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, CO 2 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.

In this paper, the HVAC System for the S.J. Carew Building at Memorial University is modeled using a state space multi-input and multi-output system (MIMO) approach for analyses and control system design. The IDA Indoor Climate and Energy ICE simulation program is used to develop the models. The system has three air-handling units and four floors. Supply air flow temperature and hot water temperature data are used as input data for the model. Environmental inputs of outdoor temperature, wind direction and velocity are used as disturbances. The temperature of the zones and humidity are used as output data. The simulated energy consumption for the first fifteen days of Dec 2015 is compared to measured data and good agreement is achieved for the whole building. The main purpose of this paper is to obtain a state space model of a MIMO system using the Matlab system identification toolbox. Building data and details of the model are presented in the paper.

A HVAC system is modeled by applying a state space MIMO (multi-input/multioutput) system method for control system design and analysis. Thermal models are developed using the simulation program IDA Indoor Climate and Energy. The building has four floors in total, with separate air-handling units (AHUs) on each floor. The system’s eight main input data are hot water and the energy usage for each AHU, while the eight main outputs are return airflow temperature and CO2 levels for AHUs. The factors of wind direction and velocity are also applied as disturbances. By comparing usage data on simulated power consumption versus measured data for the three months of October, November, and December 2016, good agreement was achieved with simulated data. The main aim is to develop a state feedback controller and then apply it toward optimal functionality of a control system. After utilizing the MATLAB identification toolbox, a MIMO system-based state space model is developed.

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.

One of the most important characteristics contributing to the thermal management efficiency of commercial, industrial, institutional or home environments is the optimal functioning of HVAC (heating, ventilation, air conditioning) systems. In addition to using supervisor controllers for balancing comfort level in a building, the majority of today’s HVACs employ nonlinear time variance controllers when dealing with a variety of disturbances. This paper investigates both current and potential HVAC systems at Memorial University’s S. J. Carew building, St. John’s, Newfoundland. The study investigates the viability of algorithm-based supervisor fuzzy logic controllers (SFLC) for the control of the building’s four air-handling units (AHUs) used to manage the interior environment. Along with temperature, the SFLCs also control the AHUs’ fan speeds and CO2 concentrations modifying hot water and air flow rates. This work presents models of damper positions, fan speeds and globe valves that have been built in accordance with current rates of air and hot water flow in the S. J. Carew building. Based on these specifications, a novel method of SFLC adaptation using fuzzy rules has been devised. The novel system aims to better balance the performance level of the Carew building’s HVAC system on a floor-by-floor basis. The overall results indicate better overall thermal comfort levels and enhanced cost-effectiveness when using the SFLC redesign.