Triangular Fuzzy Membership Function Research Articles

Cox Regressive Outlier Robust Incremental Extreme Learning Machine based IoT aware Smart Car Parking and Route Optimization for Intelligent Transportation

An Intelligent Transport System (ITS) is a wide-ranging framework that influences advanced technologies to improve efficiency of transportation networks. An ITS integrated with an IoT enhances the capabilities of transportation networks. Owing to quick development of vehicles, traffic congestion during road network is major problems. Therefore, intelligent traffic control system is required to create smart transportation through optimization of vehicle route, and so on. In this paper, Cox Regressive Fuzzified Outlier Robust Incremental Extreme Learning Machine (CRFORIELM) is developed for smart ITS. The IoT technology in IT’S is used to collect traffic data, vehicle information, Parking slot occupancy data. After data collection process, proposed CRFORIELM technique includes two major processing steps namely parking slot availability detection and traffic aware route optimization. The extreme learning machine includes three types of layers such as input, three hidden layers as well as output layer. Initial hidden layer predicts the parking slot availability using Cox regression is performed. With the parking lot availability predictions, integrate this information into a route optimization algorithm. This algorithm considers the real-time traffic conditions as well as parking availability for detecting optimal route by applying fuzzy triangular membership function in the second hidden layer. Finally, nearest route path is effectively determined to minimize travel time in intelligent transport system. Experimental analysis indicates that the CRFORIELM technique achieved 2%, 5%, 10% improvement in accuracy, sensitivity, specificity and reduced error rates, route detection time by 39%, and 36%, compared to existing methods.

A fuzzy interval optimization approach for p-hub median problem under uncertain information.

Stochastic and robust optimization approaches often result in sub-optimal solutions for the uncertain p-hub median problem when continuous design parameters are discretized to form different environmental scenarios. To solve this problem, this paper proposes a triangular fuzzy number model for the Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem. To enhance the quality and the speed of optimization, a novel optimization approach, combining the triangular fuzzy number evaluation index with the Genetic-Tabu Search algorithm, is proposed. During the iterations of the Genetic-Tabu Search algorithm for finding the optimal solution, the fitness of fuzzy hub schemes is calculated by considering the relative positional relationships of triangular fuzzy number membership functions. This approach directly addresses the triangular fuzzy number model and ensures the integrity of information in the p-hub problem as much as possible. It is verified by the classic Civil Aeronautics Board and several self-constructed data sets. The results indicate that, compared to the traditional Genetic Algorithm and Tabu Search algorithm, the Genetic-Tabu Search algorithm reduces average computation time by 49.05% and 40.93%, respectively. Compared to traditional random, robust, and real-number-based optimization approaches, the proposed optimization approach reduces the total cost in uncertain environments by 1.47%, 2.80%, and 8.85%, respectively.

Wearable sensor-based on exercise monitoring system for disabled the individuals using a multi-attribute fuzzy evaluation mode

Recently, there has been a lot of interest in using the wearable sensors for tracking the exercise progress because of the unbiased accuracy and precision they are provided throughout the continual monitoring. For those with physical impairments, the system’s non-intrusive, lightweight ways of the monitoring activity may ease their load and enhance the quality of their decision-making. As a different measuring unit measures the exercise activity levels recorded by the each wearable sensor, it is challenging to assess the monitoring system. Hence, this paper proposes a Hybridized Fuzzy Multi-Attribute for Exercise Monitoring System (HFMA-EMS) to address the uncertainty issues of the wearable sensors. The Triangular Fuzzy membership function is proposed to begin classifying the observed values. Pair-wise attribute comparison and evaluator weighting in a T-spherical uncertain linguistic set setting utilizing the Techniques for Ordering of Preferences by Similarities to Ideal Solutions (TOPSIS). In the suggested method, a utility function is used to assess the merits of a model in which attribute the weights are calculated, followed by an exercise in which the attributes are ordered employing the Measurements of the Alternative and Ranking Compromise Solutions model (MARCOS). The performance is performed to analyze the proposed method’s accuracy, precision, recall, f1-score, and correct and incorrect exercise assessment by an accelerometer, gyroscope, and magnetic field sensor unit. The application scenario of the HFMA-EMS can be used in the clinical applications, healthcare management, and sports injury detection.

