An Intelligent System for Load Forecasting on a Distribution Network

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Saheed Gbadamosi ◽  
Ojo Olukayode Adedayo
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Distribution Network ◽  
Load Forecasting ◽  
Integrated System ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Load Forecast ◽  
Artificial Neural Network Ann

This paper presents an Adaptive Neuro-Fuzzy Inference system (ANFIS) technique for medium-term load forecasting in a distribution network. This technique is an integrated system consisting of fuzzy logic systems and Artificial Neural network (ANN). The inputs to the system include days of the week, temperature, time, current and previous hourly load on the distribution network. The data collection is within the period of two years. The formulation and mapping of the input data is done using fuzzy logic system and ANN are employed for generation of inference. The experimental results show the average load pre-diction accuracy of 87.23% and regression coefficient of 0.873. The analysis of the proposed ANFIS for load forecast is effective in planning, managing and organizing the electric load forecast with accurate prediction.

scholarly journals An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

An Adaptive Neuro-Fuzzy Inference System in Assessment of Technical Losses in Distribution Networks

2016 ◽  
Vol 6 (3) ◽  
pp. 1294
Author(s):  
Dragan Mlakić ◽  
Srete N Nikolovski ◽  
Goran Knežević
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Low Voltage ◽  
Distribution Networks ◽  
Investment Planning ◽  
Smart Meters ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Neuro Fuzzy

The losses in distribution networks have always been key elements in predicting investment, planning work, evaluating the efficiency and effectiveness of a network. This paper elaborates on the use of fuzzy logic systems in analyzing the data from a particular substation area predicting losses in the low voltage network. The data collected from the field were obtained from the Automatic Meter Reading (AMR) and Automatic Meter Management (AMM) systems. The AMR system is fully implemented in EPHZHB and integrated within the network infrastructure at secondary level substations 35/10kV and 10(20)/0.4 kV. The AMM system is partially implemented in the areas of electrical energy consumers; precisely, in accounting meters. Daily information gathered from these systems is of great value for the calculation of technical and non-technical losses. Fuzzy logic in combination with the Artificial Neural Networks implemented via the Adaptive Neuro-Fuzzy Inference System (ANFIS) is used. Finally, FIS Sugeno, FIS Mamdani and ANFIS are compared with the measured data from smart meters and presented with their errors and graphs.

scholarly journals A Novel Fuzzy Inference System Based Robust Reversible Watermarking Technique provided with Six Layer Security

2021 ◽  
Vol 9 (2) ◽  
pp. 661-678
Author(s):  
V. Belmer Gladson, Dr. R. Balasubrimanian
Keyword(s):  
Fuzzy Logic ◽  
Digital Media ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Image Watermarking ◽  
Similarity Index ◽  
Structural Similarity ◽  
Fuzzy Logic Systems ◽  
Inference System ◽  
Inference Models

Digital Watermarking has evolved as one of the latest technologies for digital media copyright protection. Watermarking of images can be done in many ways and one of the proposed algorithms for image watermarking is by utilizing Fuzzy Logic. It is similar to the concept of a Fuzzy set, each element can be defined by an ordered pair, in which one is the value and other is the membership function value. Fuzzy logic systems can explain inaccurate information and explain their decisions. Fuzzy inference system is the simplest way of performing Fuzzy Logic. In the proposed method, three Fuzzy inference models are used to generate the weighing factor for embedding the watermark and input to the Fuzzy Inference System is taken from the Human Visual System model. The Performance measures used in the Process are Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Normalized Cross Correlation (NCC) and Bit Error Ratio (BER). The Proposed algorithm is immune to various Image Processing attacks.

scholarly journals Prediction of Attendance Demand in European Football Games: Comparison of ANFIS, Fuzzy Logic, and ANN

