Prakiraan Beban Puncak Pada Transformator GITET 150 kV Kesugihan Cilacap Menggunakan Jaringan Syaraf Tiruan Multilayer Feedforward Dengan Algoritma Backpropagation

The increasing population and the growth of the industrial world, offices, hotels, and modern markets must be directly proportional to Indonesia's availability of electrical energy. The availability of sufficient electrical energy can affect the quality of life of the people and foster investor confidence in our country. Studies on the prediction (estimation) of peak electrical loads in electricity in Indonesia can be carried out using the Artificial Neural Network (ANN) method. The estimation of electricity load for the next 5 years is strongly influenced by several parameters, including population growth and peak load data of 150 kV GITET, Kesugihan Cilacap. This study took population data and peak load data at GITET 150 KV Kesugihan Cilacap in the past 5 years. The data used in this study were actual data, starting from 2015 to 2019. To display the results of the estimated electrical load on the 150 kV GITET transformer, the authors used the artificial neural network method. The peak electrical loads estimation results using artificial neural networks for electricity loads in the next 5 years, to wit from 2020 - 2024. The estimated peak load in Lomanis District is20.0311 MW, 24.443 MW, 19.9707 MW, 19.9705 MW and 19, 9705 MW. In Gombong District, the estimated peak load is 57,398 MW, 57,472 MW, 57,476 MW, 57,474 MW, and 57,479 MW.

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Development of Hybrid Artificial Neural Network for Quantifying Energy Saving using Measurement and Verification

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v8.i1.pp137-145 ◽

2017 ◽

Vol 8 (1) ◽

pp. 137

Author(s):

Wan n Nazirah Wan Md Adna ◽

Nofri Yenita Dahlan ◽

Ismail Musirin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Energy Use ◽

Model Development ◽

Operating Time ◽

Electrical Energy ◽

Measurement And Verification ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hybrid Artificial Neural Network

This paper presents a Hybrid Artificial Neural Network (HANN) for chiller system Measurement and Verification (M&V) model development. In this work, hybridization of Evolutionary Programming (EP) and Artificial Neural Network (ANN) are considered in modeling the baseline electrical energy consumption for a chiller system hence quantifying saving. EP with coefficient of correlation (R) objective function is used in optimizing the neural network training process and selecting the optimal values of ANN initial weights and biases. Three inputs that are affecting energy use of the chiller system are selected; 1) operating time, 2) refrigerant tonnage and 3) differential temperature. The output is hourly energy use of building air-conditioning system. The HANN model is simulated with 16 different structures and the results reveal that all HANN structures produce higher prediction performance with R is above 0.977. The best structure with the highest value of R is selected as the baseline model hence is used to determine the saving. The avoided energy calculated from this model is 132944.59 kWh that contributes to 1.38% of saving percentage.

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PREDIKSI BEBAN ENERGI LISTRIK APJ KOTA SEMARANG MENGGUNAKAN METODE RADIAL BASIS FUNCTION (RBF)

eLEKTRIKA ◽

10.26623/elektrika.v11i2.1699 ◽

2019 ◽

Vol 11 (2) ◽

pp. 21

Author(s):

Mukti Dwi Cahyo ◽

Sri Heranurweni ◽

Harmini Harmini

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Radial Basis Function ◽

Basis Function ◽

Peak Load ◽

Electricity Demand ◽

Training Data ◽

Radial Basis ◽

Artificial Neural ◽

Artificial Neural Network Ann

Electric power is one of the main needs of society today, ranging from household consumers to industry. The demand for electricity increases every year. So as to achieve adjustments between power generation and power demand, the electricity provider (PLN) must know the load needs or electricity demand for some time to come. There are many studies on the prediction of electricity loads in electricity, but they are not specific to each consumer sector. One of the predictions of this electrical load can be done using the Radial Basis Function Artificial Neural Network (ANN) method. This method uses training data learning from 2010 - 2017 as a reference data. Calculations with this method are based on empirical experience of electricity provider planning which is relatively difficult to do, especially in terms of corrections that need to be made to changes in load. This study specifically predicts the electricity load in the Semarang Rayon network service area in 2019-2024. The results of this Artificial Neural Network produce projected electricity demand needs in 2019-2024 with an average annual increase of 1.01% and peak load in 2019-2024. The highest peak load in 2024 and the dominating average is the household sector with an increase of 1% per year. The accuracy results of the Radial Basis Function model reached 95%.

