Multi-circuit air-conditioning system modelling for temperature control

The suitable application of innovative control strategies in Heating, Ventilation, and Airconditioning systems is important to improving the energy efficiency and maintenance of temperature set point to improve thermal comfort in buildings. The increased focus on energy savings and appropriate thermal comfort has resulted in the necessity for more dynamic approach to the use of these controllers. However, the design of these controllers requires the use of an accurate dynamic modelling. Substantial progresses have been made in the past on model development to provide better control strategy to ensure energy savings without sacrificing thermal comfort and indoor air quality in the Heating, Ventilation, and Air-conditioning systems. However, there are scarce model using the data driven approach in the Multi-circuit air-conditioning system. This research, carried out a study on the choice of a dynamic model for an operating centralized multi-circuit water-cooled package unit air-conditioning system using a system identification procedure. Baseline data were collected and analyzed, the model development was achieved by processing, estimating and validating the data in system identification. Result shows that the Autoregressive-moving average with exogenous terms (ARMAX) of the third order model, established the best model structure with the highest Best Fit and Lowest Mean Square Error

Modeling, Analysis, and Design of a Fuzzy Logic Controller for an AHU in the S.J. Carew Building at Memorial University

Proper functioning of heating, ventilation, and air conditioning (HVAC) systems is important for efficient thermal management, as well as operational costs. Most of these systems use nonlinear time variances to handle disturbances, along with controllers that try to balance rise times and stability. The latest generation of fuzzy logic controllers (FLC) is algorithm-based and is used to control indoor temperatures, CO2 concentrations in air handling units (AHUs), and fan speeds. These types of controllers work through the manipulation of dampers, fans, and valves to adjust flow rates of water and air. In this paper, modulating equal percentage globe valves, fans speed, and dampers position have been modeled according to exact flow rates of hot water and air into the building, and a new approach to adapting FLC through the modification of fuzzy rules surface is presented. The novel system is a redesign of an FLC using MATLAB/Simulink, with the results showing an enhancement in thermal comfort levels

Energy Consumption Analysis of a Large Building at Memorial University

In this paper, energy consumption analysis and a process to identify appropriate models based on heat dynamics for large structures are presented. The analysis uses data from heating, ventilation, and air-conditioning (HVAC) system sensors, as well as data from the indoor climate and energy software (IDA Indoor Climate and Energy (IDA-ICE) 4.7 simulation program). Energy consumption data (e.g., power and hot water usage) agrees well with the new models. The model is applicable in a variety of applications, such as forecasting energy consumption and controlling indoor climate. In the study, both data-derived models and a grey-box model are tested, producing a complex building model with high accuracy. Also, a case study of the S. J. Carew building at Memorial University, St. John’s, Newfoundland, is presented

System identification for control of temperature and humidity in buildings

HVAC systems are widely used to provide a good indoor air quality in buildings. Buildings stand for a substantial part of the total energy consumption in developed countries, and with an increased focus on cost reductions and energy savings, it is necessary to use intelligent and energy-efficient controllers. Accurate models describing the dynamics of the building system is a good basis for intelligent control. In countries like Sweden there are large seasonal variations in the outdoor climate, and these variations interfere with the indoor condition and thus affects the control. In this thesis the seasonal variations are investigated, and the aim is to determine how these differences affect identified models for control of temperature and relative humidity in buildings. Two MISO (Multiple Input-Single Output) systems and one MIMO (Multiple Input-Multiple Output) system is used to describe the mean room temperature and relative humidity in a selected room in the Q-building at KTH, Stockholm. The models are identified following the black-box approach, and data from four different months during 2014, representing the winter, spring, summer and autumn season respectively, are collected and preprocessed. The validation of the identified models for the humidity and temperature, shows that it is possible to use identical orders and input delays for all seasons, with good results. Based on the results one would not recommend using models with the same model parameters throughout the year, since the conditions varies too much from season to season

