisory control and data acquisition (SCADA) data. Analytic wake models rely on wake velocity deficit superposition methods to generalize the individual wake deficit to collective wind farm flow. In this study, the impact of the wake model superposition methodologies on closed-loop control are tested in large eddy simulations of the conventionally neutral atmospheric boundary layer with full Coriolis effects. A model for the non-vanishing lateral velocity trailing a yaw misaligned turbine, termed secondary steering, is also presented, validated, and tested in the closed-loop control framework. Modified linear and momentum conserving wake superposition methodologies increase the power production in closed-loop wake steering control statistically significantly more than linear superposition. While the secondary steering model increases the power production and reduces the predictive error associated with the wake model, the impact is not statistically significant. Modified linear and momentum conserving superposition using the proposed secondary steering model increase a six turbine array power production, compared to baseline control, in large eddy simulations by 7.5% and 7.7%, respectively, with wake model predictive mean absolute errors of 0.03P1 and 0.04P1, respectively, where P1 is the baseline power production of the leading turbine in the array. Ensemble Kalman filter parameter estimation significantly reduces the wake model predictive error for all wake deficit superposition and secondary steering cases compared to predefined model parameters.
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Ryu, Hyuncheol, and Jong Min Lee. "Model Predictive Control (MPC)-Based Supervisory Control and Design of Off-Gas Recovery Plant with Periodic Disturbances from Parallel Batch Reactors." Industrial & Engineering Chemistry Research 55, no. 11 (March 10, 2016): 3013–25. http://dx.doi.org/10.1021/acs.iecr.5b03224.

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25

Achour, Yasmine, Ahmed Ouammi, Driss Zejli, and Sami Sayadi. "Supervisory Model Predictive Control for Optimal Operation of a Greenhouse Indoor Environment Coping With Food-Energy-Water Nexus." IEEE Access 8 (2020): 211562–75. http://dx.doi.org/10.1109/access.2020.3037222.

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26

Rui, Yan Nian, Xiao Mei Jiang, and Kai Qiang Liu. "Intelligent Control in Low Pressure Methanol Carbonylation CH3COOH Reaction." Advanced Materials Research 233-235 (May 2011): 1027–30. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.1027.

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With catalysis system, carbonylation of methanol liquid is a better method in manufacture of acetic acid. However previous control mode can not well meet technique demands. This paper made some modification on reaction process control based on manufacturing technique. Aimed at much influence upon temperature including uncertainty, cross coupling effects and big delay about model, predictive functional control (PFC) technique combined with PID and feedforward are applied to temperature control of reaction process through supervisory total distributed control which leads to the improvement of regulatory capacity for both reference input tracking and load disturbance rejection. Practical results show stable and reliable system operation.
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Gwaivangmin, BI, and JD Jiya. "WATER DEMAND PREDICTION USING ARTIFICIAL NEURAL NETWORK FOR SUPERVISORY CONTROL." Nigerian Journal of Technology 36, no. 1 (December 29, 2016): 148–54. http://dx.doi.org/10.4314/njt.v36i1.19.

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With increase in population growth, industrial development and economic activities over the years, water demand could not be met in a water distribution network. Thus, water demand forecasting becomes necessary at the demand nodes. This paper presents Hourly water demand prediction at the demand nodes of a water distribution network using NeuNet Pro 2.3 neural network software and the monitoring and control of water distribution using supervisory control. The case study is the Laminga Water Treatment Plant and its water distribution network, Jos. The proposed model will be developed based on historic records of water demand in the 15 selected demand nodes for 60 days, 24 hours run. The data set is categorized into two set, one for training the neural network and the other for testing, with a learning rate of 50 and hidden nodes of 10 of the neural network model. The prediction results revealed a satisfactory performance of the neural network prediction of the water demand. The predictions are then used for supervisory control to remotely control and monitor the hydraulic parameters of the water demand nodes. The practical application in the plant will cut down the cost of water production and even to a large extend provide optimal operation of the distribution networks solving the perennial problem of water scarcity in Jos. http://dx.doi.org/10.4314/njt.v36i1.19
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Lee, Hyunjin, Bruce A. Buckingham, Darrell M. Wilson, and B. Wayne Bequette. "A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator." Journal of Diabetes Science and Technology 3, no. 5 (September 2009): 1082–90. http://dx.doi.org/10.1177/193229680900300511.

