Using artificial neural networks and multiple regression models to generate economic solar thermal energy

Scilight ◽  
2021 ◽  
Vol 2021 (36) ◽  
pp. 361103
Author(s):  
Aili McConnon
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Thermal Energy ◽  
Regression Models ◽  
Solar Thermal ◽  
Solar Thermal Energy ◽  
Multiple Regression Models ◽  
Artificial Neural

scholarly journals Application of Artificial Neural Networks for estimating index floods

2012 ◽  
Vol 42 (4) ◽  
pp. 295-311 ◽  
Author(s):  
Viliam Šimor ◽  
Kamila Hlavčová ◽  
Silvia Kohnová ◽  
Ján Szolgay
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Multiple Regression ◽  
Regression Models ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Activation Function ◽  
Multiple Regression Models ◽  
Artificial Neural ◽  
Hidden Layer

Abstract This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

scholarly journals ARTIFICIAL NEURAL NETWORKS FOR MODELING HYPSOMETRIC RELATIONSHIPS OF Pinus caribaea Morelet var. caribaea Barr. & Golf.

2018 ◽  
Vol 36 (4) ◽  
pp. 891
Author(s):  
Ouorou Ganni Mariel GUERA ◽  
José Antônio Aleixo SILVA ◽  
Rinaldo Luiz Caraciolo FERREIRA ◽  
Héctor Barrero MEDEL ◽  
Daniel Álvarez LAZO
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sample Size ◽  
Regression Models ◽  
Growth Models ◽  
Pinus Caribaea ◽  
Three Stages ◽  
Artificial Neural ◽  
Sigmoidal Growth ◽  
Pinus Caribaea Morelet

The present study was carried out to compare the performances of regression models and Artificial Neural  Networks (ANNs) in hypsometric relationships modeling and to analyze the influence of ANN type  and sample size on ANN performance. The database was consisted by 65 circular plots of 500 m² in which  Diameter at Breast Height - DBH (cm) and Total Height - Ht (m) of 2538 trees were measured in plantations of Pinus caribaea var. caribaea in Macurije forest company, Cuba. The study was carried out in three  stages: i) Fit of traditional hypsometric models and sigmoidal growth models; ii) ANNs training and comparison of the selected ANN with the regression model selected; iii) Analysis of sample size and ANN type influences on the estimates precision by means of a completely random experimental design with 5x2 factorial arrangement, with the factors sample size (N) and ANN type (R). The results indicated that the best equation to estimate trees heights was that of Gompertz. The ANNs MLP 1-4-1 and MLP 8-4-1 were superior to the selected equation (Gompertz). Multi-Layer Perceptron ANNs generated more accurate estimates and their performances were less influenced by the sample size.

Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions

2020 ◽  
Vol 10 (2) ◽  
pp. 154-162
Author(s):  
Engin Özdemir ◽  
Didem Eren Sarici
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Calorific Value ◽  
Volatile Matter ◽  
Ash Content ◽  
Percentage Error ◽  
Multiple Regression Models ◽  
Artificial Neural

Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values. Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor. Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined. Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90). Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.

scholarly journals Multiple regression models and Artificial Neural Network (ANN) as prediction tools of changes in overall quality during the storage of spreadable processed Gouda cheese

2019 ◽  
Vol 245 (11) ◽  
pp. 2539-2547 ◽  
Author(s):  
J. Stangierski ◽  
D. Weiss ◽  
A. Kaczmarek
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multiple Regression ◽  
Regression Models ◽  
Principal Component ◽  
Multiple Regression Models ◽  
Gouda Cheese ◽  
Artificial Neural ◽  
Validation Set ◽  
Artificial Neural Network Ann

Abstract The aim of the study was to compare the ability of multiple linear regression (MLR) and Artificial Neural Network (ANN) to predict the overall quality of spreadable Gouda cheese during storage at 8 °C, 20 °C and 30 °C. The ANN used five factors selected by Principal Component Analysis, which was used as input data for the ANN calculation. The datasets were divided into three subsets: a training set, a validation set, and a test set. The multiple regression models were highly significant with high determination coefficients: R2 = 0.99, 0.87 and 0.87 for 8, 20 and 30 °C, respectively, which made them a useful tool to predict quality deterioration. Simultaneously, the artificial neural networks models with determination coefficient of R2 = 0.99, 0.96 and 0.96 for 8, 20 and 30 °C, respectively were built. The models based on ANNs with higher values of determination coefficients and lower RMSE values proved to be more accurate. The best fit of the model to the experimental data was found for processed cheese stored at 8 °C.

Sign in / Sign up

Export Citation Format

Share Document