Dynamic modeling and fuzzy logic control of a large building HVAC system

Energy and cost-efficient management of a building’s thermal properties requires heating, ventilation and air conditioning (HVAC) systems controllers to be working at optimal settings. However, many HVAC systems employ nonlinear time variances to deal with issues that affect the system’s optimal operation. The present work considers an HVAC system at Memorial University’s S. J. Carew Building which has been mathematically modeled using a state space multi-input and multi-output system (MIMO) approach for analyses and control system design. An IDA-ICE (Indoor Climate and Energy) simulation program has been applied for modeling the building, note that the four-story Carew Building includes an air-handling unit (AHU) on every floor. Compared with real data for one year’s (2016) power consumption, the simulated annual power consumption for the building shows good agreement. Based on that data, two scenarios are applied for building the system models. Scenario 1 considers the HVAC system as a single unit with energy consumption (kWh) as inputs and zonal temperature and CO2 concentrations as outputs. By employing the MATLAB system identification toolbox, a MIMO-based system forms the basis for a state space model. In the model for Scenario 1, there are eight main AHU inputs (hot water power usage and power usage) and eight main outputs (return airflow temperature and CO2 levels). The state feedback controller obtains good results for both responses rise time and stability. In Scenario 2, there are four AHUs in total. Each of this scenario’s AHUs features three main inputs (hot water, internal-to-internal air flow, and external-to-internal air flow) and three main outputs (static air pressure, CO2 levels, and temperature). In the first AHU (AHU1), we apply state-of-the-art fuzzy logic controllers (FLCs) to control fan speeds, CO2 concentrations, and temperature in the building in accordance with the flow rates for air and hot water. This strategy represents a novel approach for adapting FLCs by modifying fuzzy rule using the Simulink. The modified system shows improved levels of thermal comfort. The final part of the work presents the design for a supervisor fuzzy logic controller (SFLC) that can be applied to the entire S. J. Carew Building HVAC control. This SFLC features 24 inputs and 12 outputs and employs a state-space model that considers each AHU as an individual system. The SFLC detailed design and system simulation results are presented in this thesis

Dynamic modeling, validation, and control for vapor compression systems

This thesis traces the complete process of model-based control design for vapor compression systems (VCSs), from nonlinear model development to linearization and control formulation. Addressing gaps in the previous literature, the equations behind each model and control approach are clearly stated and emphasis is placed on conducting experimental validation at every stage. Both finite volume and switched moving boundary approaches for nonlinear control-oriented heat exchanger modeling are presented, illustrating the key differences in the method of discretization between these approaches. Practical considerations for the numerical implementation of these approaches in simulation are also provided. A detailed linearization of the switched moving boundary approach leads to the creation of a family of four-component linear models for different modes of operation of a VCS. The nonlinear and linear models are then validated with experimental data to reveal the tradeoffs of each. Furthermore, an augmentation to the switched moving boundary method is derived which captures the effects of air humidity. Experimental validation demonstrates that this augmented model more accurately predicts both air-side and refrigerant-side outputs at high humidity in addition to providing accurate predictions of liquid condensate formation and air outlet humidity. Finally, the value of the linear VCS models is demonstrated by their application in model-based control. A switched LQR approach is shown in both simulation and experimental application to be capable of driving the system between operational modes in order to regulate about a desired nominal operating condition. In particular, the experiments demonstrate improved robustness at low evaporator superheat of the switched LQR approach as compared to a decentralized PI approach

Hybrid Method for Dynamic Thermal Modelling of Buildings Based on the Resistance- Capacitance Analogy

U ovoj disertaciji istražena je izrada termodinamičkog modela zgrade i HVAC sustava, s namjerom da se model koristi kao temelj upravljanja u modelskom prediktivnom upravljanju, a sve s ciljem poboljšavanja energetske učinkovitosti u zgradi. Cilj istraživanja bio je razvoj metode za izradu hibridnog termodinamičkog modela zgrade tipa siva kutija koji se temelji na otporničkokapacitivnoj analogiji za određivanje strukture modela i te određivanju parametara modela na temelju raspoloživih mjernih podataka. Predstavljen je algoritam koji omogućuje automatiziranu izradu strukture termodinamičkog modela zgrade u obliku prostora stanja iz građevinskog nacrta zgrade. Početni parametri modela postavljaju se na temelju nazivnih podataka o svojstvima korištenih materijala, no u drugom koraku koriste se mjerenja sa zgrade za prilagodbu modela kroz prepodešavanje njegovih parametara. Za prilagodbu se koristi minimalizacija funkcije greške definirane razlikom između mjerenja i izlaza modela. Razvijena metoda i njome dobiveni modeli testirani su korištenjem mjernih podataka sa stvarne zgrade te uspoređeni s mjerenjima i rezultatima dobivenim pomoću umjetnih neuronskih mreža. Rezultati pokazuju da se predložena metoda može koristiti za automatiziranu izradu termodinamičkog modela zgrade, s rezultatima koji su dovoljno točni za korištenje u modelskom prediktivnom upravljanju, a usporedba pokazuje da hibridni model daje bolje rezultate od umjetnih neuronskih mreža.This thesis investigates the development of thermodynamic model of building and HVAC system, with purpose of using this model as a basis for Model Predictive Control, with goal of increasing the energy efficiency in buildings. The goal of the research was to develop a method for developing grey-box thermodynamic model of building based on resistance-capacitance analogy for structure of model and estimation of model parameters based on available measured data. It proposes an algorithm that enables automated development of structure of thermodynamic model in state-space representation based on construction drawing of building. Initial parameters of the model are based on nominal information of building materials' properties, but in second step, measured data from a building are used for fitting of model. The fitting is accomplished by minimization of error-function defined as difference between the measurements and outputs of the model. The method and developed models are tested with data from a real building and compared to measurements and results from Artificial Neural Network. Results show that proposed method enables automated development of thermal model of building, with results acceptable for use in Model Predictive Control, while comparison shows that hybrid model gives better results than Artificial Neural Network