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energy analysis of an air conditioning system using fuzzy logic control

energy analysis of an air conditioning system using fuzzy logic control

Reducing energy consumption and to ensure thermal comfort are two important considerations for the designing an air conditioning system. An alternative approach to reduce energy consumption proposed in this study is to use a variable speed compressor. The control strategy will be proposed using the fuzzy logic controller (FLC). FLC was developed to imitate the performance of human expert operators by encoding their knowledge in the form of linguistic rules. The system is installed on a thermal environmental room with a data acquisition system to monitor the temperature of the room, coefficient of performance (COP), energy consumption and energy saving. The measurements taken during the two hour experimental periods at 5-minutes interval times for temperature setpoints of 20 o C, 22 o C and 24 o C with internal heat loads 0, 500, 700 and 1000 W. The experimental results indicate that the proposed technique can save energy in comparison with On/Off and proportional-integral-derivative (PID) control.
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Optimization of Energy Conservation Potential for VAV Air Conditioning System using Fuzzy based Genetic Algorithm

Optimization of Energy Conservation Potential for VAV Air Conditioning System using Fuzzy based Genetic Algorithm

results of variable air volume (VAV) air conditioning system optimized by two objective genetic algorithm (GA). The objective functions are energy savings and thermal comfort. The optimal set points for fuzzy logic controller (FLC) are the supply air temperature (T s ), the supply duct static pressure (P s ), the chilled water temperature (T w ), and zone temperature (T z ) that is taken as the problem variables. Supply airflow rate and chilled water flow rate are considered to be the constraints. The optimal set point values are obtained from GA process and assigned into fuzzy logic controller (FLC) in order to conserve energy and maintain thermal comfort in real time VAV air conditioning system. A VAV air conditioning system with FLC installed in a software laboratory has been taken for the purpose of energy analysis. The total energy saving obtained in VAV GA optimization system with FLC compared with constant air volume (CAV) system is expected to achieve 31.5%. The optimal duct static pressure obtained through Genetic fuzzy methodology attributes to better air distribution by delivering the optimal quantity of supply air to the conditioned space. This combination enhanced the advantages of uniform air distribution, thermal comfort and improved energy savings potential.
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DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM

DEVELOPMENT OF NEURO FUZZY CONTROLLER ALGORITHM FOR AIR CONDITIONING SYSTEM

One of the major problems of the fuzzy logic control is the difficulty of choice and design of membership functions for a given problem [6]. Therefore, tuning of membership functions becomes an important issue in fuzzy modeling. Since this tuning task can be viewed as an optimization problem. Neural networks offer a possibility to solve this problem [7]. Hence, combining the adaptive neural networks and fuzzy logic control forms a system called neuro-fuzzy system. Neural networks are well known for its ability to learn and adapt to unknown or changing environment to achieve better performance. On the other hand, fuzzy logic by its effectiveness in handling linguistic information, incorporate human knowledge, deal with imprecision and uncertainty. Neuro-fuzzy system combines the learning capabilities of the neural networks and control capabilities of a fuzzy logic control system. It is a system that uses a learning algorithm to determine its parameters by processing data samples. Fig. 1. shows architecture of neuro-fuzzy system. First layer of neurons represents the input variables, second layer represents the input membership functions, third layer represents the rule base, fourth layer represents the output membership functions and fifth layer represents the output variables [8].
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Comparative Analysis of Single and Hybrid Neuro- Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

Comparative Analysis of Single and Hybrid Neuro- Fuzzy-Based Models for an Industrial Heating Ventilation and Air Conditioning Control System

The hybridization of machine learning methods with soft computing techniques is an essential approach to improve the performance of the prediction models. Hybrid machine learning models, particularly, have gained popularity in the advancement of the high-performance control systems. Higher accuracy and better performance for prediction models of exergy destruction and energy consumption used in the control circuit of HVAC systems can be highly economical in the industrial scale to save energy. This research proposes two hybrid models of ANFIS- PSO and ANFIS-GA for the HVAC control system. The results are further compared with the single ANFIS model. The ANFIS- PSO model with the RMSE of 0.0065, MAE of 0.0028, and R2 equal to 0.9999, with a minimum deviation of 0.0691 (KJ/s), outperforms the ANFIS-GA and single ANFIS models. For the future research, advancement of hybrid and ensemble machine learning models, e.g., [47-52], and comparative analysis with deep learning models, e.g., [53-56] are proposed to identify models with higher efficiency.
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Energy And Thermal Comfort Analysis For Air Conditioning And Ventilation System In Laboratory