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CLASSIFICATION OF MEAT IMAGERY USING ARTIFICIAL NEURAL NETWORK METHOD AND TEXTURE FEATURE EXTRACTION BY GRAY LEVEL CO-OCCURRENCE MATRIX METHOD

Jurnal Teknik Informatika (Jutif) ◽

10.20884/1.jutif.2021.2.1.37 ◽

2021 ◽

Vol 2 (1) ◽

pp. 1-8

Author(s):

Chairul Imam ◽

Eka Wahyu Hidayat ◽

Neng Ika Kurniati

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Texture Feature ◽

General Term ◽

Gray Level ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Occurrence Matrix

Lately, there is often a mixture of beef and pork done by traders to the general public as buyers. This is due to the unconsciousness of the buyer on how to recognize the type of meat purchased. The effect of this meat mix can certainly be detrimental to buyers, especially Muslims. Image processing is a general term for various methods in which it is used to manipulate and modify images in various ways. Classification is a method of grouping some information and ensuring it is listed in a class.. Classification of beef and pork differentiator in this application using Artificial Neural Network (ANN) method while for texture extraction using Gray Level Co-occurrence Matrix (GLCM) method. The information used in the examination was 30 images of fresh meat divided into 15 images of fresh beef and 15 images of fresh pork. The data used is data Classification of Beef and Pork Image based on Color and Texture Characteristics. The result of classification accuracy obtained in this application is 80%.

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Application of artificial neural network in sizing a stand-alone photovoltaic system: a review

International Journal of Power Electronics and Drive Systems (IJPEDS) ◽

10.11591/ijpeds.v11.i1.pp342-349 ◽

2020 ◽

Vol 11 (1) ◽

pp. 342 ◽

Cited By ~ 1

Author(s):

Ahmad Fateh Mohamad Nor ◽

Suriana Salimin ◽

Mohd Noor Abdullah ◽

Muhammad Nafis Ismail

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Design Tool ◽

Photovoltaic System ◽

Pv System ◽

System A ◽

Load Demand ◽

Artificial Neural ◽

Artificial Neural Network Ann

<span>Artiﬁcial Neural Network (ANN) techniques are becoming useful in the current era due to the vast development of the current computer technologies. ANN has been used in various fields especially in the field of science and technology. One of the advantage that makes ANN so interesting is the ANN’s ability to learn the input and output relationship even though the relationship is non-linear. In addition, ANN is also useful for modelling, optimization, prediction, forecasting, and controlling systems. The main objective of this paper is to present a review of the ANN techniques for sizing a stand-alone photovoltaic (PV) system. The review in this paper shows the potential of ANN as a design tool for a stand-alone PV. In addition, ANN is very useful to improve the sizing process of the stand-alone PV system. The sizing process is of paramount importance to a stand-alone PV system in order to make sure the system can generate ample electrical energy to supply the load demand.</span>

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PENERAPAN ARTIFICIAL NEURAL NETWORK UNTUK KLASIFIKASI FERTILITAS TELUR ITIK MENGGUNAKAN RASPBERRY PI

BUANA ILMU ◽

10.36805/bi.v3i1.460 ◽

2018 ◽

Vol 3 (1) ◽

Author(s):

Jamaludin Indra

Keyword(s):