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Mehmet Şahin ◽  
Rızvan Erol
Keyword(s):  
Fuzzy Logic ◽  
Fuzzy Inference ◽  
Fuzzy Rule ◽  
Optimization Methods ◽  
Ann Model ◽  
Absolute Deviation ◽  
Inference System ◽  
European Football ◽  
Artificial Neural Network Ann ◽  
Adaptive Neurofuzzy Inference System

An artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS) models, and fuzzy rule-based system (FRBS) models are developed to predict the attendance demand in European football games, in this paper. To determine the most successful method, each of the methods is analyzed under different situations. The Elman backpropagation, feed-forward backpropagation, and cascade-forward backpropagation network types are developed to determine the outperforming ANN model. The backpropagation and hybrid optimization methods are used for training fuzzy inference system (FIS) to determine the outperforming ANFIS model. The fuzzy logic model is developed after experimenting different forms of membership functions. To this end, the data of 236 soccer games are used to train the ANN and ANFIS models, and 2017/2018 season’s data of these clubs are used to test all of the models. The results of all models are compared with each other and real past data. To assess the performance of each model, two error measures that are Mean Absolute Percent Error (MAPE) and Mean Absolute Deviation (MAD) are implemented. These measures reveal that the ANN model that has Elman network type outperforms the other models. Finally, the results emphasize that the proposed ANN model can be effectively used for prediction purposes.

IMPLEMENTASI PENENTUAN ARSITEKTUR MASJID MODERN DI ACEH MENGGUNAKAN PEMODELAN SISTEM CERDAS

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Soraya Masthura Hasan ◽  
T Iqbal Faridiansyah
Keyword(s):  
Fuzzy Inference System ◽  
Architectural Design ◽  
Fuzzy Inference ◽  
Intelligent System ◽  
Selection Process ◽  
System Modeling ◽  
Inference System ◽  
Normal Limits ◽  
Roof Structure ◽  
Selection Of

Mosque architectural design is based on Islamic culture as an approach to objects and products from the Islamic community by looking at their suitability and values and basic principles of Islam that explore more creative and innovative ideas. The purpose of this system is to help the team and the community in seeing the best mosque in the top order so that the system can be used as a reference for the team and the community. The variables used in the selection of modern mosques include facilities and infrastructure, building structure, roof structure, mosque area, level of security and facilities. The system model used is a fuzzy promethee model that is used for the modern mosque selection process. Fuzzy inference assessment is used to determine the value of each variable so that the value remains at normal limits. Fuzzy values will then be included in promethee assessment aspects. The highest promethee ranking results will be made a priority for the best mosque ranking. This fuzzy inference system and promethee system can help the management team and the community in determining the selection of modern mosques in aceh in accordance with modern mosque architecture. Intelligent System Modeling System In Determining Modern Mosque Architecture in the City of Aceh, this building will be web based so that all elements of society can see the best mosque in Aceh by being assessed by all elements of modern mosque architecture.Keywords: Fuzzy inference system, Promethe, Option of  Masjid

scholarly journals Global Image Thresholding Adaptive Neuro-Fuzzy Inference System Trained with Fuzzy Inclusion and Entropy Measures

Symmetry ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 286 ◽  
Author(s):  
Athanasios Bogiatzis ◽  
Basil Papadopoulos
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Initial Step ◽  
Local Area ◽  
Mathematical Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Entropy Measures ◽  
Complex Procedure ◽  
Artificial Neural Network Ann

Thresholding algorithms segment an image into two parts (foreground and background) by producing a binary version of our initial input. It is a complex procedure (due to the distinctive characteristics of each image) which often constitutes the initial step of other image processing or computer vision applications. Global techniques calculate a single threshold for the whole image while local techniques calculate a different threshold for each pixel based on specific attributes of its local area. In some of our previous work, we introduced some specific fuzzy inclusion and entropy measures which we efficiently managed to use on both global and local thresholding. The general method which we presented was an open and adaptable procedure, it was free of sensitivity or bias parameters and it involved image classification, mathematical functions, a fuzzy symmetrical triangular number and some criteria of choosing between two possible thresholds. Here, we continue this research and try to avoid all these by automatically connecting our measures with the wanted threshold using some Artificial Neural Network (ANN). Using an ANN in image segmentation is not uncommon especially in the domain of medical images. However, our proposition involves the use of an Adaptive Neuro-Fuzzy Inference System (ANFIS) which means that all we need is a proper database. It is a simple and immediate method which could provide researchers with an alternative approach to the thresholding problem considering that they probably have at their disposal some appropriate and specialized data.

