scholarly journals Electricity Consumption Forecasting Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

2019 ◽  
Vol 6 (5B) ◽  
pp. 37-48 ◽  
Author(s):  
K. G. Tay ◽  
Hassan Muwafaq ◽  
Shuhaida Binti Ismail ◽  
Pauline Ong
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Electricity Consumption ◽  
Inference System ◽  
Neuro Fuzzy

scholarly journals Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

2021 ◽  
Vol 18 (1) ◽  
pp. 75-94
Author(s):  
Robert Ofosu ◽  
Benjamin Odoi ◽  
Mercy Asamoah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Inference System ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variables ◽  
Testing Dataset ◽  
Neuro Fuzzy

Electricity has become one of the inelastic goods in our world today. The proper functioning of most equipment today relies on electricity. Taking Tarkwa which is a mining community into consideration, the various mines, schools, shops, banks and other companies in the municipality massively rely on electricity for their day to day running. Therefore, knowing the exact amount of electricity to produce and distribute for the smooth running of businesses and basic living is of great necessity. This study compared and formulated a model to forecast and predict the daily electrical energy consumption in Tarkwa for the year 2019. The data used was a monthly dataset for the year 2018 and it comprised of the temperature, wind speed, population and electricity consumption for Tarkwa. The methods used were Artificial Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Integrated Moving Average (ARIMA). The ANFIS was used as a predictor to predict the electricity consumption based on the training and testing of the dependent and independent variables. The ARIMA was used to forecast the dependent and independent variables for 2019. These simulations were done using MATLand Minitab. The results of the analysis revealed that the training and testing dataset allowed ANFIS to learn and understand the system but the ANFIS could only forecast the 2019 electricity consumption after the input data to the system was changed to the ARIMA forecasted 2019 independent variables. It was observed that the amount of electricity consumed in 2019 increased by 14%.

Aplikasi Peramalan Kebutuhan Beban Listrik Menggunakan Metode Adaptive Neuro Fuzzy Inference System (ANFIS)

2017 ◽  
Vol 3 (1) ◽  
pp. 36-48
Author(s):  
Erwan Ahmad Ardiansyah ◽  
Rina Mardiati ◽  
Afaf Fadhil
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy

Prakiraan atau peramalan beban listrik dibutuhkan dalam menentukan jumlah listrik yang dihasilkan. Ini menentukan  agar tidak terjadi beban berlebih yang menyebabkan pemborosan atau kekurangan beban listrik yang mengakibatkan krisis listrik di konsumen. Oleh karena itu di butuhkan prakiraan atau peramalan yang tepat untuk menghasilkan energi listrik. Teknologi softcomputing dapat digunakan  sebagai metode alternatif untuk prediksi beban litrik jangka pendek salah satunya dengan metode  Adaptive Neuro Fuzzy Inference System pada penelitian tugas akhir ini. Data yang di dapat untuk mendukung penelitian ini adalah data dari APD PLN JAWA BARAT yang berisikan laporan data beban puncak bulanan penyulang area gardu induk majalaya dari januari 2011 sampai desember 2014 sebagai data acuan dan data aktual januari-desember 2015. Data kemudian dilatih menggunakan metode ANFIS pada software MATLAB versi b2010. Dari data hasil pelatihan data ANFIS kemudian dilakukan perbandingan dengan data aktual dan data metode regresi meliputi perbandingan anfis-aktual, regresi-aktual dan perbandingan anfis-regresi-aktual. Dari perbandingan disimpulkan bahwa data metode anfis lebih mendekati data aktual dengan rata-rata 1,4%, menunjukan prediksi ANFIS dapat menjadi referensi untuk peramalan beban listrik dimasa depan.

PENGEMBANGAN SISTEM MANAJEMEN PENDUKUNG KEPUTUSAN PENILAIAN MUTU KEPEGAWAIAN DINAS PENDIDIKAN REMBANG MENGGUNAKAN ALGORITMA ANFIS (ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM)

2020 ◽  
Vol 12 (1) ◽  
pp. 6-17
Author(s):  
Angga debby frayudha ◽  
Aris Yulianto ◽  
Fatmawatul Qomariyah
Keyword(s):  
Fuzzy Inference System ◽  
Industry 4.0 ◽  
Fuzzy Inference ◽  
System Model ◽  
Inference System ◽  
Neuro Fuzzy

Di era revolusi industry 4.0 terdapat banyak sekali kemudahan yang diberikan teknologi kepada manusia. Tentu ini akan menjadi baik apabila manusia mampu memanfaatkan hal tersebut dengan baik pula. Namun disisi lain juga bisa mengakibatkan dampak negative terhadap manusia, misalnya dengan adanya internet bisa mengakibatkan manusia melakukan penipuan di media social. Selain itu dengan canggihnya teknologi dapat menjadikan manusia menjadi malas yang bisa berimbas menurunnya kualitas sumber daya manusia. Maka dari itu untuk menghadapi hal ini perlu menyiapkan pendidikan yang baik.Pendidikan akan berjalan baik apabila lembaga yang mengurusnya berkompeten dalam melakukan tugasnya .Penulis coba memberikan ide untuk memprediksi kinerja pegawai Dinas Pendidikan Kabupaten Rembang menggunakan mentode ANFIS (Adaptive Neuro Fuzzy Inference System) guna untuk membantu lembaga tersebut menyeleksi maupun menilai kinerja karyawan demi meningkatkan kualitas dari segi sumber daya manusia. ANFIS merupakan jaringan adaptif yang berbasis pada sistem kesimpulan fuzzy (fuzzy inference system). Model penilaian kinerja pegawai di Dinas Pendidikan Kabupaten Rembang dengan menggunakan Adaptive Neuro-Fuzzy Inference System (ANFIS) menghasilkan penilaian  yang lebih baik dan akurat.  Hasil pengujian metode tersebut memiliki nilai akurasi 65%. Dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) dapat memprediksi kinerja karyawan sebagai salah satu pengambilan keputusan terhadap kinerja pegawai. Selain itu nantinya system penlaian kinerja pegawai akan lebih tertata dan efisien.

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.

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