Energy And Thermal Comfort Analysis For Air Conditioning And Ventilation System In Laboratory

Human comfort and air-conditioning are related to each other. Air conditioning system is designed to satisfy the need of the human body. Temperature, humidity, pressure and air motion are some of the important variable that refrigeration and air-conditioning deals with the techniques to control the environment and provide comforts to enable the better and longer lives. The room that have air conditioning system has their own system performance. The air- conditioning system is needed to maintain desired conditions in the laboratory during different operating conditions in most economical way (energy, cost efficiency). The air conditioning and ventilation system of the laboratory may be installing at area that the energy is being used efficiently and make human feel comfort but not in the most economical way. So the room air-conditioning strategy is fundamental scheme that describe the targeted temperature, humidity and air flow patterns within the air- conditioning room that can be used to this problem.
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													Pic 16f877a based implementation of fuzzy control algorithm for split air-conditioning system

1. Pic 16f877a based implementation of fuzzy control algorithm for split air-conditioning system

In order to observe the performance of Fuzzy Logic Controller embedded in PIC, a set temperature value of 20 0 C was given and the room temperature was logged. The data collected is plotted as shown in figure12. The MATLAB_Simulink model of plant has been executed and the result plotted is shown in figure 12. The computer simulation and experimental results are comparable which implicates the validity of the outcome. The little differences in parameter values are observed because of thermal mass effect that causes time lag in experimental readings which is not considered in computer simulation.
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Performance Analysis of Wind Energy Conversion System Using Fuzzy Logic Controller

Performance Analysis of Wind Energy Conversion System Using Fuzzy Logic Controller

ABSTRACT: This paper proposes a novel control strategy for the operation of direct driven Permanent Magnet Synchronous Generator (PMSG) based grid connected variable speed wind turbine (VSWT). VSWT driving a PMSG is connected to the power system through a fully controlled frequency converter, which supplies reactive power demand of the network during disturbance as well as extract maximum power from the wind. This improves the Low voltage ride through capability (LVRT) of the system without integration of any FACTS devices in the wind farm terminal. Fuzzy logic controller is employed for the control of pitch angle, real and reactive power flows of grid connected direct driven VSWT-PMSG system. The low voltage ride through (LVRT) Capability and dynamic performance of both fixed and variable speed WTGS are analyzed using Mat lab/Simulink software.
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Research on Fuzzy Regulation Strategies in the Constant Air Volume Air Conditioning System

Research on Fuzzy Regulation Strategies in the Constant Air Volume Air Conditioning System

condition. As the variation of outdoor air parameters and indoor load, the operating parameters must change accordingly. Therefore, effective regulation strategies are important for avoid wasting energy. CAV system is one of the most popular air conditioning systems and some researches have been done on the regulation strategies of CAV system. Gong Yanfeng determined the relationship between supply air condition and fresh air condition by analyzing the whole year air condition load. In their research the outdoor air conditions which have the same air treating course were divided into one region and the same operating strategy was adopted [1] . Wang Li analyzed the temperature control principles to achieve economical operation in CAV system, and presented improved control strategy to integrate fresh air control with indoor air temperature control [2] .
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Indoor Air Quality Monitoring System With Fuzzy Logic Control Based On IOT

Indoor Air Quality Monitoring System With Fuzzy Logic Control Based On IOT

Polluted air in indoor environment can be contaminated by harmful chemicals and others materials [1]. Air pollution can lead to various diseases such as asthma, wet lung, even coronary heart. Pollution can be done outside or indoors. However, people spend around 90% of their activities indoor, such as at office, homes, school, etc. [2]. CO2 is one of highest elements in indoor environment due to respiration and activities of human inside the room. High level of CO2 can make variety of irritants and decrease cognitive performance [3]. Another material in the air that can effect for human health is PM10. There is standard for indoor air quality gases concentration in room from ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers). For PM10, Based on US Environmental Protection Agencies (EPA), the standards for PM10 concentration in 24-hour is 150 ug/m 3 . For carbon dioxide ppm maximum in indoor room that still make comfort for human odor in room, ASHRAE has standard 1000 PPM maximum in the room [4]. Air quality has index that represent the quality of air. That has value from 0 to 500 called AQI. Indoor air quality monitoring needs to be implemented to control air quality in room. Indoor air quality monitoring can ensures that indoor environment in room is safe for stay or do activities[5]. With the current technology, Indoor air quality monitoring can also integrated with Internet .Internet of things concept can be implement on system indoor air quality monitoring. Internet of Things is technology that can make something smarter than before [6].
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Performance improvement of a wind energy system using fuzzy logic based pitch angle control