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The objective of this article is to present a comprehensive strategy for a closed-loop artificial pancreas. A meal detection and meal size estimation algorithm is developed for situations in which the subject forgets to provide a meal insulin bolus. A pharmacodynamic model of insulin action is used to provide insulin-on-board constraints to explicitly include the future effect of past and currently delivered insulin boluses. In addition, a supervisory pump shut-off feature is presented to avoid hypoglycemia. All of these components are used in conjunction with a feedback control algorithm using model predictive control (MPC). A model for MPC is developed based on a study of 20 subjects and is tested in a hypothetical clinical trial of 100 adolescent and 100 adult subjects using a Food and Drug Administration-approved diabetic subject simulator. In addition, a performance comparison of previously and newly proposed meal size estimation algorithms using 200 in silico subjects is presented. Using the new meal size estimation algorithm, the integrated artificial pancreas system yielded a daily mean glucose of 138 and 132 mg/dl for adolescents and adults, respectively, which is a substantial improvement over the MPC-only case, which yielded 159 and 145 mg/dl.
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29

Adegbenro, Akinkunmi, Michael Short, and Claudio Angione. "An Integrated Approach to Adaptive Control and Supervisory Optimisation of HVAC Control Systems for Demand Response Applications." Energies 14, no. 8 (April 8, 2021): 2078. http://dx.doi.org/10.3390/en14082078.

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Heating, ventilating, and air-conditioning (HVAC) systems account for a large percentage of energy consumption in buildings. Implementation of efficient optimisation and control mechanisms has been identified as one crucial way to help reduce and shift HVAC systems’ energy consumption to both save economic costs and foster improved integration with renewables. This has led to the development of various control techniques, some of which have produced promising results. However, very few of these control mechanisms have fully considered important factors such as electricity time of use (TOU) price information, occupant thermal comfort, computational complexity, and nonlinear HVAC dynamics to design a demand response schema. In this paper, a novel two-stage integrated approach for such is proposed and evaluated. A model predictive control (MPC)-based optimiser for supervisory setpoint control is integrated with a digital parameter-adaptive controller for use in a demand response/demand management environment. The optimiser is designed to shift the heating load (and hence electrical load) to off-peak periods by minimising a trade-off between thermal comfort and electricity costs, generating a setpoint trajectory for the inner loop HVAC tracking controller. The tracking controller provides HVAC model information to the outer loop for calibration purposes. By way of calibrated simulations, it was found that significant energy saving and cost reduction could be achieved in comparison to a traditional on/off or variable HVAC control system with a fixed setpoint temperature.
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Marusak, Piotr, and Piotr Tatjewski. "Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point Optimizers." International Journal of Applied Mathematics and Computer Science 18, no. 4 (December 1, 2008): 539–52. http://dx.doi.org/10.2478/v10006-008-0047-2.

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Actuator Fault Tolerance in Control Systems with Predictive Constrained Set-Point OptimizersMechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.
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Anuntasethakul, Chanthawit, and David Banjerdpongchai. "Design of Supervisory Model Predictive Control for Building HVAC System With Consideration of Peak-Load Shaving and Thermal Comfort." IEEE Access 9 (2021): 41066–81. http://dx.doi.org/10.1109/access.2021.3065083.

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32

Duan, Xuechao, Yuanying Qiu, Jianwei Mi, and Ze Zhao. "Motion prediction and supervisory control of the macro–micro parallel manipulator system." Robotica 29, no. 7 (April 11, 2011): 1005–15. http://dx.doi.org/10.1017/s0263574711000282.

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SUMMARYThis paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a canonical PID controller is discussed. In addition, the digital servo filters with feedforward are employed in the linear actuators as the lower layer controllers. Experimental results of a one-tenth scale FAST field model validate the effectiveness of the supervisory controller and the motion prediction algorithm.
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Franzè, Giuseppe, Angelo Furfaro, Massimiliano Mattei, and Valerio Scordamaglia. "A Safe Supervisory Flight Control Scheme in the Presence of Constraints and Anomalies." International Journal of Applied Mathematics and Computer Science 25, no. 1 (March 1, 2015): 39–51. http://dx.doi.org/10.1515/amcs-2015-0003.