Application of Fuzzy Mamdani Model for Biogas Yield Prediction in Anaerobic Co-Digestion of Decomposable Wastes

Accurate prediction of biogas yield is crucial for optimizing waste-to-energy conversion systems in anaerobic co-digestion processes. In this study, a double input and single output (DISO) fuzzy mamdani model (FMM) was developed for the prediction of biogas yield in a pilot scale of 105-L mesophilic anaerobic sludge bio-digester. The input variables considered are the combination of cow dung and pig waste and the retention time (RT), while the output variable is the experimental biogas yield. Triangular Fuzzy Membership Functions (TFMF) were utilized to define the input and output datasets, and rules were derived from de-fuzzification. Comparative analysis between the FMM's predicted results and experimental values showcased its effectiveness in forecasting biogas yield during the anaerobic co-digestion of the hybrid wastes. Significantly, the FMM consistently produced results with low error values for the sample dataset, underscoring its accuracy even under stochastic conditions. This study emphasizes the FMM's ability to generate predictions with minimal deviations, offering superior results. As a prospect for future research, the implementation of hybrid algorithms may further enhance biogas yield prediction accuracy within waste-to-energy systems.

An Enhanced Security in Medical Image Encryption Using Dynamic Chaotic Fuzzy Based Technique

As IoT and cloud computing have grown in popularity, medical images are now often transmitted between devices or accessed directly from the cloud. With this, the security is always a concern as these images are prone to many types of attack. We have proposed a proven method that is efficient in terms of security, time complexity, and integrity in order to be cloud-friendly so that it may be launched into the cloud and made accessible to users at any time. The goal of the work is to create a dynamic key that, depending on fuzzy values, alters the reproduction rate parameters with each repetition. By applying the last chaotic value created from the previous iteration, the fuzzy triangular membership function has been used in this manner to generate the reproduction rate parameter. The uniqueness and major benefit of the suggested strategy are that it can increase the security of the algorithm that makes use of a chaotic map and a static key. The method has been put forth when designing algorithms so that it should not only demonstrate security against different attacks but also provide efficiency towards computational complexity. The technique has been tested against a set of images and an existing algorithm using a variety of security metrics, including the correlation coefficient, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and entropy. It has been determined from the comparative analysis that the proposed approach can make the existing algorithm more secure.

An Experimental Private Small Hydropower Plant Investments Selection Classification System

Investment selection problems and models are crucial for humans, communities, and states. Private small hydroelectric power/ hydropower plant investments (PSHPPIs) selection problem is a unique one in those problems and models. Many available scientific methods can be implemented on this research topic. One methods group is type-2 Mamdani’s type fuzzy inference systems (FISs). This study presents an experimental one-node general type-2 4-zslices Mamdani’s type FIS model for PSHPPIs selection. There are 3 input variables (total estimated electricity generation: M1, total estimated cost: M2, change in the average runoff: M3), 1 output variable (O1: selected/unselected, selection classification of PSHPPIs), and 49 rules. M1 is designed in 5 triangular and trapezoidal general type-2 4-zslices fuzzy membership functions (MFs), M2 in 5 triangular and trapezoidal general type-2 4-zslices fuzzy MFs, M3 in 5 Gaussian type-2 4-zslices fuzzy MFs, and O1 in 2 Gaussian type-2 4-zslices fuzzy MFs. All modeling details are finalized by a human-expert decision process. JuzzyOnline V2.0, a browser-based platform of Juzzy (a Java-based library: Juzzy toolkit), is used for all FIS modeling and calculation activities. Five PSHPPI options assumed to be in their very early investment stages in Turkiye are evaluated in an experimental application. It is clear that an improved, tuned, and fine-tuned version of this model will help private investors to take more convenient/satisfying actions/decisions in a very short evaluation period in their PSHPPIs decision processes. An automatic investment selection classification system can be built in an appropriate development period after experiencing many methods and models. This study is one of the open-science humanoid robot, robot and platform development activities entitled Global Power Robots and Platforms: GPRP/GPRAP (Global Power Plants Developers: GP2D/G2PD/GPPD, Global Power Plants Engineers: GP2E/G2PE/ GPPE, Global Power Plants Owners: GP2O/G2PO/GPOO, Global Power Prediction Systems: GP2S/G2PS/GPPS), Global Profile Analyses Systems, and Global Social Network Analyses Systems.

