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. 

scholarly journals A New Model for Predicting Rate of Penetration Using an Artificial Neural Network

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 2058 ◽  
Author(s):  
Salaheldin Elkatatny ◽  
Ahmed Al-AbdulJabbar ◽  
Khaled Abdelgawad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mutual Effect ◽  
Percentage Error ◽  
Drilling Process ◽  
Ann Model ◽  
Data Set ◽  
Individual Parameter ◽  
Rate Of Penetration ◽  
Artificial Neural

The drilling rate of penetration (ROP) is defined as the speed of drilling through rock under the bit. ROP is affected by different interconnected factors, which makes it very difficult to infer the mutual effect of each individual parameter. A robust ROP is required to understand the complexity of the drilling process. Therefore, an artificial neural network (ANN) is used to predict ROP and capture the effect of the changes in the drilling parameters. Field data (4525 points) from three vertical onshore wells drilled in the same formation using the same conventional bottom hole assembly were used to train, test, and validate the ANN model. Data from Well A (1528 points) were utilized to train and test the model with a 70/30 data ratio. Data from Well B and Well C were used to test the model. An empirical equation was derived based on the weights and biases of the optimized ANN model and compared with four ROP models using the data set of Well C. The developed ANN model accurately predicted the ROP with a correlation coefficient (R) of 0.94 and an average absolute percentage error (AAPE) of 8.6%. The developed ANN model outperformed four existing models with the lowest AAPE and highest R value.

scholarly journals Performance Evaluation of Artificial Neural Networks in Estimating Global Solar Radiation, Case Study: New Borg El-arab City, Egypt

2017 ◽  
Vol 2 (1) ◽  
pp. 16
Author(s):  
Gasser E. Hassan ◽  
Mohamed A. Ali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Solar Radiation ◽  
Solar Energy ◽  
Global Solar Radiation ◽  
Data Set ◽  
Primary Input ◽  
Artificial Neural ◽  
Availability Assessment

The most sustainable source of energy with unlimited reserves is the solar energy, which is the main source of all types of energy on earth. Accurate knowledge of solar radiation is considered to be the first step in solar energy availability assessment. It is also the primary input for various solar energy applications. The unavailability of the solar radiation measurements for several sites around the world leads to proposing different models for predicting the global solar radiation. Artificial neural network technique is considered to be an effective tool for modelling nonlinear systems and requires fewer input parameters. This work aims to investigate the performance of artificial neural network-based models in estimating global solar radiation. To achieve this goal, measured data set of global solar radiation for the case study location (Lat. 30˚ 51 ̀ N and long. 29˚ 34 ̀ E) are utilized for model establishment and validation. Mostly, common statistical indicators are employed for evaluating the performance of these models and recognizing the best model. The obtained results show that the artificial neural network models demonstrate promising performance in the prediction of global solar radiation. In addition, the proposed models provide superior consistency between the measured and estimated values.

scholarly journals Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar

2020 ◽  
Vol 33 (4) ◽  
pp. 110
Author(s):  
Layla A. Ahmed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Mean Square ◽  
Data Set ◽  
Intelligent Technique ◽  
Number Of Patients ◽  
Artificial Neural

    Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network.  The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model  and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Jenkins models on a data set for predict. Comparisons between the models has been performed using Criterion indicator Akaike information Criterion, mean square of error,  root mean square of error, and mean absolute percentage error, concluding that the prediction for patients with hypertension by using artificial neural networks model is the best.

Modelling Adjusted Baseline Energy in an Office Building Using Artificial Neural Network

2015 ◽  
Vol 785 ◽  
pp. 655-660 ◽  
Author(s):  
F.M.A. Rahman ◽  
Nofri Yenita Dahalan ◽  
Nor Shahida Razali
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Input Data ◽  
Energy Use ◽  
Energy Savings ◽  
Regression Technique ◽  
Office Building ◽  
Ann Model ◽  
Data Set ◽  
Artificial Neural

This paper proposes Artificial Neural Network (ANN) to determine adjusted baseline energy for quantifying energy savings from an energy efficiency program implemented in an office building. The input data to the ANN includes number of working days and cooling degree days (CDD) each month for one year period before implementation of the retrofitting program. On the other hand, output data is baseline energy use (i.e. energy use before retrofit). Since the input data to the network encompasses of 36 months set of data only, Bootstrap method is used to generate more input data without changing the input and output trend of the original data set. This is performed to increase validity of the training process. Once the optimum training parameters have been obtained, adjusted baseline energy is determined by feeding the number of working days and CDDs in the post-retrofit period (i.e. 12 months set of data) to the network. Energy savings is then calculated by comparing the adjusted baseline energy with the energy use after implementing the retrofit program. The performances of the ANN model are then compared with Multi-regression technique in term of R2, Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE) and Mean Absolute Deviation (MAD). Results show that the proposed ANN model has smaller errors and R2 closer to one compare to Multi-regression technique.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

2020 ◽  
Vol 38 (4A) ◽  
pp. 510-514
Author(s):  
Tay H. Shihab ◽  
Amjed N. Al-Hameedawi ◽  
Ammar M. Hamza
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Artificial Neural Network ◽  
Random Forest ◽  
Satellite Image ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Data Set ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019.  They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

scholarly journals Optimal Artificial Neural Network Type Selection Method for Usage in Smart House Systems

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 47
Author(s):  
Vasyl Teslyuk ◽  
Artem Kazarian ◽  
Natalia Kryvinska ◽  
Ivan Tsmots
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Data ◽  
Optimization Criterion ◽  
Fuzzy Input ◽  
Different Types ◽  
Smart House ◽  
Artificial Neural

In the process of the “smart” house systems work, there is a need to process fuzzy input data. The models based on the artificial neural networks are used to process fuzzy input data from the sensors. However, each artificial neural network has a certain advantage and, with a different accuracy, allows one to process different types of data and generate control signals. To solve this problem, a method of choosing the optimal type of artificial neural network has been proposed. It is based on solving an optimization problem, where the optimization criterion is an error of a certain type of artificial neural network determined to control the corresponding subsystem of a “smart” house. In the process of learning different types of artificial neural networks, the same historical input data are used. The research presents the dependencies between the types of neural networks, the number of inner layers of the artificial neural network, the number of neurons on each inner layer, the error of the settings parameters calculation of the relative expected results.

scholarly journals Predicting Pressure Sensitivity to Luminophore Content and Paint Thickness of Pressure-Sensitive Paint Using Artificial Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5188
Author(s):  
Mitsugu Hasegawa ◽  
Daiki Kurihara ◽  
Yasuhiro Egami ◽  
Hirotaka Sakaue ◽  
Aleksandar Jemcov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Augmentation ◽  
Percentage Error ◽  
Pressure Sensitivity ◽  
Pressure Sensitive Paint ◽  
Pressure Sensitive ◽  
Artificial Neural ◽  
Paint Thickness ◽  
Artificial Neural Network Ann

An artificial neural network (ANN) was constructed and trained for predicting pressure sensitivity using an experimental dataset consisting of luminophore content and paint thickness as chemical and physical inputs. A data augmentation technique was used to increase the number of data points based on the limited experimental observations. The prediction accuracy of the trained ANN was evaluated by using a metric, mean absolute percentage error. The ANN predicted pressure sensitivity to luminophore content and to paint thickness, within confidence intervals based on experimental errors. The present approach of applying ANN and the data augmentation has the potential to predict pressure-sensitive paint (PSP) characterizations that improve the performance of PSP for global surface pressure measurements.

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