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Abstract In this paper the hybrid supervisory control architecture developed by Famularo et al. (2011) for constrained control systems is adopted with the aim to improve safety in aircraft operations when critical events like command saturations or unpredicted anomalies occur. The capabilities of a low-computational demanding predictive scheme for the supervision of non-linear dynamical systems subject to sudden switchings amongst operating conditions and time-varying constraints are exploited in the flight control systems framework. The strategy is based on command governor ideas and is tailored to jointly take into account time-varying set-points/constraints. Unpredictable anomalies in the nominal plant behaviour, whose models fall in the category of time-varying constraints, can also be tolerated by the control scheme. In order to show the effectiveness of the proposed approach, simulations both on a high altitude performance demonstrator unmanned aircraft with redundant control surfaces and the P92 general aviation aircraft are discussed.
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Vilas Boas, Fernanda Mitchelly, Luiz Eduardo Borges-da-Silva, Helcio Francisco Villa-Nova, Erik Leandro Bonaldi, Levy Ely Lacerda Oliveira, Germano Lambert-Torres, Frederico de Oliveira Assuncao, et al. "Condition Monitoring of Internal Combustion Engines in Thermal Power Plants Based on Control Charts and Adapted Nelson Rules." Energies 14, no. 16 (August 11, 2021): 4924. http://dx.doi.org/10.3390/en14164924.

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In thermal power plants, the internal combustion engines are constantly subjected to stresses, requiring a continuous monitoring system in order to check their operating conditions. However, most of the time, these monitoring systems only indicate if the monitored parameters are in nonconformity close to the occurrence of a catastrophic failure—they do not allow a predictive analysis of the operating conditions of the machine. In this paper, a statistical model, based on the statistical control process and Nelson Rules, is proposed to analyze the operational conditions of the machine based on the supervisory system data. The statistical model is validated through comparisons with entries of the plant logbook. It is demonstrated that the results obtained with the proposed statistical model match perfectly with the entries of the logbook, showing our model to be a promising tool for making decisions concerning maintenance in the plant.
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Aswad Mohammed, Subhi, Osama Ali Awad, and Abdulkareem Merhej Radhi. "Optimization of energy consumption and thermal comfort for intelligent building management system using genetic algorithm." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 3 (December 1, 2020): 1613. http://dx.doi.org/10.11591/ijeecs.v20.i3.pp1613-1625.

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This paper presents a design, simulation and performance evaluation of an optimized model for the Heating, Ventilation and Air-Conditioning (HVAC) systems using intelligent control algorithm. Fanger’s comfort method and genetic algorithms were used to obtain the optimal and initial values. The heat transmission coefficient between internal and external environments were determined depending on several inputs and factors acquired via supervisory control and data acquisition (SCADA) system sensors. The main feature of the real-time model is the prediction of the internal buildings environment, in order to control HVAC system for indoor environment and to utilize the optimum power consumed depending on optimized air temperature value. The predicted air temperature value and Predictive Mean Vote (PMV) value was applied using intelligent algorithm to obtain an optimal comfort level of the air temperature. The optimized air temperature value can be used in HVAC system controller to ensure that the temperature of indoor can reach a specific value after a known period of time. The use of genetic algorithm (GE) ensures that the used power is well below its peak value and maintains the comfort of the user’s environment.
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Roscher, Björn, and Ralf Schelenz. "Usability of SCADA as predictive maintenance for wind turbines." Forschung im Ingenieurwesen 85, no. 2 (March 22, 2021): 173–80. http://dx.doi.org/10.1007/s10010-021-00454-1.

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AbstractWind energy is an essential source of renewable energy. However, to compete with conventional energy sources, energy needs to be produced at low costs. An ideal situation would be to have no costly, unscheduled maintenance, preferably. Currently, O&M are half of the yearly expenses. The O&M costs are kept low by scheduled and reactive maintenance. An alternative is predictive maintenance. This method aims to act before any critical and costly repair is required. Additionally, the component is used to its full potential. However, such a strategy requires a damage indication, similar to one provided by a condition monitoring system (CMS). This paper investigates if Supervisory Control and Data Acquisition (SCADA) can be used as a damage indicator and CMS. Since 2006, every wind turbine is obliged to use such a SCADA-system. SCADA records a 10-minute average, maximum, minimum, and standard deviation of multiple technical information channels. Analytics can use those data to determine the normal behavior and a prediction model of the wind turbine. The authors investigated statistical and data mining methods to predict main bearing faults. The methods indicated a defect of up to 6 months before its maintenance.
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Allen, James, Ari Halberstadt, John Powers, and Nael H. El-Farra. "An Optimization-Based Supervisory Control and Coordination Approach for Solar-Load Balancing in Building Energy Management." Mathematics 8, no. 8 (July 23, 2020): 1215. http://dx.doi.org/10.3390/math8081215.