A Novel Image Encryption Scheme Using Chaotic Maps and Fuzzy Numbers for Secure Transmission of Information

The complexity of chaotic systems, if used in information encryption, can determine the status of security. The paper proposes a novel image encryption scheme that uses chaotic maps and fuzzy numbers for the secure transmission of information. The encryption method combines logistic and sine maps to form the logistic sine map, as well as the fuzzy concept and the Hénon map to form the fuzzy Hénon map, in which these maps are used to generate secure secret keys, respectively. Additionally, a fuzzy triangular membership function is used to modify the initial conditions of the maps during the diffusion process. The encryption process involves scrambling the image pixels, summing adjacent row values, and XORing the result with randomly generated numbers from the chaotic maps. The proposed method is tested against various attacks, including statistical attack analysis, local entropy analysis, differential attack analysis, signal-to-noise ratio, signal-to-noise distortion ratio, mean error square, brute force attack analysis, and information entropy analysis, while the randomness number has been evaluated using the NIST test. This scheme also has a high key sensitivity, which means that a small change in the secret keys can result in a significant change in the encrypted image The results demonstrate the effectiveness of the proposed scheme in ensuring the secure transmission of information.

Alternate and Effective Dissolved Gas Interpretation to Understand the Transformer Incipient Faults

Various techniques of fault diagnosis in oil immersed power transformers have been utilized for the last two decades and are well reported in the literature. The traditional and widely accepted dissolved gas analysis methods include IEC gas ratio code, IEEE Key gas, Doernenburg ratios and the Duval triangle. Amongst these methods, Duval triangle method is reported to be the most prominent. A new fault gas interpretation approach has been proposed in the present work. This method combines three Duval triangles with triangular membership functions evaluated empirically for diagnosing the incipient faults. Like the existing Duval triangles, five gases, namely hydrogen, methane, ethylene, ethane, and acetylene, are considered for the present study. Initially, the relevant gas percentages for the three Duval triangles are calculated and later converted into triangular fuzzy membership functions. Finally, an expert rule-based method synthesizes the outcomes into a specific main fault type or sub-fault type. Test data of 139 transformers are used to compare the proposed method with the conventional Duval triangle method. The proposed approach is simple and can potentially determine the main faults and sub-faults simultaneously based on numerical indices in a single fuzzy logic system. In addition, the proposed method has overcome the ambiguity in overlapping faults in traditional methods.

Developing a Controlling Model for Analyzing the Subjectivity of Enterprise Sustainability and Expert Group Judgments Using Fuzzy Triangular Membership Functions

The evaluation of sustainability is complex, involving several factors and expert opinions. In our research, we analyzed and evaluated the activities of a premium packaging material manufacturer with the participation of three relevant and professional groups. Various expectations have been defined in the form of plan values. From these plan values, we measured and evaluated the sustainability level of the company with the help of plan-fact ratios, fuzzy logic and triangular functions, enabling the subjective evaluation and integration of different opinions into the model. The purpose of our model is to support enterprise decision-making by taking into account sustainability aspects and the different expectations of interested parties. Our model helps identify intervention points and manages subjectivity in the field so that the decisions of enterprise managers better reflect the expectations and perspectives of those involved. The model we built is significantly different from previously used and developed indexes, as it functions as an enterprise-controlling model and index during sustainability evaluation. The developed model can also integrate company-specific and global sustainability indicators. Our research contributes to the development of sustainability evaluation methodology and the scientific examination of enterprise decision-making models. The controlling model we developed offers an integrated approach to managing the subjectivity of sustainability evaluation and the different expectations of stakeholder groups.

Network intrusion detection using data dimensions reduction techniques

Due to the increasing growth of the Internet and its widespread application, the number of attacks on the network has also increased. Therefore, maintaining network security and using intrusion detection systems is of critical importance. The connection between devices leads to a large number of data being generated and saved. The era of “big data” emerges over time. This paper presents a new method for selecting effective features on network intrusion detection based on the concept of fuzzy numbers and scoring methods based on correlation feature selection for intrusion detection systems. The goal of this paper is to present a new approach for reducing data size using the concept of fuzzy numbers and scoring methods based on correlation feature selection for intrusion detection systems. In this method, to eliminate inefficient features and reduce data dimensions, number of features are defined as a fuzzy number, and the heuristic function of the correlation-based feature selection algorithm is expressed as a triangular fuzzy number membership function. To evaluate the proposed method, it is then compared to previous intrusion detection methods. The results show that the proposed method selects several features less than the conventional methods with a higher detection rate. The proposed method is compared with the correlation-based feature selection method on two datasets. The proposed method is evaluated and validated on KDD Cup, NSL-KDD and CICIDS datasets. The achieved accuracy is 99.9% which is 96.01% with CFS method.

