A novel control approach based on hybrid Fuzzy Logic and Seeker Optimization for optimal energy management between micro-sources and supercapacitor in an islanded Microgrid
یک روش کنترل جدید مبتنی بر منطق فازی ترکیبی و بهینه سازی جستجوگر برای مدیریت بهینه انرژی بین منابع کوچک و ابر رسانا در یک میکروگرید جزیره ای-2020
This work presents a novel control technique for proportional power sharing among parallel VSCs connected to an islanded Microgrid in a distributed generation system consisting of Photovoltaic (PV) and Solid Oxide Fuel Cell (SOFC) as two micro-sources. For tracking the maximum solar energy, a Seeker Optimized Fuzzy based Dynamic PI (FSOA-DPI) controller is implemented for the Modified Perturb and Observe MPPT method. Again, for the optimum cost management of the Microgrid system, DPI controller based decentralized Virtual Impedance Drooping (VID) technique is implemented for suitable load sharing between the two hybrid micro-sources. An Energy Storage System (ESS) regulated by FSOA-DPI controller is also proposed for this Microgrid system to ensure the better transient and sub-transient stability during fault occurrence. The dynamic response and stability of the system with proposed method is compared and contrasted with conventional PI controller based method during load sharing for ensuring the robust control under conditions of nonlinear load and faults. The harmonic analysis has been carried out by Fast Fourier Transform (FFT) and the values indicate that the Total Harmonic Distortion (THD) is well within the prescribed IEEE standard limits. Validation and justification of the improvements achieved by the proposed controller are realized using Matlab/Simulink environment.
Keywords: Microgrid | Distributed generation (DG) | Hybrid micro-source | Photovoltaic (PV) | Solid Oxide Fuel Cell (SOFC) | Virtual Impedance Drooping | Seeker Optimization Approach (SOA)
Comfort evaluation of seasonally and daily used residential load insmart buildings for hottest areas via predictive mean vote method
ارزیابی راحتی ساختمانهای بار مسکونی فصلی و روزانه برای گرمترین مناطق با استفاده از روش پیش بینی میانگین رای گیری-2020
tIn this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mam-dani (BPSOFMAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonallyused appliances are considered for the analysis of the efficient energy management through these con-trollers. Energy management is performed using the two Demand Side Management (DSM) strategies:load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of thedaily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances,i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are appliedin this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of thehottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occu-pancy, price, decision control variables, priority and length of operation times of the appliances, whereasthe output parameters are: energy consumption, cost and thermal and appliance usage comfort. More-over, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger’spredictive mean vote method. The comfort is further investigated by incorporating the renewable energysources, i.e., photovoltaic systems. Simulation results show the effectiveness of the proposed controllersas compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energyconsumption and cost of the proposed scenario.
Keywords:Energy management | Thermal comfort | Appliance usage comfort | Fuzzy logic | Fuzzy inference systems
Z-number based earned value management (ZEVM): A novel pragmatic contribution towards a possibilistic cost-duration assessment
مدیریت ارزش به دست آمده مبتنی بر عدد Z (ZEVM): سهم عملگرا جدید نسبت به ارزیابی هزینه تمام شده احتمالی-2020
The Earned value management (EVM) is one of the simplified analytical cost-duration assessment tools which assist project managers in monitoring the status of the project undertaken. The EVM has been elaborated by both deterministic and uncertain numbers such as fuzzy logic in the light of time. Even though cost-duration analysis is so sensitive and fluctuating in projects, the adopted approaches were unable to consider the conspicuous unreliability which is always involving the decision-making data. This problem impedes project managers to trust the foreseen inferences. To help in overcoming this critical deficiency, Z-numbers were proposed to take possibilities and reliabilities into account. Applying Z-numbers and possibilistic modeling in the EVM is a challenging topic which causes the accuracy of cost-duration tracing results to be significantly enhanced. This paper presents the application of z-numbers for modeling the earned value indicators and proves the superiority of the ZEVM against traditional fuzzy EVM. This work originally adds to the state-of-the-art literature on earned value management by presenting a proposal and applications of a new as Z-Earned Value Management (ZEVM). An illustrative case is resolved to magnify the capability of the proposed framework in dealing with higher levels of uncertainty associated with decision-making data.