Performance improvement of a wind energy system using fuzzy logic based pitch angle control

The pitch angle controller maintains the aerodynamic captured power at rated level when the wind speed is above the rated speed. Besides, it can also improve the transient stability occurring in the wind energy system (WES). This paper, therefore, proposes an effective pitch angle control strategy that can deliver the conditioned output power in windy condition and increase the transient stability capability in grid faults. A fuzzy logic method has employed to design the proposed control strategy, Moreover, in this paper some major factors that affect the transient stability have been investigated by deriving steady-state equivalent model of the wind energy system. The simulated results show that the proposed fuzzy logic based pitch angle controller is effective at conditioning the output power and complying with fault ride through requirements for WES in the power system.
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Energy Conservation of Heat, Ventilation & Air- Conditioning System with the help of Fuzzy Controller

Energy Conservation of Heat, Ventilation & Air- Conditioning System with the help of Fuzzy Controller

The results have been derived from MATLAB/SIMULINK (SIMULATION LINK) library browser comprising of various blocks such as Sinks, Sources, Continuous, Discrete, Discontinuities, Math Operations, Signal Attributes, User Defined Functions, Ports and Subsystems, Signal Routing, Logic and Bit Operations, Lookup Tables, Model Verification, Model Wide Utilities, Additional Math and Discrete blocks. The SIMULINK block diagram showing the comparison between a PID (Proportional Integral Derivative) Controller and Fuzzy Controller is as shown in fig 5.1. Figure 5.2 shows the course of supply air temperature, before the optimization of system operation by PID (Proportional Integral Derivative) control. The alternation of the supply air temperature is between 10.5 °C and 4.8°C. The reason for such a big set point error range lies in the discontinuous operation of the chilling system by a PID (Proportional Integral Derivative) control system. This high alternation of the supply temperature is a reflected image of the alternation of the system status. This unsatisfied system behaviour was realized by Fuzzy Controllers which have the ability of fine tuning the controller gains to match the dynamic characteristics of process it is controlling without regard for the other processes. In this way the various nonlinearities are minimized in a fuzzy control system. Thus effective coordination and adequate operation takes place with the help of fuzzy logic techniques.
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An Intelligent Air Traffic Control System using Fuzzy Logic Model

An Intelligent Air Traffic Control System using Fuzzy Logic Model

The application of fuzzy logic in solving flight schedule and air traffic control problems. Fuzzy set theory is appropriate in deal- ing with this problem because of its ability to deal with control operations and the development of knowledge-based systems us- ing approximate reasoning. The objective is to solve the problem inherent with poor traffic control using an intelligent (knowledge- based) system based on learnt procedures and processes over a set period of time. A fuzzy logic model for air traffic control sys- tem is developed that will enhance the performance of air traf- fic controller and reduce the rate of aircraft accident. The Object- Oriented Analysis and Design Methodology (OOADM) is used in designing the intelligent air traffic control system proposed in this paper. A fuzzification block is designed to convert the fuzzy logic controller. The fuzzy logic input values used are Path- way, Velocity, Climate, Airplane, Height and D-term - which al- lows the controller to respond faster to permission for clearance. Eleven rules were constructed based on the assigning of linguis- tic values defined by relatively small number of membership func- tion to variable. The computation block runs the inference en- gine through all the rules, evaluating the firing strength of each rule whose result is proportional to the truth-value of the precon- ditions. MATLAB was used to simulate the outcome. The Nige- ria airspace is used in the study. From simulated results, safety rules projected were observed by all aircrafts with absolute con- trol irrespective of the number of aircrafts demanding service at a particular time interval. Air accidents were perfectly avoided.
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HYDRO ELECTRIC POWER DAM CONTROL SYSTEM USING FUZZY LOGIC

