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
A Type-2 Fuzzy Logic Approach to Explainable AI for regulatory compliance, fair customer outcomes and market stability in the Global Financial Sector
رویکرد منطق فازی نوع 2 به هوش مصنوعی قابل توضیح برای انطباق با مقررات ، نتایج عادلانه مشتری و ثبات بازار در بخش مالی جهانی-2020
The field of Artificial Intelligence (AI) is enjoying unprecedented success and is dramatically transforming the landscape of the financial services industry. However, there is a strong need to develop an accountability and explainability framework for AI in financial services, based on a risk-based assessment of appropriate explainability levels and techniques by use case and domain. This paper proposes a risk management framework for the implementation of AI in banking with consideration of explainability and outlines the implementation requirements to enable AI to achieve positive outcomes for financial institutions and the customers, markets and societies they serve. The work presents the evaluation of three algorithmic approaches (Neural Networks, Logistic Regression and Type 2 Fuzzy Logic with evolutionary optimisation) for nine banking use cases. We review the emerging regulatory and industry guidance on ethical and safe adoption of AI from key markets worldwide and compare leading AI explainability techniques. We will show that the Type-2 Fuzzy Logic models deliver very good performance which is comparable to or lagging marginally behind the Neural Network models in terms of accuracy, but outperform all models for explainability, thus they are recommended as a suitable machine learning approach for use cases in financial services from an explainability perspective. This research is important for several reasons: (i) there is limited knowledge and understanding of the potential for Type-2 Fuzzy Logic as a highly adaptable, high performing, explainable AI technique; (ii) there is limited cross discipline understanding between financial services and AI expertise and this work aims to bridge that gap; (iii) regulatory thinking is evolving with limited guidance worldwide and this work aims to support that thinking; (iv) it is important that banks retain customer trust and maintain market stability as adoption of AI increases.
Keywords: Regulatory Compliance | Accountability and Explainability | Type-2 Fuzzy Logic | Neural Networks
Constrained Interval Type-2 Fuzzy Classification Systems for Explainable AI (XAI)
سیستم های طبقه بندی فازی فاصله محدود نوع 2 برای هوش مصنوعی قابل توضیح (XAI)-2020
In recent year, there has been a growing need for intelligent systems that not only are able to provide reliable classifications but can also produce explanations for the decisions they make. The demand for increased explainability has led to the emergence of explainable artificial intelligence (XAI) as a specific research field. In this context, fuzzy logic systems represent a promising tool thanks to their inherently interpretable structure. The use of a rule-base and linguistic terms, in fact, have allowed researchers to create models that are able to produce explanations in natural language for each of the classifications they make. So far, however, designing systems that make use of interval type-2 (IT2) fuzzy logic and also give explanations for their outputs has been very challenging, partially due to the presence of the type-reduction step. In this paper, it will be shown how constrained interval type-2 (CIT2) fuzzy sets represent a valid alternative to conventional interval type-2 sets in order to address this issue. Through the analysis of two case studies from the medical domain, it is shown how explainable CIT2 classifiers are produced. These systems can explain which rules contributed to the creation of each of the endpoints of the output interval centroid, while showing (in these examples) the same level of accuracy as their IT2 counterpart.
Index Terms: Constrained interval type-2 | XAI | explainable type-2 fuzzy systems
Hybrid Deep Learning Type-2 Fuzzy Logic Systems For Explainable AI
سیستم های منطق فازی نوع 2 یادگیری عمیق ترکیبی برای هوش مصنوعی قابل توضیح-2020
The recent years have witnessed a rapid rise in the use of Artificial Intelligence (AI) systems, in particular Machine Learning (ML) models. The vast majority of AI systems employ black box models that lack transparency in operation and decision making. This lack of transparency curtails the use of these AI systems in regulated applications (such as medical, financial applications, etc.) where it is important to understand the reasoning behind the predictions of the AI system. In these situations, interpretable models need to be used. However, interpretable models can turn into black-box models for high dimensional inputs. There are a variety of approaches that have been proposed to solve this problem. In this paper, we present a novel hybrid deep learning type-2 fuzzy logic system for explainable AI which addresses these challenges to provide a highly interpretable model that has reasonable performance when compared to the other black box models.
Keywords: Explainable Artificial Intelligence | Interval Type-2 Fuzzy Logic System | Deep Learning
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
Application of AI for Frequency Normalization of Solar PV-Thermal Electrical Power System
کاربرد هوش مصنوعی برای عادی سازی فرکانس سیستم برق الکتریکی حرارتی خورشیدی PV-2020
Grid-connected solar-PV schemes have become a significant part of the energy balance in the power system to satisfy the growing request for clean, affordable energy. This study attempts to link solar-PV generation with conventional thermal power plants and to integrate the control zone resulting in a hybrid solar PV-thermal electric power system using an AC tie line. An analysis of the frequency dynamics for varying load conditions of the interconnected system is studied. Diverse approaches of proportional, integral, and proportional-integral fuzzy logic built controllers are design and tested in order to match the electric power with variable loads of the system and hence to normalize the frequency ofthe system in shortest possible time. A comparative analysis of the design topologies is conducted out for the PV-Thermal scheme. Results obtain from the implementation are shown to justify the performance of proposed control efforts, using MATLAB software tool.
Keywords: Solar PV-Thermal electrical power system | frequency dynamics | Proportional| Integral | FLPI control