Probabilistic stability analysis of anchored cantilever sheet pile walls using fuzzy set theory

Ensuring the safety and stability of waterfront retaining structures is of utmost importance. However, uncertainties may lead to imprecise analysis and inadequate or uneconomical design. Fuzzy set theory is one of the computationally efficient methods of evaluating the effects of uncertainties on systems and has a great potential of making probability-based stability analysis of sheet piles considerably less cumbersome. This paper analyzes the effects of uncertainty on the stability of anchored cantilever sheet pile walls in the sand using the fuzzy set theory and genetic algorithm. The uncertain variables include soil properties as well as structural and geometrical parameters. Different levels of uncertainty are assigned to the governing parameters using the triangular fuzzy membership functions and the α-cut method. To generate the fuzzy responses for wall stability, a search for extreme (minimum and maximum) safety factors is carried using evolutionary algorithm at various cut levels. The proposed method is used in this study to investigate the effects of uncertainty in soil and wall parameters on overturning, sliding, and flexural stabilities, as well as the overall wall stability. In addition, the effects of changes in water level, anchor tensile force and wall section modulus on the probability of different failure modes were determined. Results confirm the previous findings in the literature on significant impact of uncertainty in these parameters on the wall stability. Conducting a sensitivity analysis, it was found that the uncertainty in water level is an effective factor on all failure modes. Reduction of the water level leads to a decrease in the probability of flexural failure, while it increases the probability of overturning and sliding failures. The provided diagrams are also useful for probability-based design against various modes of failure, in engineering practice. The results emphasize the robustness of the presented fuzzy approach to account for the uncertainties of input variables in the sheet pile wall design procedure.

Selection of sustainable and green suppliers using a fuzzy R-method in group decision-making situations

A growing number of companies treat "sustainability" as an important objective in their strategy. Sustainable and green suppliers increase efficiency by reducing costs and waste in industries. Sustainable and green supplier selection is a critically important factor in moving forward along the path of sustainability. A fuzzy R-method is proposed in this paper based on ranking the fuzzy numbers of alternative suppliers and criteria. Triangular fuzzy membership function and different fuzzy scales are presented to demonstrate and validate the proposed method. Fuzzy composite scores are generated, and these scores are converted into crisp forms to evaluate the alternative suppliers. The novelty of the proposed method is that it is simple, can deal with qualitative and quantitative criteria of supplier selection, and requires less computational effort for evaluating and ranking the green and sustainable suppliers in fuzzy group decision-making situations. The weights of the criteria and alternative suppliers are generated using a simple equation and hence there is no need to apply different methods for weights generation and ranking. Furthermore, this method does not require normalization of the data. Two realistic group decision-making problems of green and sustainable supplier selection to test the method. Sensitivity analysis for the proposed method is also conducted to check the consistency of the proposed method to different weights of the criteria. The proposed method is effective, robust, and competitive to the existing multi-criteria decision making (MCDM) methods of sustainable supplier selection.

Health status evaluation of photovoltaic array based on deep belief network and Hausdorff distance

Photovoltaic (PV) arrays, as the core part of PV plants, are sensitive to the complex environment that can lead to fluctuations in their power generation performance. The health status evaluation (HSE) of PV arrays is beneficial for routine maintenance and economic value evaluation. In this paper, a method for evaluating the health status of PV array based on deep belief network (DBN) and Hausdorff distance (HD) is proposed. First, the I–V curves of the PV array are preprocessed, including curve filtering and points redistribution. Then, the practical features of I–V characteristics are extracted by DBN. Next, the health indicator (HI) of the PV array is constructed by HD and Logistic function. Finally, the triangular fuzzy membership function is used to build the mapping relationship between the HI values and the health grades of the PV array. The proposed method enables fully extracting the features from the I–V characteristics of PV arrays and gives an accurate evaluation of different states of PV arrays. The experimental results show that the proposed HSE method can realize the expected objectives.

A GIS-based offshore wind site selection model using fuzzy multi-criteria decision-making with application to the case of the Gulf of Maine