Keywords: Earned value management | Fuzzy sets | Project evaluation | Uncertainty | Z-number
Application of a fuzzy-logic based model for risk assessment in additive manufacturing r&d projects
استفاده از یک مدل مبتنی بر منطق فازی برای ارزیابی ریسک در پروژه های تحقیق و توسعه افزودنی-2020
Experts from industry and academics have highlighted Additive Manufacturing (AM) as a technology that is revolutionizing manufacturing. AM is a process that consists of creating a three-dimensional object by incorporating layers of a material such as metal or polymer. This research studies risks associated with AM R&D Project Management. A significant set of risks with a potential negative impact on project objectives in terms of scope, schedule, cost and quality are identified through an extensive literature review. These risks are assessed through a survey answered by ninety academics and professionals with noteworthy sector expertise. This process is made by the measurement of two parameters: likelihood of occurrence and impact on project objectives. According to the responses of the experts, the level of relevance of each risk is calculated, innovatively, through a fuzzy logic-based model, specifically developed for this study, implemented in MATLAB Fuzzy Logic Toolbox. The results of this study show that the risks “Defects occurring during the manufacturing process”, “Defective design”, “Poor communication in the project team” and “Insufficient financing” are determined as the most critical in AM R&D Project Management. The proposed model is presented as a powerful new tool for organizations and academics, to prioritize the risks that are more critical to develop appropriate response strategies to achieve the success of their projects.
Keywords: Additive Manufacturing | 3D printing | Risk Assessment | Project Management | Fuzzy Logic
Artificial intelligence in the AEC industry: Scientometric analysis and visualization of research activities
هوش مصنوعی در صنعت AEC: تجزیه و تحلیل ساینومتریک و تجسم فعالیتهای تحقیقاتی-2020
The Architecture, Engineering and Construction (AEC) industry is fraught with complex and difficult problems. Artificial intelligence (AI) represents a powerful tool to assist in addressing these problems. Therefore, over the years, researchers have been conducting research on AI in the AEC industry (AI-in-the-AECI). In this paper, the first comprehensive scientometric study appraising the state-of-the-art of research on AI-in-the-AECI is presented. The science mapping method was used to systematically and quantitatively analyze 41,827 related bibliographic records retrieved from Scopus. The results indicated that genetic algorithms, neural networks, fuzzy logic, fuzzy sets, and machine learning have been the most widely used AI methods in AEC. Optimization, simulation, uncertainty, project management, and bridges have been the most commonly addressed topics/ issues using AI methods/concepts. The primary value and uniqueness of this study lies in it being the first in providing an up-to-date inclusive, big picture of the literature on AI-in-the-AECI. This study adds value to the AEC literature through visualizing and understanding trends and patterns, identifying main research interests, journals, institutions, and countries, and how these are linked within now-available studies on AI-in-the-AECI. The findings bring to light the deficiencies in the current research and provide paths for future research, where they indicated that future research opportunities lie in applying robotic automation and convolutional neural networks to AEC problems. For the world of practice, the study offers a readily-available point of reference for practitioners, policy makers, and research and development (R&D) bodies. This study therefore raises the level of awareness of AI and facilitates building the intellectual wealth of the AI area in the AEC industry.
Keywords: Architecture-engineering-construction | Artificial intelligence | Machine intelligence | Industry 4.0 | Automation | Digital transformation | Scientometric | Review
Prediction of greenhouse gas emissions from Ontario’s solid waste landfills using fuzzy logic based model
پیش بینی انتشار گازهای گلخانه ای از محل های دفع زباله جامد انتاریو با استفاده از مدل مبتنی بر منطق فازی-2020
In this study, multi-criteria assessment technique is used to predict the methane generation from large municipal solid waste landfills in Ontario, Canada. Although a number of properties determine the gas generation from landfills, these parameters are linked with empirical relationships making it difficult to generate precise information concerning gas production. Moreover, available landfill data involve sources of uncertainty and are mostly insufficient. To fully characterize the chemistry of reaction and predict gas generation volumes from landfills, a fuzzy-based model is proposed having seven input parameters. Parameters were identified in a linguistic form and linked by 19 IF-THEN statements. When compared to measured values, results of the fuzzy based model showed good prediction of landfill gas generation rates. Also, when compared to other first order decay and second order decay models like LandGEM, the fuzzy based model showed better results. When plotting the LandGEM and Fuzzy model values to the actual measured data, the fuzzy model resulted in a better fit to actual data than the LandGEM model with a coefficient of determination R2 of 0.951 for fuzzy model versus 0.804 for LandGEM model. The results show how multi-criteria assessment technique can be used in modelling of complicated processes that take place within the landfills and somehow accurately predicting the landfill gas generation rate under different operating conditions
Keywords: Municipal solid waste | Landfill gas | Life-cycle assessment | Waste to energy | Greenhouse gas emissions | Fuzzy model
Fuzzy logic interpretation of quadratic networks
تفسیر منطق فازی از شبکه های درجه دوم-2020
Over past several years, deep learning has achieved huge successes in various applications. However, such a data-driven approach is often criticized for lack of interpretability. Recently, we proposed arti- ficial quadratic neural networks consisting of quadratic neurons in potentially many layers. In cellular level, a quadratic function is used to replace the inner product in a traditional neuron, and then under- goes a nonlinear activation. With a single quadratic neuron, any fuzzy logic operation, such as XOR, can be implemented. In this sense, any deep network constructed with quadratic neurons can be interpreted as a deep fuzzy logic system. Since traditional neural networks and quadratic counterparts can represent each other and fuzzy logic operations are naturally implemented in quadratic neural networks, it is plau- sible to explain how a deep neural network works with a quadratic network as the system model. In this paper, we generalize and categorize fuzzy logic operations implementable with individual quadratic neu- rons, and then perform statistical/information-theoretic analyses of exemplary quadratic neural networks.
