Support vector regression model predictive control on a HVAC plant

Xi, Xue-Cheng; Poo, A.N.; Chou, S.K.

doi:10.1016/j.conengprac.2006.10.010

Cited by 131 publications

(64 citation statements)

References 21 publications

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“…(10) Transient response improvement (decrease in rise time, settling time, and peak time) [63,[66][67][68]. (11) Reduction in fluctuations from a set-point (better regulation) [56].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

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Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

et al. 2018

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Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.

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“…(10) Transient response improvement (decrease in rise time, settling time, and peak time) [63,[66][67][68]. (11) Reduction in fluctuations from a set-point (better regulation) [56].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

“…[64,65]. (4) Steady state response improvement (decrease in offset error) [58,66]. (5) Prediction on upcoming disturbance [9].…”

Section: Model Predictive Control (Mpc)mentioning

confidence: 99%

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

et al. 2018

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“…LS-SVM takes equality in instead of inequality constrains of SVM in the problem formulation such that LS-SVM is easy to train, which promotes the applications of LS-SVM and many function approximation and nonlinear control problems have been tackled with LS-SVM in the last decades [3,4,5,6]. However, those works lack the definite stability proof of the closed loop system using LS-SVM approaches.…”

Section: Introductionmentioning

confidence: 99%

Direct Adaptive H∞ Control for a Class of Nonlinear Systems based on LS-SVM

Zhao¹,

Xie²,

Wang³

2014

Appl. Math. Inf. Sci.

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A scheme of direct adaptive H ∞ control based on least squares support vector machines (LS-SVM) is proposed for a class of nonlinear uncertain systems. In this method, LS-SVM is employed to construct the adaptive controller, and an on-line learning rule for the weighting vector and bias is derived. A parameter selection method based on the genetic algorithm (GA) is given for LS-SVM regression with Gauss kernel. H ∞ control is used to attenuate the effect on the tracking error caused by LS-SVM approximation errors and external disturbances. Lyapunov theory is used to prove the uniformly ultimately bounded stability of the close-loop system. The simulation result shows the effectiveness and feasibility of the proposed method.

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“…The NMPC design was able to adequately regulate t he display air case temperatures and suction pressure at desired setting for a simulated supermarket refrigeration system . Xi et al [2007] regulated an experimental HVAC using a NMPC design. T he HVAC system consisted of a chiller as t he primary unit which cooled a water medium which was subsequently sent to an air handling unit (AHU) where the zone air would be blown over the water to enable cooling of the air.…”

Section: Sarabia Et Al [2007] Used a Non-linear Model Predictive Conmentioning

confidence: 99%

Energy efficient model predictive building temperature control

Wallace

McBride

Aumi

et al. 2012

Chemical Engineering Science

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Sustainability consider ations have placed increasing emphasis on t he energy efficient operation and control of temperature control systems. It is estimated t hat t he use of advanced control structures could lead to valuable savings in energy expenditure (up to 15-20 %) .This work considers t he problem of developing a model predictive control (NIP C) algorit hm for temperature cont rol in buildings . To this end, a cascade control structure was designed to regulate t he room temperature subj ect to heat load disturbances, such as outdoor condit ions or cha nges in the internal gains (i .e., number of people in a room). The inner loop of t he cascade control structure involved controlling key variables of a vapor compression cycle (VCC), namely the superheat and supply air temperature (from the evaporator), by manipulating t he compressor speed and valve opening (components in the VCC). Linear inputoutput models were appropriately identified for the vee using a detailed first-principles model (adapted from T hermosys) for event ual utilization in a predictive control design .Then, closed loop simulations were performed by interfacing the VCC model wit h EnergyPlus (developed by the U.S . Department of Energy) , which was used to model realistic room temperature behavior. The control performance using a predictive controller (in t he inner loop) was t hen evaluated against PI control.

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Support vector regression model predictive control on a HVAC plant

Cited by 131 publications

References 21 publications

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

Direct Adaptive H∞ Control for a Class of Nonlinear Systems based on LS-SVM

Energy efficient model predictive building temperature control

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