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

Abdo-Allah, Almahdi; Iqbal, M. Tariq; Pope, Kevin

doi:10.1155/2018/4540387

Cited by 17 publications

(11 citation statements)

References 23 publications

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“…Meanwhile, in the real system (as shown in Figure 2), a hot Journal of Energy 3 water valve collects relevant data for water heated by a heating coil. Although the building's system features a single valve for its hot water production, the IDA-ICE software divides the single valve into four distinct units, enabling each airhandling unit (AHU) to have an individual valve [22,23].…”

Section: Simulation Toolmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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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.

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Section: Simulation Toolmentioning

confidence: 99%

Energy Consumption Analysis of a Large Building at Memorial University

2019

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“…CO2 levels )unit is parts per million, PPM) for controlling fresh air dampers. A detailed state space model used for controller design for AHU1 was presented in [29].…”

Section: Input Data Reportmentioning

confidence: 99%

“…To increase the output gain, the tuning of a PI controller can be used, as described the outputs of the first AHU1 in the following subsections [29]. Also, the table 4 illustrate all the details of MFs (Close-Fast, Close, No-Change, Open and Open-Fast) and the related operation percentages of hot water valve's, fan speed and fresh air dampers of whole system.…”

Section: Sflc Input Variablesmentioning

confidence: 99%

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Supervisor Fuzzy Logic Controller for HVAC System of S.J Carew Building at Memorial University

Abdo-Allah

Iqbal²,

Pope³

2019

EJECE

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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.

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“…In this study, the FLC also is developed and employed to decrease power consumption and cost of both illumination and HVAC systems, resulting in a significant reduction in the energy consumption, monetary cost, and peak to average ratio (PAR). Abdo-Allah et al [20] concluded that the function of HVAC systems are significant for effective thermal management and operational costs. Therefore, the FLC is used to control indoor temperatures, CO2 concentrations in air handling units (AHUS), fan speeds and power consumption.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration

et al. 2020

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Recently, homes consume around 40% of world power and produce 21% of the total greenhouse gas emissions. Thus, the proper management of energy in the domestic sector is a vital element for creating a sustainable environment and cost reduction. In this study, an intelligent home energy management system (HEMS) is developed to control domestic appliances load. The motivation of this work is reduced the electricity cost and power consumption from all the appliances by maintaining the customer's high comfort level using an efficient optimized controller. The domestic household appliances such as heating ventilation and air conditioning (HVAC), electric water heater (EWH) and lighting were modelled and analysed using Simulink/Matlab. The developed models analysed the appliances' energy consumption and cost sceneries during peak, off-peak and both peak and off-peak hours. Fuzzy logic controller (FLC) was developed for the HEMS to perform energy utilization estimation and cost analysis during these periods taking the Malaysian tariff for domestic use into consideration. To improve the FLC outcomes and the membership function constraint, particle swarm optimization (PSO) is developed to ensure an optimal cost and power consumption. The results showed that the developed FLC controller minimized the cost and energy consumption for peak period by 19.72% and 20.34%, 26.71% and 26.67%, 37.5% and 33.33% for HVAC, EWH, and dimmable lamps, respectively. To validate the optimal performance, the obtained results shows that the FLC-PSO can control the home appliances more significantly compared to FLC only. In this regard, the FLC-PSO based optimum scheduled controller for the HEMS minimized power and cost by 36.17%-36.54%, 54.54%-55.76%, and 62.5%-58% per day for HVAC, EWH, and light, respectively. In sum, the PSO shows good performance to reduce the cost and power consumption toward efficient HEMS. Thus, the developed fuzzy-based heuristic optimized controller of HEMS is beneficial towards sustainable energy utilization. INDEX TERMS Home energy management, cost of energy, fuzzy logic controller, particle swarm optimization, home appliances, building energy, energy saving.

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

Cited by 17 publications

References 23 publications

Energy Consumption Analysis of a Large Building at Memorial University

Energy Consumption Analysis of a Large Building at Memorial University

Supervisor Fuzzy Logic Controller for HVAC System of S.J Carew Building at Memorial University

Fuzzy Based Particle Swarm Optimization for Modeling Home Appliances Towards Energy Saving and Cost Reduction Under Demand Response Consideration

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