Load Frequency Control in Two Area Power System using ANFIS

The load-frequency control (LFC) is used to restore the balance between load and generation in each control area by means of speed control. The main goal of LFC is to minimize the transient deviations and steady state error to zero in advance. This paper investigated LFC using proportional integral (PI) Controller and Adaptive Neuro Fuzzy Inference System (ANFIS) for two area system. The results of the two controllers are compared using MATLAB/Simulink software package. Comparison results of conventional PI controller and Adaptive Neuro Fuzzy inference System are presented. Keywords: Load Frequency Control, Adaptive Neuro Fuzzy Inference System (ANFIS), PI controller

Forecasting seasonality in prices of potatoes and onions: challenge between geostatistical models, neuro fuzzy approach and Winter method

Price, Geostatistical model, Kiriging, Inverse distance weighting, Winter’s method, Adaptive neuro fuzzy inference system, Potatoes, Onions, Iran, Crop Production/Industries, Demand and Price Analysis,

Prediction in Photovoltaic Power by Neural Networks

The ability to forecast the power produced by renewable energy plants in the short and middle term is a key issue to allow a high-level penetration of the distributed generation into the grid infrastructure. Forecasting energy production is mandatory for dispatching and distribution issues, at the transmission system operator level, as well as the electrical distributor and power system operator levels. In this paper, we present three techniques based on neural and fuzzy neural networks, namely the radial basis function, the adaptive neuro-fuzzy inference system and the higher-order neuro-fuzzy inference system, which are well suited to predict data sequences stemming from real-world applications. The preliminary results concerning the prediction of the power generated by a large-scale photovoltaic plant in Italy confirm the reliability and accuracy of the proposed approaches

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR OKRA YIELD PREDICTION

This paper, adaptive neuro-fuzzy inference system for okra yield prediction, describes the use of neuro-fuzzy inference system in the prediction of okra yield using environmental parameters such as minimum temperature, relative humidity, evaporation, sunshine hours, rainfall and maximum temperature as input into the neuro-fuzzy inference system, and yield as output. The agro meteorological data used were obtained from the department of agro meteorological and water management, Federal University of Agriculture, Abeokuta and the yield data were obtained from the Department of Horticulture, Federal University of Agriculture, Abeokuta. MATLAB was used for the analysis of the data. From the results, the maximum predicted yield showed that at minimum temperature of 24.4 oc, relative humidity of 78.3% and evaporation of 5.5mm, the yield predicted is 1.67 tonnes/hectare.

Diagnosis of rotor fault using neuro-fuzzy inference system

The three-phase induction machines (IM) is large importance and are being widely used as electromechanical system device regarding for their robustness, reliability, and simple design with well developed technologies. This work presents a reliable method for diagnosis and detection of rotor broken bars faults in induction machine. The detection faults are based on monitoring of the current signal. Also the calculation of the value of relative energy for each level of signal decomposition using package wavelet, which will be useful as data input of adaptive Neuro-Fuzzy inference system (ANFIS). In this method, fuzzy logic is used to make decisions about the machine state. The adaptive Neuro-Fuzzy inference system is able to identify the IM bearing state with high precision. This technique is applied under the MATLAB®.Keywords: Induction Machine; Diagnosis; Detection; Neuro-Fuzzy inference system

Adaptive neuro fuzzy inference system untuk peramalan jumlah wisatawan

Adaptive Neuro Fuzzy Inference System(ANFIS) yaitu metode yang menggabungkan metode-metode yang ada pada softcomputing. Softcomputing yaitu pemodelan dengan pendekatan seperti nalar dari manusia dan belajar sesuai kondisi yang tidak pasti yang fleksibel atau berubah-ubah. Fuzzy Inference System(FIS) dan Jaringan syaraf merupakan komponen softcomputing dan pembentukan ANFIS. Penggunaan ANFIS terdapat metode pembelajran secara maju dan mundur atau yang disebut hybrid. Pembelajaran secara maju digunakan metode Least Square Estimator(LSE) dan pembelajaran mundur digunakan Gradient descent. Pada penelitian ini juga menggunakan FCM difungsikan untuk membangun aturan akan digunakan pada ANFIS. Tujuan dari peramalan jumlah wisatawan adalah untuk mengimplementasikan metode Adaptive Neuro Fuzzy Inference System dalam memprediksi jumlah wisatawan dan mengetahui hasil prediksi/ramalan jumlah wisatawan. Hasil peramalan jumlah wisatawan dengan menggunakan metode Adaptive Neuro Fuzzy Inference System(ANFIS) pada tahap latih didapatkan epoh 11 dan laju pembelajaran 0.02 mendapatkan nilai RMSE 909.2 sedangkan tahap uji epoh dan laju pembelajaran memberikan keakurasian bagus yang dapat dilihat dari Root Mean Square Error(RMSE) 123.3029