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This work considers the problem of reducing the cost of electricity to a grid-connected commercial building that integrates on-site solar energy generation, while at the same time reducing the impact of the building loads on the grid. This is achieved through local management of the building’s energy generation-load balance in an effort to increase the feasibility of wide-scale deployment and integration of solar power generation into commercial buildings. To realize this goal, a simulated building model that accounts for on-site solar energy generation, battery storage, electrical vehicle (EV) charging, controllable lighting, and air conditioning is considered, and a supervisory model predictive control (MPC) system is developed to coordinate the building’s generation, loads and storage systems. The main aim of this optimization-based approach is to find a reasonable solution that minimizes the economic cost to the electricity user, while at the same time reducing the impact of the building loads on the grid. To assess this goal, three objective functions are selected, including the peak building load, the net building energy use, and a weighted sum of both the peak load and net energy use. Based on these objective functions, three MPC systems are implemented on the simulated building under scenarios with varying degrees of weather forecasting accuracy. The peak demand, energy cost, and electricity cost are compared for various forecast scenarios for each MPC system formulation, and evaluated in relation to a rules-based control scheme. The MPC systems tested the rules-based scheme based on simulations of a month-long electricity consumption. The performance differences between the individual MPC system formulations are discussed in the context of weather forecasting accuracy, operational costs, and how these impact the potential of on-site solar generation and potential wide-spread solar penetration.
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Iannace, Gino, Giuseppe Ciaburro, and Amelia Trematerra. "Wind Turbine Noise Prediction Using Random Forest Regression." Machines 7, no. 4 (November 6, 2019): 69. http://dx.doi.org/10.3390/machines7040069.

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Wind energy is one of the most widely used renewable energy sources in the world and has grown rapidly in recent years. However, the wind towers generate a noise that is perceived as an annoyance by the population living near the wind farms. It is therefore important to new tools that can help wind farm builders and the administrations. In this study, the measurements of the noise emitted by a wind farm and the data recorded by the supervisory control and data acquisition (SCADA) system were used to construct a prediction model. First, acoustic measurements and control system data have been analyzed to characterize the phenomenon. An appropriate number of observations were then extracted, and these data were pre-processed. Subsequently two models of prediction of sound pressure levels were built at the receiver: a model based on multiple linear regression, and a model based on Random Forest algorithm. As predictors wind speeds measured near the wind turbines and the active power of the turbines were selected. Both data were measured by the SCADA system of wind turbines. The model based on the Random Forest algorithm showed high values of the Pearson correlation coefficient (0.981), indicating a high number of correct predictions. This model can be extremely useful, both for the receiver and for the wind farm manager. Through the results of the model it will be possible to establish for which wind speed values the noise produced by wind turbines become dominant. Furthermore, the predictive model can give an overview of the noise produced by the receiver from the system in different operating conditions. Finally, the prediction model does not require the shutdown of the plant, a very expensive procedure due to the consequent loss of production.
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Schwanenberg, D., B. P. J. Becker, and M. Xu. "The open real-time control (RTC)-Tools software framework for modeling RTC in water resources sytems." Journal of Hydroinformatics 17, no. 1 (September 9, 2014): 130–48. http://dx.doi.org/10.2166/hydro.2014.046.

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Real-time control-Tools is a novel software framework for modeling real-time control and decision support in water resources systems. It integrates different control paradigms ranging from simple feedback control strategies with triggers, operating rules and controllers to advanced optimization-based approaches such as model predictive control (MPC). A key feature of the package is the modular integration of modeling components, related adjoint models, and optimization algorithms which makes it well suited for the control of large-scale water systems. Interfaces enable its integration into Supervisory Control and Data Acquisition systems, operational stream flow forecasting, and decision support systems as well as hydraulic modeling packages. This paper presents an overview of the novel software framework, gives an introduction into the underlying control theory for which it has been developed and discusses the related software architecture. A first case describes an innovative combination of binary decision trees and feedback control in application to the modeling of a highly regulated River Rhine reach along the German–French border. Two additional cases present the efficient application of MPC to the short-term management of two large-scale water systems in the Netherlands and the USA.
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Drewnowski, Jakub. "Advanced Supervisory Control System Implemented at Full-Scale WWTP—A Case Study of Optimization and Energy Balance Improvement." Water 11, no. 6 (June 11, 2019): 1218. http://dx.doi.org/10.3390/w11061218.

