Development and Validation of Online Parameter Estimation for HVAC Systems

2002 ◽  
Author(s):  
Jin Wen ◽  
Theodore F. Smith
Keyword(s):  
Parameter Estimation ◽  
Thermal Load ◽  
Least Squares Method ◽  
Hvac Systems ◽  
Recursive Least Squares ◽  
Thermal Loads ◽  
Variable Air Volume ◽  
Improve Energy Efficiency ◽  
System Parameters ◽  
Occupant Comfort

Heating, ventilating, and air-conditioning systems of buildings consume nearly 50 percent of the world’s energy. To improve energy efficiency, to increase occupant comfort, and to provide better system operation and control for these systems, online estimation of system parameters, including system thermophysical parameters and thermal loads, is desirable. Several reported studies have presented simulation results and assumed that the thermal loads are known. A difficulty in HVAC system parameter estimation is that most HVAC systems are nonlinear, have multiple and time varying parameters, and require an estimate of the thermal load for a building zone. In this study, the building zone and variable-air-volume unit are modeled. The system parameters including the thermal load are estimated using the recursive-least-squares method with variable forgetting factor. The sensitivity of the estimation results to different factors is examined. Different experiments are used to validate the estimation results. The comparisons between the experiments and the estimation results show good agreement.

Development and Validation of Online Parameter Estimation for HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 324-330 ◽  
Author(s):  
Jin Wen ◽  
Theodore F. Smith
Keyword(s):  
Energy Efficiency ◽  
Parameter Estimation ◽  
Hvac Systems ◽  
Recursive Least Squares ◽  
Volume Discharge ◽  
Thermal Loads ◽  
Variable Air Volume ◽  
Improve Energy Efficiency ◽  
System Parameters ◽  
Air Volume

Improving the energy efficiency of buildings by examining their heating, ventilating, and air-conditioning (HVAC) systems represents an opportunity. To improve energy efficiency, to increase occupant comfort, and to provide better system operation and control algorithms for these systems, online estimation of system parameters, including system thermophysical parameters and thermal loads, is desirable. Several reported studies have presented simulation results and assumed that the thermal loads are known. A difficulty in HVAC system parameter estimation is that most HVAC systems are nonlinear, have multiple and time varying parameters, and require an estimate of the thermal loads for a building zone. In this study, building zones and variable-air-volume units are modeled. The system parameters including the thermal loads are estimated using the recursive-least-squares method with a variable forgetting factor. The sensitivity of the estimation results to different factors is examined. The estimated parameters are used to predict the zone and variable-air-volume-discharge-air temperatures. Several experiments are used to validate the prediction results. The comparisons show good agreement between the experiments and the prediction results.

Development and Validation of Online Models With Parameter Estimation for a Building Zone With VAV System

2004 ◽  
Author(s):  
Jin Wen ◽  
Theodore F. Smith
Keyword(s):  
Parameter Estimation ◽  
Air Flow ◽  
Hvac Systems ◽  
Test Facility ◽  
Prediction Errors ◽  
Commercial Building ◽  
Variable Air Volume ◽  
Online System ◽  
System Models ◽  
Validation Tests

The energy consumption by building heating, ventilating, and air conditioning (HVAC) systems has evoked more attention for energy efficient HVAC control and operation. Application of advanced control and operation strategies requires robust online system models. In this research, online models with parameter estimation for a building zone with variable air volume (VAV) system, which is one of the most common HVAC systems, are developed and validated using experimental data. Building zone temperature and VAV entering air flow are modeled based on physical rules and using only the measurements that are commonly available in a commercial building. Different series of validation tests were performed in a real-building test facility to examine the prediction accuracies for system outputs. Using the online system models with parameter estimation, the prediction errors for all the validation tests are less than 0.5°F for temperature outputs, and less than 50 ft3/min for air flow outputs. The online models can be further used for local and supervisory control, as well as fault detection applications.

Online parameter estimation and adaptive control with limited information in structures

2021 ◽  
pp. 107754632110191
Author(s):  
Fereidoun Amini ◽  
Elham Aghabarari
Keyword(s):  
Parameter Estimation ◽  
Adaptive Control ◽  
Least Squares ◽  
Control Algorithm ◽  
Least Squares Method ◽  
Recursive Least Squares ◽  
Limited Information ◽  
Online Identification ◽  
Recursive Least Squares Method ◽  
Adaptive Control Algorithm

An online parameter estimation is important along with the adaptive control, that is, a time-dependent plant. This study uses both online identification and the simple adaptive control algorithm with velocity feedback. The recursive least squares method was used to identify the stiffness and damping parameters of the structure’s stories. Identification was carried out online without initial estimation and only by measuring the structural responses. The limited information regarding sensor measurements, parameter convergence, and the effects of the covariance matrix is examined. The integration of the applied online identification, the appropriate reference model selection in simple adaptive control, and adopting the proportional integral filter was used to limit the structural control response error. Some numerical examples are simulated to verify the ability of the proposed approach. Despite the limited information, the results show that the simultaneous use of online identification with the recursive least squares method and simple adaptive control algorithm improved the overall structural performance.

scholarly journals A review of common human errors in design, installation, and operation of multiple-zone VAV AHU systems