Keywords: Machine learning | Artificial neural network (ANN) | Quadratic network | Fuzzy logic
Variable structure battery-based fuel cell hybrid power system and its incremental fuzzy logic energy management strategy
سیستم قدرت هیبریدی سلول سوختی مبتنی بر باتری ساختار متغیر و استراتژی مدیریت انرژی منطق فازی افزایشی آن-2020
A hybrid power system consists of a fuel cell and an energy storage device like a battery and/or a supercapacitor possessing high energy and power density that beneficially drives electric vehicle motor. The structures of the fuel cell-based power system are complicated and costly, and in energy management strategies (EMSs), the fuel cell’s characteristics are usually neglected. In this study, a variable structure battery (VSB) scheme is proposed to enhance the hybrid power system, and an incremental fuzzy logic method is developed by considering the efficiency and power change rate of fuel cell to balance the power system load. The principle of VSB is firstly introduced and validated by discharge and charge experiments. Subsequently, parameters matching of the fuel cell hybrid power system according to the proposed VSB are designed and modeled. To protect the fuel cell as well as ensure the efficiency, a fuzzy logic EMS is formulated via setting the fuel cell operating in a high efficiency and generating an incremental power output within the affordable power slope. The comparison between a traditional deterministic rules-based EMS and the designed fuzzy logic was implemented by numerical simulation in three different operation conditions: NEDC, UDDS, and user-defined driving cycle. The results indicated that the incremental fuzzy logic EMS smoothed the fuel cell power and kept the high efficiency. The proposed VSB and incremental fuzzy logic EMS may have a potential application in fuel cell vehicles.
Keywords: Fuel cell vehicles | Hybrid fuel cell/battery power | system | Variable structure battery (VSB) | Incremental fuzzy logic energy | management | Maintain efficiency and lifespan
Intelligent energy management system for conventional autonomous vehicles
سیستم هوشمند مدیریت انرژی برای وسایل نقلیه معمولی خود مختار -2020
Autonomous vehicles have been envisioned to increase vehicle safety, primarily via the reduction of accidents. However, their design could also affect the vehicle travel demand and energy consumption. Although battery-powered electric and hybrid-electric autonomous vehicles assume more widespread use than conventional autonomous vehicles, energy management is harder and more significant for conventional autonomous vehicles. As such, it is necessary to investigate how to manage energy consumption in conventional autonomous vehicles. In this paper, an energy management system is constructed and analyzed by using a road-power-demand model and an intelligent system to reduce fuel consumption for a conventional autonomous vehicle. The road-power-demand model utilizes three impact factors (i) environment-conditions (ii) driver-behavior, and (iii) vehicle-specifications. The proposed intelligent energy management system includes a fuzzy-logic-system with the aim of generating the desired engine torque, based on the vehicle road power demand and a PID controller to control the air/fuel ratio, by changing the throttle angle. Results show that the intelligent energy management system reduces the vehicle energy consumption from 7.2 to 6.71 L/100 km. Next, the parameters of the fuzzy-logic-system are intelligently optimized by the particle-swarm-optimization method and new results indicate that the vehicle energy consumption is reduced by around 9.58%.
Keywords: Autonomous vehicle | Intelligent energy management | Control strategies | Conventional autonomous vehicle | Fuzzy logic system | Particle swarm optimization | Artificial Intelligence
The impact of big data on firm performance in hotel industry
تأثیر داده های بزرگ بر عملکرد شرکت در صنعت هتلداری-2020
Big data has increasingly appeared as a frontier of opportunity in enhancing firm performance. However, it still is in early stages of introduction and many enterprises are still un-decisive in its adoption. The aim of this study is to propose a theoretical model based on integration of Human-Organization-Technology fit and Technology- Organization-Environment frameworks to identify the key factors affecting big data adoption and its consequent impact on the firm performance. The significant factors are gained from the literature and the research model is developed. Data was collected from top managers and/or owners of SMEs hotels in Malaysia using online survey questionnaire. Structural Equation Modelling (SEM) is used to assess the developed model and Adaptive Neuro- Fuzzy Inference Systems (ANFIS) technique is used to prioritize adoption factors based on their importance levels. The results showed that relative advantage, management support, IT expertise, and external pressure are the most important factors in the technological, organizational, human, and environmental dimensions. The results further revealed that technology is the most important influential dimension. The outcomes of this study can assist the policy makers, businesses and governments to make well-informed decisions in adopting big data.
Keywords: Firm performance | Big data | Hotel industry | Fuzzy logic | Structural equation modelling