scholarly journals A Neuro-fuzzy Approach for Predicting Load Peak Profile

2015 ◽  
Vol 5 (6) ◽  
pp. 1304 ◽  
Author(s):  
Abdellah Draidi ◽  
Djamel Labed
Keyword(s):  
Power Systems ◽  
Fuzzy Inference ◽  
Load Forecasting ◽  
Infrastructure Development ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables ◽  
Load Peak ◽  
Inference Systems ◽  
Mathematical Process

<p>Load forecasting has many applications for power systems, including energy purchasing and generation, load switching, contract evaluation, and infrastructure development.</p> <p>Load forecasting is a complex mathematical process characterized by random data and a multitude of input variables.To solve load forecasting, two different approaches are used, the traditional and the intelligent one.Intelligent systems have proved their efficiency in load forecasting domain.</p> <p>Adaptive neuro-fuzzy inference systems (ANFIS) are a combination of two intelligent techniques where we can get neural networks and fuzzy logics advantages simultaneously.</p> In this paper, we will forecast night load peak of Algerian power system using multivariate input adaptive neuro-fuzzy inference system (ANFIS) introducing the effect of the temperature and type of the day as input variables.

Artificial neural networks and adaptive neuro-fuzzy inference systems for parameter identification of dynamic systems

2020 ◽  
Vol 39 (5) ◽  
pp. 6145-6155
Author(s):  
Ramin Vatankhah ◽  
Mohammad Ghanatian
Keyword(s):  
Differential Equations ◽  
Dynamic Systems ◽  
Fuzzy Inference ◽  
Dynamic Equations ◽  
Control Analysis ◽  
Unknown Parameters ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Artificial Neural ◽  
Inference Systems

There would always be some unknown geometric, inertial or any other kinds of parameters in governing differential equations of dynamic systems. These parameters are needed to be numerically specified in order to make these dynamic equations usable for dynamic and control analysis. In this study, two powerful techniques in the field of Artificial Intelligence (AI), namely Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to explain how unknown parameters in differential equations of dynamic systems can be identified. The data required for training and testing the ANN and the ANFIS are obtained by solving the direct problem i.e. solving the dynamic equations with different known parameters and input stimulations. The governing ordinary differential equations of the system is numerically solved and the output values in different time steps are obtained. The output values of the system and their derivatives, the time and the inputs are given to the ANN and the ANFIS as their inputs and the unknown parameters in the dynamic equations are estimated as the outputs. Finally, the performances of the ANN and the ANFIS for identifying parameters of the system are compared based on the test data Percent Root Mean Square Error (% RMSE) values.

Integrated real-time flood forecasting and inundation analysis in small-medium streams

2019 ◽  
Author(s):  
Byunghyun Kim ◽  
Seung-Yong Choi ◽  
Kun-Yeun Han
Keyword(s):  
Real Time ◽  
Fuzzy Inference ◽  
Flood Forecasting ◽  
Water Levels ◽  
Subtractive Clustering ◽  
Lead Times ◽  
Hydrodynamic Models ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

This study presents the application of an adaptive neuro-fuzzy inference system (ANFIS) and one dimensional (1-D) and two dimensional (2-D) hydrodynamic models to improve the problems of hydrological models currently used for flood forecasting in small-medium streams of South Korea. The optimal combination of input variables (e.g., rainfall and water level) in ANFIS was selected based on a statistical analysis of the observed and forecasted values. Two membership functions (MFs) and two ANFIS rules were determined by the subtractive clustering (SC) approach in the processes of training and checking. The developed ANFIS was applied to Jungrang Stream and water levels for six lead times (0.5, 1.0, 1.5, 2.0, 2.5 and 3.0 hour) were forecasted. Based on point forecasted water levels by ANFIS, 1-D section flood forecast and 2-D spatial inundation analysis were carried out. This study demonstrated that the proposed methodology can forecast flooding based only on observed data without abundant physical, and can be performed in real time by integrating point- and section flood forecasting and spatial inundation analysis.

Modelling of Grewia mollis Stem Bark Gum Extraction Yield Using Neuro-Fuzzy Technique

2018 ◽  
Vol 34 ◽  
pp. 70-80 ◽  
Author(s):  
Emmanuel Olusola Oke ◽  
Oladayo Adeyi ◽  
Abiola John Adeyi ◽  
Kayode Feyisetan Adekunle
Keyword(s):  
Fuzzy Inference ◽  
Water System ◽  
Stem Bark ◽  
Extraction Process ◽  
Extraction Yield ◽  
Percentage Error ◽  
Process Time ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Input Variables

In this paper, Adaptive Neuro-Fuzzy Inference System (ANFIS) was used to model and predict Grewia Polysaccharide Gum (GPG) extraction yield from Grewia mollis (GM) powder/water system. The data for modelling the process behaviour consisted of four inputs (process temperature, GM powder/water ratio, process time and pH) and GPG yield as the output. The gbell Membership Function (MF) was used for the fuzzification of input variables and hybrid algorithm was chosen for the learning method of input–output data of the process. Simulation study was conducted on the developed ANFIS architecture at different MFs and epoch numbers to establish minimum error and maximum correlation coefficient (R) of the model. From the results obtained, ANFIS can be used as a reliable tool for modelling and prediction of GPG powder/water extraction process behaviour. The R between the experimental and predicted values was found to be high (> 0.96) and the mean percentage error was less than 2%, showing the great efficiency and reliability of the developed model.

scholarly journals Hybrid Based Artificial Intellegence Short –Term Load Forecasting

2021 ◽  
pp. 75-87
Author(s):  
Kayode O. Adebunmi ◽  
Temilola M. Adepoju ◽  
Gafari A. Adepoju ◽  
Akeem O. Bisiriyu
Keyword(s):  
Statistical Method ◽  
Fuzzy Inference ◽  
Load Forecasting ◽  
Fuzzy Model ◽  
Absolute Error ◽  
Multilinear Regression ◽  
Power Utility ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Power Load

Electrical power load forecasting, which forms a key element in the power industry's electricity preparation, is used for providing required data for day-to-day system management activities and power utility unit participation. Since the statistical method is a linear model, and the load and meteorological parameters have a nonlinear relationship, the statistical method for load forecasting involves a great calculation time for parameter recognition. Using this tool for load forecasting often results in a major mistake in prediction. Due to the disadvantages of the statistical method of load forecasting Neuro-fuzzy model was used in this work. Three models: Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN) and Multilinear Regression (MLR) were simulated in MATLAB environment and their output results were compared using root mean square error (RMSE) and mean absolute error (MAE). The ANFIS model outperforms the other models with least errors of RMSE and MAE of 2.2198% and 1.7932% respectively.

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