Mapping of solar energy potential in Indonesia using artificial neural network and geographical information system

2012 ◽  
Vol 16 (3) ◽  
pp. 1437-1449 ◽  
Author(s):  
Meita Rumbayan ◽  
Asifujiang Abudureyimu ◽  
Ken Nagasaka
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Information System ◽  
Solar Energy ◽  
Geographical Information System ◽  
Geographical Information ◽  
Energy Potential ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Network and Geographical Information System Models to Predict and Evaluate the Quality of Diyala River Water, Iraq

2019 ◽  
Vol 26 (1) ◽  
pp. 160-169
Author(s):  
Nawar Omran Al-Musawi ◽  
Fatima Muqdad Al-Rubaie
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Artificial Neural Network ◽  
Information System ◽  
Geographical Information System ◽  
Geographical Information ◽  
Total Alkalinity ◽  
Artificial Neural ◽  
Diyala River

This research discusses application Artificial Neural Network (ANN) and Geographical Information System (GIS) models on water quality of Diyala River using Water Quality Index (WQI). Fourteen water parameters were used for estimating WQI: pH, Temperature, Dissolved Oxygen, Orthophosphate, Nitrate, Calcium, Magnesium, Total Hardness, Sodium, Sulphate, Chloride, Total Dissolved Solids, Electrical Conductivity and Total Alkalinity. These parameters were provided from the Water Resources Ministryfrom seven stations along the river for the period 2011 to 2016. The results of WQI analysis revealed that Diyala River is good to poor at the north of Diyala province while it is poor to very polluted at the south of Baghdad City. The selected parameters were subjected to Kruskal-Wallis test for detecting factors contributing to the degradation of water quality and for eliminating independent variables that exhibit the highest contribution in p-value. The analysis of results revealed that ANN model was good in predicting the WQI. The confusion matrix for Artificial Neural Model (NNM) gave almost 96% for training, 85.7% for testing and 100% for holdout. In relation to GIS, six color maps of the river have been constructed to give clear images of the water quality along the river.

scholarly journals Solar Energy Potential Estimation in Perak Using Clearness Index and Artificial Neural Network

2014 ◽  
Vol 13 ◽  
pp. 02015 ◽  
Author(s):  
Morteza Khalaji Assadi ◽  
Abdul Faliq Qushairi Bin Abdul Razak ◽  
Khairul Habib
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Energy ◽  
Clearness Index ◽  
Energy Potential ◽  
Artificial Neural

scholarly journals MODELING SOLAR POTENTIAL IN SEMARANG, INDONESIA USING ARTIFICIAL NEURAL NETWORKS

2021 ◽  
pp. 1-8
Author(s):  
Djoko Adi Widodo ◽  
Purwanto Purwanto ◽  
Hermawan Hermawan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Energy ◽  
Solar Irradiance ◽  
Input Data ◽  
Geographical Information ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Data Set ◽  
Artificial Neural

Artificial neural network shows a good performance in predicting renewable energy. Many versions of Artificial Neural Network (ANN) models have been implemented to predict solar potential. This study aims to determine the monthly solar radiation in Semarang, Indonesia using ANN, and to visualize monthly solar irradiance as a map of the solar system of Semarang. This research applied the perceptron multi-layer ANN model, with 7 variables as input data of network learning, which were maximum temperature, relative humidity, wind speed, rainfall, longitude, latitude, and elevation. The input data set was obtained from a NASA normalized geo-satellite database website with a 5-year average daily score. Network training used backpropagation with one of the input layers, two of hidden layers, and one of the output layer. The performance of the model during the analysis of mean absolute percentage error was highly accurate (6.6%) when 12 and 10 neurons were respectively installed in the first and second hidden layers. The result was presented in a monthly map of solar potential within the geographical information system (GIS) environment. The result showed that ANN was able to be one of the alternatives to estimate solar irradiance data. The sun irradiance map can be used by the government of Semarang City to provide information about the solar energy profile for the implementation of the solar energy system. 

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