Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

2015 ◽  
Vol 34 (5) ◽  
pp. 1528-1535
Author(s):  
Ali Wadi Abbas Al-Fatlawi ◽  
Nasrudin Abdul Rahim ◽  
Rahman Saidur ◽  
Thomas Arthur Ward
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Peninsular Malaysia ◽  
Complex Topography ◽  
Energy Prediction ◽  
Artificial Neural

scholarly journals Solar Energy Prediction for Malaysia Using Artificial Neural Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-16 ◽  
Author(s):  
Tamer Khatib ◽  
Azah Mohamed ◽  
K. Sopian ◽  
M. Mahmoud
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Energy ◽  
Solar Irradiation ◽  
Mean Bias Error ◽  
Percentage Error ◽  
Clearness Index ◽  
Energy Prediction ◽  
Global Solar Irradiation ◽  
Artificial Neural

This paper presents a solar energy prediction method using artificial neural networks (ANNs). An ANN predicts a clearness index that is used to calculate global and diffuse solar irradiations. The ANN model is based on the feed forward multilayer perception model with four inputs and one output. The inputs are latitude, longitude, day number, and sunshine ratio; the output is the clearness index. Data from 28 weather stations were used in this research, and 23 stations were used to train the network, while 5 stations were used to test the network. In addition, the measured solar irradiations from the sites were used to derive an equation to calculate the diffused solar irradiation, a function of the global solar irradiation and the clearness index. The proposed equation has reduced the mean absolute percentage error (MAPE) in estimating the diffused solar irradiation compared with the conventional equation. Based on the results, the average MAPE, mean bias error and root mean square error for the predicted global solar irradiation are 5.92%, 1.46%, and 7.96%. The MAPE in estimating the diffused solar irradiation is 9.8%. A comparison with previous work was done, and the proposed approach was found to be more efficient and accurate than previous methods.

Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: a case study in Spathiphyllum wallisii

2021 ◽  
Vol 43 (5) ◽  
Author(s):  
Amin Taheri-Garavand ◽  
Abdolhossein Rezaei Nejad ◽  
Dimitrios Fanourakis ◽  
Soodabeh Fatahi ◽  
Masoumeh Ahmadi Majd
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Water Status ◽  
Leaf Water ◽  
Leaf Water Status ◽  
Non Invasive ◽  
Color Features ◽  
Artificial Neural

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Predicting the concentration of sulfate (SO42-) in drinking water using artificial neural networks: a case study: Médéa-Algeria

2021 ◽  
Vol 217 ◽  
pp. 181-194
Author(s):  
Hichem Tahraoui ◽  
Abd-Elmouneïm Belhadj ◽  
Adhya-eddine Hamitouche ◽  
Mounir Bouhedda ◽  
Abdeltif Amrane
Keyword(s):  
Neural Networks ◽  
Drinking Water ◽  
Artificial Neural Networks ◽  
Artificial Neural
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