Prediction and estimation of solar radiation using artificial neural network (ANN) and fuzzy system: a comprehensive review

2021 ◽  
Author(s):  
D. Shah ◽  
K. Patel ◽  
M. Shah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Fuzzy System ◽  
Comprehensive Review ◽  
System A ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Application of Multi Layer Artificial Neural Network in the Diagnosis System: A Systematic Review

2018 ◽  
Vol 7 (3) ◽  
pp. 138
Author(s):  
Arvind Singh Rawat ◽  
Arti Rana ◽  
Adesh Kumar ◽  
Ashish Bagwari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Reconfigurable Computing ◽  
Research Papers ◽  
Computing Systems ◽  
System A ◽  
Major Scale ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Execution Technique

Basic hardware comprehension of an artificial neural network (ANN), to a major scale depends on the proficientrealization of a distinctneuron. For hardware execution of NNs, mostly FPGA-designed reconfigurable computing systems are favorable .FPGA comprehension of ANNs through a hugeamount of neurons is mainlyan exigentassignment. This workconverses the reviews on various research articles of neural networks whose concernsfocused in execution of more than one input neuron and multilayer with or without linearity property by using FPGA. An execution technique through reserve substitution isprojected to adjust signed decimal facts. A detailed review of many research papers have been done for the <br /> proposed work.

scholarly journals Estimation of Solar Radiation using Artificial Neural Network

2004 ◽  
Vol 18 (1) ◽  
Author(s):  
Slamet Suprayogi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Water Surface ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The solar radiation is the most important fator affeccting evapotranspiration, the mechanism of transporting the vapor from the water surface has also a great effect. The main objectives of this study were to investigate the potential of using Artificial Neural Network (ANN) to predict solar radiation related to temperature. The three-layer backpropagation were developed, trained, and tested to forecast solar radiation for Ciriung sub Cachment. Result revealed that the ANN were able to well learn the events they were trained to recognize. Moreover, they were capable of effecctively generalize their training by predicting solar radiation for sets unseen cases.

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

2020 ◽  
Vol 11 (1) ◽  
pp. 342 ◽  
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>

scholarly journals Global Solar Radiation Forecasting using Artificial Neural Network and Support Vector Machine

2021 ◽  
Vol 2129 (1) ◽  
pp. 012079
Author(s):  
Emmanuel Philibus ◽  
Roselina Sallehuddin ◽  
Yusliza Yussof ◽  
Lizawati Mi Yusuf
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Machine ◽  
Solar Radiation ◽  
Land Surface ◽  
Statistical Tests ◽  
Global Solar Radiation ◽  
Support Vector ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Abstract Global solar radiation (GSoR) forecasting involves predicting future energy from the sun based on past and present data. Literature reveals that not all meteorological stations record solar radiation, some equipments are faulty, and are not available in every location due to high cost. Hence, the need to predict and forecast using predictors such as land surface temperature (LST). Satellite data when were used to complement ground-based stations have been yielding good results. Different artificial intelligence (AI) methods such as Support Vector Machine (SVM) and Artificial Neural Network (ANN) present different forecasting performances. Motivated by existing literature-related contradictions on the performance superiority of ANN and SVM in GSoR forecasting, the two techniques were compared based on several statistical tests. Experimental results show that ANN outperformed SVM by 2.9864% accuracy, making it superior in the forecast of GSoR.

scholarly journals Two-step artificial neural network to estimate the solar radiation at Java Island

2021 ◽  
Vol 11 (4) ◽  
pp. 3559
Author(s):  
Adi Kurniawan ◽  
Eiji Shintaku
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Meteorological Parameters ◽  
Input Parameter ◽  
Radiation Characteristics ◽  
Daily Average ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Time Required

<span>The availability of information about solar radiation characteristics, particularly solar radiation predictions, is important for efficiently designing solar energy systems. Solar radiation information is not available in Indonesia because official measurements have not been conducted by the Indonesian Meteorological, Climatology, and Geophysical Agency (BMKG). In this study, a new two-step artificial neural network (ANN) is proposed to estimate both the daily average and hourly solar radiation at Java Island, Indonesia. The input parameters for the daily average solar radiation estimation are the location and time required, along with five selected monthly meteorological parameters that BMKG predicts for the subsequent month. The selected meteorological parameters are temperatures, relative humidity, and precipitation. The estimated daily average solar radiation is then used as the input parameter of the hourly solar radiation estimation along with the local time and location. The ANN training was conducted using two years of data, 2018 and 2019, from Surabaya and Jakarta, while the validation was performed in the same cities for January through July 2020. The accuracy of the proposed method is comparable to previous studies with an average R2 of 98.70% for the daily average solar radiation estimate and 97.44% for the hourly solar radiation estimate.</span>

scholarly journals IMPLEMENTASI ALGORITMA WEIGHTED MOVING AVERAGE PADA (FUZZY EAs) UNTUK PERAMALAN KALENDER MASA TANAM BERBASIS CURAH HUJAN

2016 ◽  
Vol 1 (1) ◽  
Author(s):  
Fhira Nhita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Soft Computing ◽  
Fuzzy System ◽  
Moving Average ◽  
Grammatical Evolution ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Weighted Moving Average

<p>Peramalan merupakan proses memperkirakan sesuatu secara sistematis berdasarkan keadaan atau fakta sebelumnya. Peramalan bisa dilakukan melalui serangkaian metode ilmiah atau dengan subjektif belaka. Soft computing (SC) merupakan salah satu metode ilmiah yang dapat digunakan untuk kasus peramalan atau prediksi, Soft Computing (SC) memiliki Algoritma dasar yakni Fuzzy System, Artificial Neural Network  (ANN), dan Evolutionary Alghorithms (EAs). Pada Tugas akhir ini dilakukan penelitian mengenai peramalan kalender masa tanam tanaman jagung yang berbasis curah hujan di wilayah Soreang Kabupaten Bandung menggunakan salah satu jenis algoritma dasar Soft computing (SC) yakni Evolutionary Alghorithms (EAs). Data yang digunakan adalah data curah hujan wilayah Soreang Kabupaten Bandung selama 10 tahun terakhir (2006-2015), data ini akan melalui preprocessing terlebih dahulu dengan Weighted Moving Average (WMA). Pada representasi individu, EAs memiliki empat algoritma yang bisa digunakan, salah satunya <em>Grammatical Evolution </em>(GE) yang akan digunakan pada penelitian ini. Selanjutnya, dalam tugas akhir ini digunakan logika <em>Fuzzy </em>untuk pengoptimasian GE, dengan cara mendefinisikan beberapa parameter pada awal running , agar proses dapat berjalan dengan baik. Hasil akhir yang didapat menunjukkan bahwa logika <em>Fuzzy </em>membantu meningkatkan performansi Eas dan Fuzzy EAs menghasilkan performansi peramalan kalender masa tanam sebesar 76,93%. Hasil peramalan akan digunakan untuk pembuatan kalender masa tanam di Kabupaten Bandung selama 13 (tiga belas) bulan yang dimulai pada Oktober 2014 sampai Oktober 2015.</p><p><em> </em></p>

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
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