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In modern and cost-effective Wastewater Treatment Plants (WWTPs), processes such as aeration, chemical feeds and sludge pumping are usually controlled by an operating system integrated with online sensors. The proper verification of these data-driven measurements and the control of different unit operations at the same time has a strong influence on better understanding and accurately optimizing the biochemical processes at WWTP—especially energy-intensive biological parts (e.g., the nitrification zone/aeration system and denitrification zone/internal recirculation). In this study, by integrating a new powerful PreviSys with data driven from the Supervisory Control and Data Acquisition (SCADA) software and advanced algorithms such as Model Predictive Control (MPC) by using the WEST computer platform, it was possible to conduct different operation strategies for optimizing and improving the energy balance at a full-scale “Klimzowiec” WWTP located in Chorzow (Southern Poland). Moreover, the novel concept of double-checking online data-driven measurements (from installed DO, NO3, NH4 sensors, etc.) by mathematical modelling and computer simulation predictions was applied in order to check the data uncertainty and develop a support operator system (SOS)—an additional tool for the widely-used in-operation and control of modern and cost-effective WWTPs. The results showed that by using sophisticated PreviSys technology, a better understanding and accurate optimization of biochemical processes, as well as more sustainable WWTP operation, can be achieved.
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41

Deo, Bahma, and Satish Kumar. "Dynamic On-Line Control of Stainless Steel Making in AOD." Advanced Materials Research 794 (September 2013): 50–62. http://dx.doi.org/10.4028/www.scientific.net/amr.794.50.

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A new dynamic control model based on simultaneous mass transfer of C, Cr, and Mn and dynamic heat balance is developed. It allows dynamic adjustment of argon-oxygen ratio. The model is implemented through Level II control system. The total operating period of one heat is divided into five different stages: charge calculation, first blow period, second blow period, third blow period and, lastly, the reduction stage. The charge calculation model, based on heat balance, mass balance and the costs decided the optimum charge mix to start with. Both linear and non-linear regression models are used to predict the temperature and composition of bath at the end of first blow period. The second blow and the third blow periods use the dynamic models for predicting the chemical composition and temperature. In the model for reduction stage (final stage) the amount of reduction mixture is determined for obtaining maximum recovery of Cr and Mn from slag. On-line testing of the dynamic models was carried out on the shop floor. The integration of models with the Level II control system using Supervisory Control and Data Acquisition System (SCADA) are discussed. User friendly HMI are developed such that the operators can easily use it during the regular operation on shop-floor. This is the first time that a full dynamic control system was implemented in India for the AOD process.
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Al-Radhi, Mohammed Salah Hamza, Safa Jameel Dawood Al-Kamil, and Szakács Tamás. "A model-based machine learning to develop a PLC control system for Rumaila degassing stations." Journal of Petroleum Research and Studies 10, no. 4 (December 21, 2020): 1–18. http://dx.doi.org/10.52716/jprs.v10i4.364.

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Degassing station breakdowns can be dangerous to the operator health and the environment. Programmable logic controllers (PLCs) are key modules of manufacturing control systems that are applied in the complex oil and gas units to reduce manpower and unnecessary faults. However, feeding a PLC with data is a difficult part due to the need of system log files which records all events that occur in the oil fields and provide visibility to a given environment. Moreover, most critical chemical processing plants and oil distributions are visualized and inspected by Supervisory Control and Data Acquisition Systems (SCADA). These systems have been focused on safety, and there are issues that they could be the target of worldwide terrorists. Along with the frequently rising internet-related attacks, there is indication that our degassing stations may similarly be susceptible; for that reason, it is essential to secure PLC and SCADA from undesired incidents. Recently, machine learning (ML) has been increasing interest in industrial systems to detect, identify, and store information. Therefore, we propose to apply an advance ML based on deep neural networks to the PLC system with the purpose of: 1) detecting anomalous or irregular PLC actions; 2) Optimizing the operation of systems and its facilities; 3) allowing the equipment to respond to changing and novel scenarios; 4) Making predictive maintenance possible. The SIMATIC S7-1214 CPU universal TIA platform was used as the main decision-making module. Experimental results demonstrate the effectiveness and utility of the proposed approach to process large amounts of data analytics and sensor measurements, allows it to spot potential problems and provide possible solutions.
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He, Lei, Bo Lei, Haiquan Bi, and Tao Yu. "Simplified Building Thermal Model Used for Optimal Control of Radiant Cooling System." Mathematical Problems in Engineering 2016 (2016): 1–15. http://dx.doi.org/10.1155/2016/2976731.

