An evaluation of the artificial neural network based on the estimation of daily average global solar radiation in the city of Surabaya

The estimation of the daily average global solar radiation is important since it increases the cost efficiency of solar power plant, especially in developing countries. Therefore, this study aims at developing a multi layer perceptron artificial neural network (ANN) to estimate the solar radiation in the city of Surabaya. To guide the study, seven (7) available meteorological parameters and the number of the month was applied as the input of network. The ANN was trained using five-years data of 2011-2015. Furthermore, the model was validated by calculating the mean average percentage error (MAPE) of the estimation for the years of 2016-2019. The results confirm that the aforementioned model is feasible to generate the estimation of daily average global solar radiation in Surabaya, indicated by MAPE of less than 15% for all testing years.

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Global Solar Radiation Prediction Using Backward Propagation Artificial Neural Network for the City of Addis Ababa, Ethiopia

Computational Collective Intelligence - Lecture Notes in Computer Science ◽

10.1007/978-3-319-45243-2_21 ◽

2016 ◽

pp. 230-238

Author(s):

Younas Worki ◽

Eshetie Berhan ◽

Ondrej Krejcar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Addis Ababa ◽

Global Solar Radiation ◽

Artificial Neural ◽

Backward Propagation ◽

The City

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Use of Empirical Regression and Artificial Neural Network Models for Estimation of Global Solar Radiation in Dubai, UAE

Causes, Impacts and Solutions to Global Warming ◽

10.1007/978-1-4614-7588-0_4 ◽

2013 ◽

pp. 61-86

Author(s):

Hassan A. N. Hejase ◽

Ali H. Assi ◽

Maitha H. Al Shamisi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Network Models ◽

Global Solar Radiation ◽

Neural Network Models ◽

Artificial Neural ◽

Artificial Neural Network Models

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Global Solar Radiation Estimation Modeling Using Artificial Neural Network: A Case Study on Metro Cities of India

Lecture Notes in Electrical Engineering - Intelligent Computing in Control and Communication ◽

10.1007/978-981-15-8439-8_39 ◽

2021 ◽

pp. 479-489

Author(s):

Amar Choudhary ◽

Deependra Pandey ◽

Saurabh Bhardwaj

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Solar Radiation ◽

Global Solar Radiation ◽

Artificial Neural

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Prediction of Density and Speed of Sound of Binary Ionic Liquid and Ketone Mixtures Using Artificial Neural Network

E3S Web of Conferences ◽

10.1051/e3sconf/201912001003 ◽

2019 ◽

Vol 120 ◽

pp. 01003

Author(s):

Redden Rose Rivera ◽

Allan Soriano

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ionic Liquid ◽

Ionic Liquids ◽

Speed Of Sound ◽

Negative Impact ◽

Green Energy ◽

Percentage Error ◽

Average Percentage ◽

Artificial Neural

The applications of ionic liquids solve a lot of major problems regarding green energy production and environment. Ionic liquids are solvents used as alternative to unfriendly traditional and hazardous solvents which reduces the negative impact to environment to a great extent. This study produced models to predict two of the basic physical properties of binary ionic liquid and ketone mixtures: density and speed of sound. The artificial neural network algorithm was used to predict these properties by varying the temperature, mole fraction, atom count in cation, methyl group count in cation, atom count in anion, hydrogen atom count in anion of ionic liquid and atom count in ketone. Total experimental data points of 2517 for density and 947 for speed of sound were used to train the algorithm and to test the network obtained. The optimum neural network structure determined for density and speed of sound of binary ionic liquid and ketone mixtures were 7-9-9-1 and 7-7-4-1 respectively; overall average percentage error of 2.45% and 2.17% respectively; and mean absolute error of 28.21 kg/m3 and 33.91 m/s respectively. The said algorithm was found applicable for the prediction of density and speed of sound of binary ionic liquid and ketone mixtures.

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