HYDRO ELECTRIC POWER DAM CONTROL SYSTEM USING FUZZY LOGIC

295 | P a g e recognition of storage volume non-specificity in the discrete Stochastic Dynamic Programming (SPD) [3].Fuzzy dynamic programming model was used for Hirakud dam in the State of Orissa in India in which irrigation; hydropower generation and flood control were considered as fuzzy variables [4].The neural network and fuzzy systems were also adopted for dam control in which a comparison was made between reservoir operations using the fuzzy and neural network systems and actual one by operator, using examples of floods during flood and non-flood seasons [5].Reports show that hydroelectric dams produce 20 percent of the world’s total production of electrical energy. The development of a hydro-electric power dam control system based on fuzzy logic with two inputs and two outputs. Using water level and flow rate measuring devices for feedback control, and two control elements for draining and valve controlling (release), and formulated fuzzy rules for water level and flow rate has been achieved.To control the water release, the controller reads the water level and flow rate after every sampling period. This proposed design work of Hydro-Electric Power Dam System is the application of fuzzy logic control system consisting of two input variables: water level and flow rate, and two output variables: Drain valve and (Releasing) Valve control used in a reservoir plant of Hydro-Electric Power Dam to monitor the system of Dam.
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ENHANCEMENT OF MICROGRID CONTROL IN DISTRIBUTION SYSTEM USING FUZZY LOGIC TECHNIQUE

ENHANCEMENT OF MICROGRID CONTROL IN DISTRIBUTION SYSTEM USING FUZZY LOGIC TECHNIQUE

Fig. 1 shows a conceptual Micro Grid system configuration where various ac and dc sources and loads are connected to the corresponding dc and ac networks. The ac and dc links are connected together through two transformers and two four-quadrant operating three phase converters. The ac bus of the Micro Grid is tied to the utility grid. A compact Micro Grid as shown in Fig. 2.1 is modeled using the Simulink in the MATLAB to simulate system operations and controls. 40 KW PV arrays are connected to dc bus through a dc/dc boost converter to simulate dc sources. A capacitor is to suppress high frequency ripples of the PV output voltage. A 50 KW wind turbine generator (WTG) with doubly fed induction generator (DFIG) is connected to an ac bus to simulate ac sources. A 65 Ah battery as energy storage is connected to dc bus through a bidirectional dc/dc converter. Variable dc load (20 kW–40 kW) and ac load (20 kW–40 kW) are connected to dc and ac buses respectively. The rated voltages for dc and ac buses are 400 V and 400 V RMS respectively. A three phase bi directional dc/ac main converter with R-L-C filter connects the dc bus to the ac bus through an isolation transformer.
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A Research Paper on Optimum Energy in Air Conditioning System

A Research Paper on Optimum Energy in Air Conditioning System

Abstract--Thermal system cannot always be optimized by means of mathematical or numerical techniques, because a complete model of the plant is not always available; and in case mathematical difficulties are often great, even for particularly complex system and the help of computerized algorithms is needed. Furthermore, mathematical or numerical optimization simply applies to one specified structure of the system, where’s, often, structural modifications would be able to improve the cost effeteness of the system.Thermoeconomics is a discipline, which combines the concept of Exergy method with those belonging to economic analysis. The purpose of thermo economic optimizations is to achieve, within a given system structure, a balance between expenditure on capital cost and energy costs, which will give the minimum cost of the plant product. In this thesis, an attempt has been made to optimize an Air conditioning system(vapor compression refrigeration cycle) with the help of thrmoeconomi analysis. First of all, energy (thermodynamic) analysis of the different components of the system is carried out to understand the performance of the components. On exergy basis theological and total irreversibility of the system are found out and exergy basis the local and total irreversibility of the system are found and exergy losses in the different components has been presented in the form of grass man diagram[16]. The costs assigned for exergy, losses are combined with the capital and recovery cost and equations for thermo economic optimum of the components are derived.
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A New Control Strategy of Indoor Air Temperature in an Air-Conditioning System

A New Control Strategy of Indoor Air Temperature in an Air-Conditioning System

Nowadays, the main target of air conditioning controlling and adjusting system is to maintain the indoor temperature and humidity at an expected level. In a manufactory, the technique process always determines values of these air parameters, thus production can be output with high quality. However, for air conditioning systems aimed at thermal comfort of occupants in the building, things are different, people instead of production become the determinative. To find the value of air parameters that can meet occupants’ thermal comfort demand, many efforts have been made. In most cases, studies are based on laboratory and/or field works in which people are thermally investigated under different condition. Then, the collected data are statistically analyzed. As a result, a range/value is suggested as thermal comfort range/value. In a traditional air conditioning PID control strategy, this becomes the set value. For big commercial building like theater, supermarket, airport, the traditional control strategy can satisfy majority of its occupants; but for small single room, office for personal use, it may not work well in terms of thermal comfort and energy efficiency, simply because the thermal demand of the occupant in room is changing instantaneously, which has close relationship with his/her updated level of activity, cloth, hungry, anxiety. One most common experience of occupants in a room applied traditional air conditioning control strategy is that in summer when people have a sleep, if the set room temperature is too high, he may feel too hot and wake up; on the contrary, if the set room temperature is too low, he may feel too cold and get a fever. This example shows the defection of traditional air conditioning control strategy, which doesn’t fully consider its service targets, the occupant, in the control loop.
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Supervisory control for conventional unitary air conditioning system