scholarly journals Vessel Multi-Parametric Collision Avoidance Decision Model: Fuzzy Approach

2021 ◽  
Vol 9 (1) ◽  
pp. 49
Author(s):  
Tanja Brcko ◽  
Andrej Androjna ◽  
Jure Srše ◽  
Renata Boć
Keyword(s):  
Fuzzy Logic ◽  
Collision Avoidance ◽  
Decision Model ◽  
Fuzzy Inference ◽  
Weather Conditions ◽  
Target Vessel ◽  
Planning Tool ◽  
Inference System ◽  
International Regulations ◽  
Input Variables

The application of fuzzy logic is an effective approach to a variety of circumstances, including solutions to maritime anti-collision problems. The article presents an upgrade of the radar navigation system, in particular, its collision avoidance planning tool, using a decision model that combines dynamic parameters into one decision—the collision avoidance course. In this paper, a multi-parametric decision model based on fuzzy logic is proposed. The model calculates course alteration in a collision avoidance situation. First, the model collects input data of the target vessel and assesses the collision risk. Using time delay, four parameters are calculated for further processing as input variables for a fuzzy inference system. Then, the fuzzy logic method is used to calculate the course alteration, which considers the vessel’s safety domain and International Regulations for Preventing Collisions at Sea (COLREGs). The special feature of the decision model is its tuning with the results of the database of correct solutions obtained with the manual radar plotting method. The validation was carried out with six selected cases simulating encounters with the target vessel in the open sea from different angles and at any visibility. The results of the case studies have shown that the decision model computes well in situations where the own vessel is in a give-way position. In addition, the model provides good results in situations when the target vessel violates COLREG rules. The collision avoidance planning tool can be automated and serve as a basis for further implementation of a model that considers the manoeuvrability of the vessels, weather conditions, and multi-vessel encounter situations.

scholarly journals A Soft Computing Approach to Crack Detection and Impact Source Identification with Field-Programmable Gate Array Implementation

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Arati M. Dixit ◽  
Harpreet Singh
Keyword(s):  
Fuzzy Logic ◽  
Soft Computing ◽  
Fuzzy Inference System ◽  
Source Identification ◽  
Field Programmable Gate Array ◽  
Crack Detection ◽  
Fuzzy Inference ◽  
Inference System ◽  
Field Programmable ◽  
Computing Approach

The real-time nondestructive testing (NDT) for crack detection and impact source identification (CDISI) has attracted the researchers from diverse areas. This is apparent from the current work in the literature. CDISI has usually been performed by visual assessment of waveforms generated by a standard data acquisition system. In this paper we suggest an automation of CDISI for metal armor plates using a soft computing approach by developing a fuzzy inference system to effectively deal with this problem. It is also advantageous to develop a chip that can contribute towards real time CDISI. The objective of this paper is to report on efforts to develop an automated CDISI procedure and to formulate a technique such that the proposed method can be easily implemented on a chip. The CDISI fuzzy inference system is developed using MATLAB’s fuzzy logic toolbox. A VLSI circuit for CDISI is developed on basis of fuzzy logic model using Verilog, a hardware description language (HDL). The Xilinx ISE WebPACK9.1i is used for design, synthesis, implementation, and verification. The CDISI field-programmable gate array (FPGA) implementation is done using Xilinx’s Spartan 3 FPGA. SynaptiCAD’s Verilog Simulators—VeriLogger PRO and ModelSim—are used as the software simulation and debug environment.

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