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MPC has the ability to optimize the system operation parameters for energy conservation. Recently, it has been used in HVAC systems for saving energy, but there are very few applications in radiant cooling systems. To implement MPC in buildings with radiant terminals, the predictions of cooling load and thermal environment are indispensable. In this paper, a simplified thermal model is proposed for predicting cooling load and thermal environment in buildings with radiant floor. In this thermal model, the black-box model is introduced to derive the incident solar radiation, while the genetic algorithm is utilized to identify the parameters of the thermal model. In order to further validate this simplified thermal model, simulated results from TRNSYS are compared with those from this model and the deviation is evaluated based on coefficient of variation of root mean square (CV). The results show that the simplified model can predict the operative temperature with a CV lower than 1% and predict cooling loads with a CV lower than 10%. For the purpose of supervisory control in HVAC systems, this simplified RC thermal model has an acceptable accuracy and can be used for further MPC in buildings with radiation terminals.
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Betti, Alessandro, Mauro Tucci, Emanuele Crisostomi, Antonio Piazzi, Sami Barmada, and Dimitri Thomopulos. "Fault Prediction and Early-Detection in Large PV Power Plants Based on Self-Organizing Maps." Sensors 21, no. 5 (March 1, 2021): 1687. http://dx.doi.org/10.3390/s21051687.

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In this paper, a novel and flexible solution for fault prediction based on data collected from Supervisory Control and Data Acquisition (SCADA) system is presented. Generic fault/status prediction is offered by means of a data driven approach based on a self-organizing map (SOM) and the definition of an original Key Performance Indicator (KPI). The model has been assessed on a park of three photovoltaic (PV) plants with installed capacity up to 10 MW, and on more than sixty inverter modules of three different technology brands. The results indicate that the proposed method is effective in predicting incipient generic faults in average up to 7 days in advance with true positives rate up to 95%. The model is easily deployable for on-line monitoring of anomalies on new PV plants and technologies, requiring only the availability of historical SCADA data, fault taxonomy and inverter electrical datasheet.
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Luo, Zhikun, Zhifeng Sun, Fengli Ma, Yihan Qin, and Shihao Ma. "Power Optimization for Wind Turbines Based on Stacking Model and Pitch Angle Adjustment." Energies 13, no. 16 (August 12, 2020): 4158. http://dx.doi.org/10.3390/en13164158.

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As we know, power optimization for wind turbines has great significance in the area of wind power generation, which means to make use of wind resources more efficiently. Especially nowadays, wind power generation has become more and more important. Generally speaking, many parameters could be optimized to enhance power output, including blade pitch angle, which is usually ignored. In this article, a stacking model composed of Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBOOST) and Light Gradient Boosting Machine (LGBM) is trained based on historical data exported from the Supervisory Control and Data Acquisition (SCADA) system for output power prediction. Then, we carry out power optimization through pitch angle adjustment based on the obtained prediction model. Our research results indicate that power output could be enhanced by adjusting pitch angle appropriately.
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Wang, Xian, Qiancheng Zhao, Xuebing Yang, and Bing Zeng. "Condition monitoring of wind turbines based on analysis of temperature-related parameters in supervisory control and data acquisition data." Measurement and Control 53, no. 1-2 (December 2, 2019): 164–80. http://dx.doi.org/10.1177/0020294019888239.

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In order to conduct a further in-depth exploration of the role of temperature-related parameters in the condition monitoring of wind turbines, this paper proposes a method to assess the condition of wind turbines by analyzing the supervisory control and data acquisition system temperature-related parameters based on existing research. A prediction model of time-sequence regression is established, based on the key temperature signals of WTs, so as to reflect their health condition in the form of prediction residuals. A kind of health index from the perspective of temperature-related parameters is developed by separating the statistics concerning the conformity of the predicted values of key temperature parameters within a certain time window from the measured values in order to clearly present the implied information on the health condition of wind turbines contained in the model prediction residuals. The case study shows that the trend of health index from the perspective of temperature-related parameters is consistent with the health condition of wind turbines. In some instances, its decline obviously occurs earlier than the maintenance provided to address the stoppage, suggesting that such indexes can effectively reflect some early health problems of the wind turbines to provide a reference for their scientific maintenance.
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Vahidzadeh, Mohsen, and Corey D. Markfort. "An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions." Energies 13, no. 4 (February 17, 2020): 891. http://dx.doi.org/10.3390/en13040891.