In the last decades, a considerable number of studies have been conducted to find the optimal locations for renewable energy facilities. The reviewed literature demonstrates how the combination of spatial representation computer tools, such as geographic information systems (GIS), with multi-criteria decision making (MCDM) methodologies, has successfully solved the problem of identifying optimal locations. Furthermore, since the appearance of fuzzy logic, the combination approaches have extended to the field of fuzzy sets to consider the imprecision and vagueness that some criteria may involve. In this paper, we propose a comparative analysis among fuzzy versions of MCDM methodologies, including GIS technologies, for the optimal site selection of offshore wind power plants. With this aim, we combined a classical pair-wise comparison method (AHP) with two distance-based approaches (TOPSIS and VIKOR), applying GIS software and comparing the two most extended fuzzy membership functions: triangular and linear. As a case study, this optimal location problem was applied to offshore wind site selection in the Gulf of Maine (USA). Initially, 56 alternatives for potential locations were identified from 22,331 km2 study area. After applying the AHP methodology, the weights of the criteria were obtained, turning out to be the wind speed and bathymetry the most important criteria. The results demonstrate the robustness of the fuzzy TOPSIS methodology against potential variations in the criteria weights, since the best alternatives (optimal locations) and almost 90% of the 25 top-ranked alternatives were matching. Likewise, the rankings of alternatives illustrate that the use of triangular or linear fuzzy membership functions does not cause significant differences after applying the fuzzy VIKOR methodology and ArcGIS software. In fact, the most appropriate alternative is the same for both cases, and there is only an exchange of positions among the top-ranked alternatives. The proposed solutions can be applied to other locations and both onshore and offshore installations.

A Comparative Study of Gradient Descent Method and a Novel Non-Gradient Method for Structural Shape Optimization

Motivated by the works on non-gradient techniques in the domain of shape optimization of the structure, the present work intends to suggest a novel non-gradient procedure for shape optimization of structures and compare it to an existing gradient-based method. The presented technique optimizes the shape of structural parts using a fuzzy controlled integrated zero-order methodology incorporating the notion of design elements and automated mesh construction with mesh refinement at each iteration. The movement of nodes and convergence monitoring is taken care of using the triangular fuzzy membership function. The changes in shape occur according to the selected target maximum shear stress (σt) with a view of reaching as near to the target as possible at all the points. The present methodology is packaged in a piece of software termed GSO (Gradientless shape optimization) coded in FORTRAN language. To explain the efficacy of the current approach, a few basic structural shapes have been optimized under various constraints, and the results of the same are compared to those obtained using Optistruct (a part of software suite HyperWorks from Altair engineering), which works on gradient descent method. The proposed approach works well and produces more industry fabricable results than what is produced by the gradient descent method in Optistruct.

Trilateral Filterative Hermitian feature transformed deep perceptive fuzzy neural network for finger vein verification

Automated person recognition is an essential field to preserve individual privacy in biometrics. A conventional system performs the recognition, but inaccurate detection of finger-vein lines causes lesser verification accuracy. To address this problem, a novel Trilateral Filterative Hermitian Feature Transformation based Deep Perceptive Fuzzy Neural Network (TFHFT-DPFNN) model is introduced for the capability of learning biometric features that offers robust and accurate vein image verification with minimum time. The proposed TFHFT-DPFNN model performs four different processing stages in the network layers. In the first stage, multiple finger vein images are captured from the database. Then the input images are taken to perform the denoising in the second stage by applying a guided trilateral filter to smooth the input image for accurate matching with minimum time consumption. Followed by, Hermitian Hat wavelet transformation is applied to perform decomposition and feature extraction from the input image. Then, the extracted features are transferred to the next hidden layer where the feature matching process is carried by applying the Jaccard similarity index. Based on the similarity value, the finger vein verification is performed through the fuzzy triangular membership function at the output layer. Finally, the verified results are obtained at the output layer with minimum error. Experimental evaluation is carried out with training images and testing images on different factors such as peak signal-to-noise ratio, finger vein verification accuracy, false-positive rate, and computation time with the number of finger vein images. The proposed TFHFT-DPFNN model offers promising outcomes in terms of higher verification accuracy and lesser false-positive rate with minimum time consumption than the conventional methods.

Numerical analysis of a second-grade fuzzy hybrid nanofluid flow and heat transfer over a permeable stretching/shrinking sheet

In this work, the heat transfer features and stagnation point flow of Magnetohydrodynamics (MHD) hybrid second-grade nanofluid through a convectively heated permeable shrinking/stretching sheet is reported. The purpose of the present investigation is to consider hybrid nanofluids comprising of Alumina left( {{text{Al}}_{{2}} {text{O}}_{{3}} } right) and Copper left( {{text{Cu}}} right) nanoparticles within the Sodium Alginate (SA) as a host fluid for boosting the heat transfer rate. Also, the effects of free convection, viscous dissipation, heat source/sink, and nonlinear thermal radiation are considered. The converted nonlinear coupled fuzzy differential equations (FDEs) with the help of triangular fuzzy numbers (TFNs) are solved using the numerical scheme bvp4c. The numerical results are acquired for various engineering parameters to study the Nusselt number, skin friction coefficient, velocity, and temperature distribution through figures and tables. For the validation, the current numerical results were found to be good as compared to existing results in limiting cases. It is also inspected by this work that with the enhancement of the volume fraction of nanoparticles, the heat transfer rate also increases. So, it may be taken as a fuzzy parameter for a better understanding of fuzzy variables. For the comparison, the volume fraction of nanofluids and hybrid nanofluid are said to be TFN [0, 0.1, 0.2]. In the end, we can see that fuzzy triangular membership functions (MFs) have not only helped to overcome the computational cost but also given better accuracy than the existent results. Finding from fuzzy MFs, the performance of hybrid nanofluids is better than nanofluids.