PREDIKSI CURAH HUJAN MENGGUNAKAN ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS)<br><br>Prediction of Rainfall Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

ABSTRAKSI: Curah hujan adalah endapan atau deposit air dalam bentuk cair maupun padat yang berasal atmosfer. Informasi tentang banyaknya curah hujan adalah salah satu unsur penting dan besar pengaruhnya terhadap segala macam aktifitas kehidupan seperti: keselamatan masyarakat, produksi pertanian, perkebunan, perikanan, penerbangan, public service, dan lain sebagainya.Pada urutan waktu tersebut, seberapa besar jumlah curah hujan yang turun. Dan besarnya curah hujan yang turun tersebut setiap waktu tertentu adalah berbeda (non-linear). Sehingga dengan pola data yang non-linear, akan di prediksi berapa besarnya curah hujan pada waktu yang akan datang atau disebut juga time-series prediction.Adaptive Neuro Fuzzy Inference System (ANFIS) merupakan kombinasi dari Sistem Inferensi Fuzzy dengan Jaringan Syaraf Tiruan dimana nilai keanggotaan dari Sistem Inferensi Fuzzy akan diperbaiki melalui pembelajaran dengan Jaringan Syaraf Tiruan sehingga dapat memberikan tingkat akurasi yang lebih baik untuk suatu sistem prediksi.Tugas akhir ini mengimplementasikan arsitektur ANFIS untuk prediksi curah hujan untuk wilayah depok dengan menggunakan data curah hujan dasarian. Terlebih dahulu data curah hujan dibagi menjadi data latih dan data uji. Kemudian dilakukan pelatihan untuk mencari parameter-parameter yang akan digunakan pada saat pengujian. Setelah itu dilakukan pengujian dengan menggunakan parameter yang didapat dari pelatihan.Kata Kunci : : Sistem Inferensi Fuzzy, Adaptive Neuro Fuzzy Inference System (ANFIS), Jaringan Syaraf Tiruan, time-series prediction, Curah hujan.ABSTRACT: Rainfall is a deposit of water in liquid or solid form that originated the atmosphere. Information about the amount of rainfall is one important element and the greatest effect on all sorts of life activities such as: public safety, agricultural production, plantations, fisheries, aviation, public service, and others.At the time the order is, how big the amount of rainfall that fell. And the amount of rainfall that fell was any particular time is different (non-linear). So, with the pattern of non-linear data, will predict how much rainfall in the future or also known as time-series prediction.Adaptive Neuro Fuzzy Inference System (ANFIS) is a combination of Fuzzy Inference System with Artificial Neural Networks in which the membership value of the Fuzzy Inference System will be improved through learning with neural networks that can provide better accuracy for a prediction system.This final project implements the architecture of ANFIS to predict rainfall for the region depok using rainfall data. First, rainfall data divided into training data and test data. Then do the training to find the parameters that will be used during testing. After it was examined by using the parameters obtained from training.Keyword: Fuzzy Inference System, Adaptive Neuro Fuzzy Inference System (ANFIS), neural networks, time-series prediction, rainfall

Development of ANFIS Control System for Seismic Response Reduction using Multi-Objective Genetic Algorithm

Adaptive neuro fuzzy inference system (ANFIS) and Genetic algorithm (GA) was proposed in this study to reduce dynamic responses of a seismically excited building. A multi-objective genetic algorithm (MOGA) was used to optimize the ANFIS+GA controller. Two MR dampers were used as multiple control devices and a scaled five-story building model was selected as an example structure. A fuzzy control algorithm was compared with the proposed ANFIS and ANFIS+GA controller. Adaptive neuro-fuzzy inference system (ANFIS) and Ganetic algorithm with several outputs was proposed. In case study, after numerical simulation, it has been verified that the ANFIS control algorithm can present better control performance compared to the fuzzy control algorithm in reducing both displacement and acceleration responses

Electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Universiti Tun Hussein Onn Malaysia (UTHM) is a developing Malaysian Technical University. There is a great development of UTHM since its formation in 1993. Therefore, it is crucial to have accurate future electricity consumption forecasting for its future energy management and saving. Even though there are previous works of electricity consumption forecasting using Adaptive Neuro-Fuzzy Inference System (ANFIS), but most of their data are multivariate data. In this study, we have only univariate data of UTHM electricity consumption from January 2009 to December 2018 and wish to forecast 2019 consumption. The univariate data was converted to multivariate and ANFIS was chosen as it carries both advantages of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS). ANFIS yields the MAPE between actual and predicted electricity consumption of 0.4002% which is relatively low if compared to previous works of UTHM electricity forecasting using time series model (11.14%), and first-order fuzzy time series (5.74%), and multiple linear regression (10.62%)