Neural Network ◽

Image Processing ◽

Artificial Neural Network ◽

Raspberry Pi ◽

Detection Accuracy ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

Artificial Neural Network Ann

ABSTRAK Artificial Neural Network (ANN) telah banyak diterapkan pada berbagai bidang, salah satunya penerapan pada bidang peternakan. Penetasan menggunakan mesin penetas telur, proses pengklasifikasian embrio telur menjadi sangat penting dalam proses penetasan untuk membedakan antara yang layak, berdasarkan adanya perkembangan embrio yang dapat dilanjutkan dalam proses inkubasi atau tidak layak (fertile atau infertile), dalam penelitian ini menyajikan klasifikasi menggunakan teknik pengolahan citra digital menggunakan metode artificial neural network yang diaplikasikan pada Raspberry Pi sebagai pemroses gambar dan menampilkan hasil klasifikasi. Dengan metode artificial neural network dan penggunaan Raspberry Pi mampu mencapai akurasi pendeteksian 95%. Kata kunci: Artificial Neural Network, Pengolahan Citra Digital, Embrio , Klasifikasi, Telur . ABSTRACT Artificial Neural Network (ANN) has been widely applied in various fields, one of which is the application in the field of animal husbandry. Hatching using an egg incubator machine, the classification process of egg embryos is very important in the hatching process to distinguish between the appropriate, based on the embryonic development that can be continued in the process of incubation or inadequate (fertile or infertile), in this study presents classification using image processing techniques digital uses the artificial neural network method that is applied to the Raspberry Pi as an image processor and displays the classification results. With the artificial neural network method and the use of Raspberry Pi it is expected to be able to achieve 90% detection accuracy. Key word : Artificial Neural Network, Digital Image Processing, Embriyo, Calssification, Egg.

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Artificial Neural Network for Non-Intrusive Electrical Energy Monitoring System

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v6.i1.pp124-131 ◽

2017 ◽

Vol 6 (1) ◽

pp. 124

Author(s):

Khairell Khazin Kaman ◽

Mahdi Faramarzi ◽

Sallehuddin Ibrahim ◽

Mohd Amri Md Yunus

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Energy Monitoring ◽

Training Time ◽

Energy Meter ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Neurons ◽

Electrical Consumption

<p> This paper discusses non-intrusive electrical energy monitoring (NIEM) system in an effort to minimize electrical energy wastages. To realize the system, an energy meter is used to measure the electrical consumption by electrical appliances. The obtained data were analyzed using a method called multilayer perceptron (MLP) technique of artificial neural network (ANN). The event detection was implemented to identify the type of loads and the power consumption of the load which were identified as fan and lamp. The switching ON and OFF output events of the loads were inputted to MLP in order to test the capability of MLP in classifying the type of loads. The data were divided to 70% for training, 15% for testing, and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. In conclusion, MLP with five hidden neurons results obtained the lowest average training time with 2.699 seconds, a small number of epochs with 62 iterations, a min square error of 7.3872×10-5, and a high regression coefficient of 0.99050.</p>

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Combinatory Finite Element and Artificial Neural Network Model for Predicting Performance of Thermoelectric Generator

Energies ◽

10.3390/en11092216 ◽

2018 ◽

Vol 11 (9) ◽

pp. 2216 ◽

Cited By ~ 4

Author(s):

Ravi Kishore ◽

Roop Mahajan ◽

Shashank Priya

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Electrical Energy ◽

Optimization Techniques ◽

Operating Conditions ◽

Ann Model ◽

Predicting Performance ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann

Thermoelectric generators (TEGs) are rapidly becoming the mainstream technology for converting thermal energy into electrical energy. The rise in the continuous deployment of TEGs is related to advancements in materials, figure of merit, and methods for module manufacturing. However, rapid optimization techniques for TEGs have not kept pace with these advancements, which presents a challenge regarding tailoring the device architecture for varying operating conditions. Here, we address this challenge by providing artificial neural network (ANN) models that can predict TEG performance on demand. Out of the several ANN models considered for TEGs, the most efficient one consists of two hidden layers with six neurons in each layer. The model predicted TEG power with an accuracy of ±0.1 W, and TEG efficiency with an accuracy of ±0.2%. The trained ANN model required only 26.4 ms per data point for predicting TEG performance against the 6.0 minutes needed for the traditional numerical simulations.

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