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Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.
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Shao, Yan Chao, Liang Jun Xu, Yan Zhu Hu, and Xin Bo Ai. "Pressure Prediction in Natural Gas Desulfurization Process Based on PCA and SVR." Advanced Materials Research 962-965 (June 2014): 564–69. http://dx.doi.org/10.4028/www.scientific.net/amr.962-965.564.

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Pressure monitoring is an important means to reflect the running status of the natural gas desulphurization process. By using the data mining technology, the interaction relationships between the pressure and other monitoring parameters are analyzed in this paper. A pressure trend prediction model is established to show the pressure status in the natural gas desulfurization process. Firstly, the theory of Principal Component Analysis (PCA) is used to reduce the dimensions of measured data from traditional Supervisory Control and Data Acquisition (SCADA) system. Secondly the principal components are taken as input data into the pressure trend prediction model based on multiple regression theory of Support Vector Regression (SVR). Finally the accuracy and the generalization ability of the model are tested by the measured data obtained from SCADA system. Compared with other prediction models, pressure trend prediction model based on PCA and SVR gets smaller MSE and higher correlation. The pressure trend prediction model gets better generalization ability and stronger robustness, and is an effective complement to SCADA system in the natural gas desulphurization process.
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Gaska, Krzysztof, and Agnieszka Generowicz. "SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study." Energies 13, no. 13 (June 30, 2020): 3338. http://dx.doi.org/10.3390/en13133338.

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The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic objects with logical representation of knowledge about the process, the control object and the control itself, for which the learning process consists of successive validation and updating of knowledge and the use of the results of this updating to make control decisions. The advantage of the proposed solution in relation to the existing ones lies in the use of advanced models of predictive diagnostics, validation and reconstruction of data, implemented in functional tools, allowing the stabilization of the work of technological objects through the use of FTC technology (fault tolerant control) and soft sensors, predictive measurement path diagnostics (sensors, transducers), validation and reconstruction of measurement data from sensors in the measuring paths in real time. The dedicated tools (Intelligent Real Time Diagnostic System − iRTDS) built into the system of a hierarchical, multi-threaded control optimizing system of SCADA system allow to obtain advanced diagnostics of technological processes in real time using HPC technology. In effect of the application of the proprietary iRTDS tool, we obtain a significant rise of energy efficiency of technological processes in key sectors of the economy, which in global terms, e.g., urban agglomeration, increases the economic efficiency.
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Liu, Simeng, and Gregor P. Henze. "Evaluation of Reinforcement Learning for Optimal Control of Building Active and Passive Thermal Storage Inventory." Journal of Solar Energy Engineering 129, no. 2 (October 31, 2006): 215–25. http://dx.doi.org/10.1115/1.2710491.

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This paper describes an investigation of machine learning for supervisory control of active and passive thermal storage capacity in buildings. Previous studies show that the utilization of active or passive thermal storage, or both, can yield significant peak cooling load reduction and associated electrical demand and operational cost savings. In this study, a model-free learning control is investigated for the operation of electrically driven chilled water systems in heavy-mass commercial buildings. The reinforcement learning controller learns to operate the building and cooling plant based on the reinforcement feedback (monetary cost of each action, in this study) it receives for past control actions. The learning agent interacts with its environment by commanding the global zone temperature setpoints and thermal energy storage charging∕discharging rate. The controller extracts information about the environment based solely on the reinforcement signal; the controller does not contain a predictive or system model. Over time and by exploring the environment, the reinforcement learning controller establishes a statistical summary of plant operation, which is continuously updated as operation continues. The present analysis shows that learning control is a feasible methodology to find a near-optimal control strategy for exploiting the active and passive building thermal storage capacity, and also shows that the learning performance is affected by the dimensionality of the action and state space, the learning rate and several other factors. It is found that it takes a long time to learn control strategies for tasks associated with large state and action spaces.
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