Fuzzy-set Theory to Support the Design of an Augmentative and Alternative Communication Systems for Aphasia Individuals

This paper presents a new design of an Augmentative and Alternative Communication (AAC) systems for conveying delicate feelings or emotions of aphasia individuals, which is based on Fuzzy-set theory. Fuzzy-set theory is crucial in addressing the ambiguity of linguistic terms used and judgments made by aphasia individuals. Due to the communication difficulties of aphasia individuals, their insights were assigned in triangular fuzzy membership functions during the design process of AAC systems. In the proposed design of AAC systems, the delicate feelings or emotions were expressed as a scale, and candidate(s) of delicate feelings or emotions were shown based on their specified position. If the candidate(s) cannot properly convey the desired delicate feelings or emotions, then the corresponding fuzzy membership function can be realized by controlling its position. The proposed method has the advantage of being able to be conveyed the exact want and needs of delicate feelings or emotions during communication. Experimental result shows that conveying delicate feelings or emotions of the aphasia individual could be improved by 50 percent using the proposed design of AAC systems.

An Inference System for Classifying Oil Palm Fungal Diseases

The oil palm plant is one of the major important cash crops of the Nigerian economy and a significant contributor to the world market for vegetable oils. Unfortunately, infection with fungi has caused a decline in the productivity of oil palms and subsequently the palm oil industry. Hence the need to detect oil palm plant disease earlier before it affects it informed this research to develop a fuzzy inference model to predict the influence of fungal disease on the oil plant plant. Following extensive review of related works, the factors associated with the severity of fungal diseases in the oil palm plant were identified following validation by Botanist. Fuzzy triangular membership functions were used to formulate the input factors identified alongside the target variables for identifying the severity of fungal diseases affecting the oil palm plant. The rule base was formulated using IF-THEN statements to combine the values of the input factors with the respective values of the target severity of oil palm plant disease. The classification model for oil palm plant disease severity was simulated using the Fuzzy Logic Toolbox available in the MATLAB R2015b Software. The results showed that the developed inference system for oil palm plant was capable of classifying and predicting the degree of the fungal disease infection into four groups; no severity, low severity, moderate severity and high severity.

(DA-DOPF): A Day-Ahead Dynamic Optimal Power Flow With Renewable Energy Integration in Smart Grids

The performance of a power system can be measured and evaluated by its power flow analysis. Along with the penetration of renewable energies such as wind and solar, the power flow problem has become a complex optimization problem. In addition to this, constraint handling is another challenging task of this problem. The main critical problem of this dynamic power system having such variable energy sources is the intermittency of these VESs and complexity of constraint handling for a real-time optimal power flow (RT-OPF) problem. Therefore, optimal scheduling of generation sources with constraint satisfaction is the main goal of this study. Hence, a renewable energy forecasting–based, day-ahead dynamic optimal power flow (DA-DOPF) is presented in this paper with the forecasting of solar and wind patterns by using artificial neural networks. Moreover, contribution factors are calculated using triangular fuzzy membership function (T-FMF) in the sub-interval time slots. Furthermore, the superiority of feasible (SF) solution constraint handling approach is used to avoid the constraint violation of inequality constraints of optimal power flow. The IEEE 30-bus transmission network has been amended to integrate a solar photovoltaic and wind farm in different buses. In this approach, the computing program is based on MATPOWER which is a tool of MATLAB for load flow analysis which uses the Newton–Raphson technique because of its rapid convergence. Meteorological information has been gathered during the time frame January 1, 2015, to December 31, 2017, from Danyore Weather Station (DWS) at Hunza, Pakistan. A Levenberg–Marquardt calculation–based artificial neural network model is utilized to foresee the breeze speed and sunlight-based irradiance in light of its versatile nature. Finally, the results are discussed analytically to select the best generation schedule and control variable values.