2021 ◽  
Vol 2042 (1) ◽  
pp. 012130
Author(s):  
Narges Torabi ◽  
H. Burak Gunay ◽  
William O’Brien
Keyword(s):  
Hvac Systems ◽  
Review Of The Literature ◽  
Human Errors ◽  
Variable Air Volume ◽  
Air Volume ◽  
Air Handling Units ◽  
Occupant Comfort ◽  
Building Life Cycle ◽  
The Impact ◽  
Design Construction

Abstract Faults in air-based heating, ventilation, and air conditioning (HVAC) systems lead to energy waste and discomfort. While the emphasis of fault detection and diagnostic (FDD) research has been on hard faults in actuators, sensors, and equipment, faults arising from human errors account for a significant portion of faults occurring in HVAC systems. In this paper, human errors occurring in air handling units (AHUs) and variable air volume (VAV) thermal zones during design, construction, and operation phases are identified through a review of the literature. Then, the faults are divided into six main categories. Based on case studies investigating these faults, the impact of each fault category on occupant comfort, energy consumption, and equipment life is discussed. The authors provide recommendations to minimize human errors in AHUs and VAV zones throughout the building life cycle.

scholarly journals Parameter Estimation of Induction Machine at Standstill Using Two-Stage Recursive Least Squares Method

2015 ◽  
Vol 2015 ◽  
pp. 1-13
Author(s):  
Jinliang Zhang ◽  
Longyun Kang ◽  
Lingyu Chen ◽  
Zhihui Xu
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Least Squares Method ◽  
Induction Machine ◽  
Recursive Least Squares ◽  
Two Stage ◽  
Electric Parameter ◽  
Rls Algorithm ◽  
Effective Implementation ◽  
Recursive Least Squares Method

This paper presents a two-stage recursive least squares (TSRLS) algorithm for the electric parameter estimation of the induction machine (IM) at standstill. The basic idea of this novel algorithm is to decouple an identifying system into two subsystems by using decomposition technique and identify the parameters of each subsystem, respectively. The TSRLS is an effective implementation of the recursive least squares (RLS). Compared with the conventional (RLS) algorithm, the TSRLS reduces the number of arithmetic operations. Experimental results verify the effectiveness of the proposed TSRLS algorithm for parameter estimation of IMs.

scholarly journals Novel Optimization-Based Parameter Estimation Method for the Bass Diffusion Model

SAGE Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 215824402110269
Author(s):  
Lang Liang
Keyword(s):  
Parameter Estimation ◽  
Least Squares Method ◽  
Weighted Least Squares ◽  
Estimation Method ◽  
Reference Value ◽  
Diffusion Problem ◽  
Bass Model ◽  
Weighted Least Squares Method ◽  
Estimated Parameters ◽  
Bass Diffusion Model

The Bass model is the most popular model for forecasting the diffusion process of a new product. However, the controlling parameters in it are unknown in practice and need to be determined in advance. Currently, the estimation of the controlling parameters has been approached by various techniques. In this case, a novel optimization-based parameter estimation (OPE) method for the Bass model is proposed in the theoretical framework of system dynamics ( SD). To do this, the SD model of the Bass differential equation is first established and then the corresponding optimization mathematical model is formulated by introducing the controlling parameters as design variable and the discrepancy of the adopter function to the reference value as objective function. Using the VENSIM software, the present SD optimization model is solved, and its effectiveness and accuracy are demonstrated by two examples: one involves the exact solution and another is related to the actual user diffusion problem from Chinese Mobile. The results show that the present OPE method can produce higher predicting accuracy of the controlling parameters than the nonlinear weighted least squares method and the genetic algorithms. Moreover, the reliability interval of the estimated parameters and the goodness of fitting of the optimal results are given as well to further demonstrate the accuracy of the present OPE method.

scholarly journals Determination of synchronous generator nonlinear model parameters based on power rejection tests using a gradient optimization algorithm

2017 ◽  
Vol 65 (4) ◽  
pp. 479-488 ◽  
Author(s):  
A. Boboń ◽  
A. Nocoń ◽  
S. Paszek ◽  
P. Pruski
Keyword(s):  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Least Squares Method ◽  
Synchronous Generator ◽  
Operating Conditions ◽  
Model Parameters ◽  
State Variables ◽  
Starting Point ◽  
Gradient Optimization

AbstractThe paper presents a method for determining electromagnetic parameters of different synchronous generator models based on dynamic waveforms measured at power rejection. Such a test can be performed safely under normal operating conditions of a generator working in a power plant. A generator model was investigated, expressed by reactances and time constants of steady, transient, and subtransient state in the d and q axes, as well as the circuit models (type (3,3) and (2,2)) expressed by resistances and inductances of stator, excitation, and equivalent rotor damping circuits windings. All these models approximately take into account the influence of magnetic core saturation. The least squares method was used for parameter estimation. There was minimized the objective function defined as the mean square error between the measured waveforms and the waveforms calculated based on the mathematical models. A method of determining the initial values of those state variables which also depend on the searched parameters is presented. To minimize the objective function, a gradient optimization algorithm finding local minima for a selected starting point was used. To get closer to the global minimum, calculations were repeated many times, taking into account the inequality constraints for the searched parameters. The paper presents the parameter estimation results and a comparison of the waveforms measured and calculated based on the final parameters for 200 MW and 50 MW turbogenerators.

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