Supervisory control for conventional unitary air conditioning system

In Supervisory Control and Data Acquisition (SCADA) systems  and Distributed Control Systems, PLCs are implemented as local controllers within a supervisory control scheme.   PLC is an industrial computer used to monitor inputs and the decisions are made based on its program or logic, to control (turn on/off) its outputs to automate a machine or a process. According to The National Electrical Manufacturers Association (NEMA), a programmable logic controller is defined as a digitally operating electronic apparatus which uses a programmable memory for the internal storage of instructions for implementing specific functions such as logic, sequencing, timing, counting, and arithmetic to control, through digital or analog input/output modules, various types of machines or processes. The common advantages why PLC is used extensively in almost all industrial processes because it is:
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Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

Multi-Objective Control of Air Conditioning Improves Cost, Comfort and System Energy Balance

The first class of price-responsive control algorithms all seek to shift air conditioner operation between hours while maintaining temperatures within a narrow band that preserves occupant comfort. For example, several authors used simple control algorithms with “brick-wall” temperature limits to investigate the aggregate load-shifting potential of HVAC systems: [5] used such an algorithm to show that electricity demand need not be considered as purely inelastic; [6] used such an algorithm as part of an effort to estimate the effects of large scale adoption of demand response technologies, and [7] used a “brick-wall” algorithm to estimate the potential flexibility of residential HVAC loads in smart grids. Others in the first class of algorithms focus on the savings available to individual customers in a dynamic pricing environment [8], possibly addressing specific technological innovations in the building [9]. Among the customer-focused algorithms, one of the most advanced was introduced by Corbin, Henze, and May-Ostendorp [10]. They use Particle Swarm Optimization (PSO) [11] with a detailed thermal simulation in open-source EnergyPlus building simulation software [12] to perform model predictive control of the HVAC system in a commercial office building. Although Corbin et al. do not mention it, their algorithm represents a sort of “gold standard” for building control—it is able to directly optimize HVAC operation throughout the day to minimize costs under time-varying pricing, using state-of-the-art building simulation software to directly account for the complex, nonlinear behavior of the coupled building and HVAC system.
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Fuzzy Logic Control System and its Applications

Fuzzy Logic Control System and its Applications

It was introduced by Dr. Lotfi Zadeh of a professor at the University of California at Berkley in the 1960's as a means to model the uncertainty of natural language. He says that rather than regarding fuzzy theory as a single theory, we should regard the process of ``fuzzification'' as a methodology to generalize ANY specific theory from a crisp (discrete) to a continuous (fuzzy) form. Thus recently researchers have also introduced "fuzzy calculus", "fuzzy differential equations" and so on.

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Energy Management using Optimal Fuzzy Logic Control in Wireless Sensor Network

Energy Management using Optimal Fuzzy Logic Control in Wireless Sensor Network

ACO algorithm is a probabilistic technique for solving computational problems which can be reduced to finding good paths through graphs [21]. The first ACO algo- rithm was called the ant system and it aimed to solve the travelling salesman problem, in which the goal is to find the shortest round trip to link a series of cities. In the natu- ral world, ants of some species (initially) wander randomly, and upon finding food return to their colony while laying down pheromone trails. If other ants find such a path, they are likely not to keep travelling at random, but instead to follow the trail. ACO algorithm simulates the foraging behavior of ants in nature. Ants perceive the pheromones released by other ants, prefer to choose the path according to high pher- omone concentration and a short distance, and release a certain amount of pheromone to form positive feedback. Finally, the shortest path is found. So ACO algorithm is strong robustness, and suitable for distributed computing. However, the parameters in the algorithm are usually determined by experiments, and closely related to human experience. If they are not chosen properly, the speed in problem solving is slow and the quality of the solution is poor.
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