scholarly journals Power Plant Energy Predictions Based on Thermal Factors Using Ridge and Support Vector Regressor Algorithms

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7254
Author(s):  
Asif Afzal ◽  
Saad Alshahrani ◽  
Abdulrahman Alrobaian ◽  
Abdulrajak Buradi ◽  
Sher Afghan Khan
Keyword(s):  
Power Plant ◽  
Polynomial Regression ◽  
Ambient Pressure ◽  
Absolute Error ◽  
Combined Cycle ◽  
Energy Output ◽  
Percentage Error ◽  
Support Vector ◽  
Output Data ◽  
Input Variables

This work aims to model the combined cycle power plant (CCPP) using different algorithms. The algorithms used are Ridge, Linear regressor (LR), and upport vector regressor (SVR). The CCPP energy output data collected as a factor of thermal input variables, mainly exhaust vacuum, ambient temperature, relative humidity, and ambient pressure. Initially, the Ridge algorithm-based modeling is performed in detail, and then SVR-based LR, named as SVR (LR), SVR-based radial basis function—SVR (RBF), and SVR-based polynomial regression—SVR (Poly.) algorithms, are applied. Mean absolute error (MAE), R-squared (R2), median absolute error (MeAE), mean absolute percentage error (MAPE), and mean Poisson deviance (MPD) are assessed after their training and testing of each algorithm. From the modeling of energy output data, it is seen that SVR (RBF) is the most suitable in providing very close predictions compared to other algorithms. SVR (RBF) training R2 obtained is 0.98 while all others were 0.9–0.92. The testing predictions made by SVR (RBF), Ridge, and RidgeCV are nearly the same, i.e., R2 is 0.92. It is concluded that these algorithms are suitable for predicting sensitive output energy data of a CCPP depending on thermal input variables.

scholarly journals Predicting the Istanbul Stock Exchange Index Return using Technical Indicators

2013 ◽  
Vol 2 (3) ◽  
pp. 111-117
Author(s):  
Senol Emir
Keyword(s):  
Stock Exchange ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Model Parameters ◽  
Technical Indicators ◽  
Istanbul Stock Exchange ◽  
Vector Machines ◽  
Classical Linear Regression ◽  
Daily Returns

The aim of this study to examine the performance of Support Vector Regression (SVR) which is a novel regression method based on Support Vector Machines (SVM) approach in predicting the Istanbul Stock Exchange (ISE) National 100 Index daily returns. For bechmarking, results given by SVR were compared to those given by classical Linear Regression (LR). Dataset contains 6 technical indicators which were selected as model inputs for 2005-2011 period. Grid search and cross valiadation is used for finding optimal model parameters and evaluating the models. Comparisons were made based on Root Mean Square (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Theil Inequality Coefficient (TIC) and Mean Mixed Error (MME) metrics. Results indicate that SVR outperforms the LR for all metrics.

scholarly journals Visualization & Prediction of COVID-19 Future Outbreak by Using Machine Learning

2021 ◽  
Vol 13 (3) ◽  
pp. 16-32
Author(s):  
Ahmed Hassan Mohammed Hassan ◽  
◽  
Arfan Ali Mohammed Qasem ◽  
Walaa Faisal Mohammed Abdalla ◽  
Omer H. Elhassan
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Future Perspective ◽  
Support Vector ◽  
Squared Error ◽  
Vector Machines ◽  
The World ◽  
Negative Factors

Day by day, the accumulative incidence of COVID-19 is rapidly increasing. After the spread of the Corona epidemic and the death of more than a million people around the world countries, scientists and researchers have tended to conduct research and take advantage of modern technologies to learn machine to help the world to get rid of the Coronavirus (COVID-19) epidemic. To track and predict the disease Machine Learning (ML) can be deployed very effectively. ML techniques have been anticipated in areas that need to identify dangerous negative factors and define their priorities. The significance of a proposed system is to find the predict the number of people infected with COVID19 using ML. Four standard models anticipate COVID-19 prediction, which are Neural Network (NN), Support Vector Machines (SVM), Bayesian Network (BN) and Polynomial Regression (PR). The data utilized to test these models content of number of deaths, newly infected cases, and recoveries in the next 20 days. Five measures parameters were used to evaluate the performance of each model, namely root mean squared error (RMSE), mean squared error (MAE), mean absolute error (MSE), Explained Variance score and r2 score (R2). The significance and value of proposed system auspicious mechanism to anticipate these models for the current cenario of the COVID-19 epidemic. The results showed NN outperformed the other models, while in the available dataset the SVM performs poorly in all the prediction. Reference to our results showed that injuries will increase slightly in the coming days. Also, we find that the results give rise to hope due to the low death rate. For future perspective, case explanation and data amalgamation must be kept up persistently.

The Effect of Ambient Conditions on the Performance of Supplementary Fired Gas-Steam Combined Cycle

2006 ◽  
Author(s):  
M. J. Kermani ◽  
B. Rad Nasab ◽  
M. Saffar-Avval
Keyword(s):  
Power Plant ◽  
Ambient Temperature ◽  
Gas Turbine ◽  
Ambient Pressure ◽  
Combined Cycle ◽  
Ambient Conditions ◽  
Site Location ◽  
Steam Generators ◽  
Steam Cycle

The effect of ambient conditions, ambient temperature and site location of the power plant (the altitude or ambient pressure), on the performance of a typical supplementary fired (SF) gas-steam combined cycle (CC) is studied, and its performances are compared with that of the unfired case. The CC used in the present study is comprised of two V94.2 gas turbine units, two HR-steam generators and a single steam cycle. For the cases studied, it is observed that SF can increase the total net power of the CC by 5% and the efficiency for the fired-cycle is observed to be about 1% less than that of unfired-cycle case. The variations of the total net power with ambient temperature for both supplementary fired and unfired cases (slope w.r.t. the ambient temperature) are almost identical.

SVR-Based Predictive Model for 2519 Aluminum Alloy

2016 ◽  
Vol 719 ◽  
pp. 41-45 ◽  
Author(s):  
J.L. Tang ◽  
H.Y. Liu ◽  
M.H. Gui ◽  
J.Y. Tang
Keyword(s):  
Aluminum Alloy ◽  
Flow Stress ◽  
Absolute Error ◽  
Multiple Correlation Coefficient ◽  
Influential Factors ◽  
Percentage Error ◽  
Support Vector ◽  
Strain And Strain Rate ◽  
Alloy Base ◽  
Dynamical Parameters

For 2519 aluminum alloy, there are very complex nonlinear relations among the thermal dynamical parameters in the process of deforming. In this paper, the support vector regression (SVR) approach is proposed to establish a model for predicting flow stress of 2519 alloy base on the flows tress experimental data of 2519 aluminum alloy under two influential factors, including strain and strain rate. Research showed that the prediction precision of SVR model is high enough: the mean absolute error (MAE) is 0.181, mean absolute percentage error (MAPE) is 0.434%, root mean square error (RMSE) is 0.22, multiple correlation coefficient (R2) is 0.998. This research suggests that SVR is an effective and powerful tool for predicting the flow stress of 2519 aluminum alloy.

Prediction of Glass Transition Temperature of Polymer by Support Vector Regression

2012 ◽  
Vol 455-456 ◽  
pp. 436-442
Author(s):  
J.F. Pei ◽  
C.Z. Cai ◽  
X.J. Zhu ◽  
G.L. Wang ◽  
B. Yan
Keyword(s):  
Glass Transition ◽  
Transition Temperature ◽  
Glass Transition Temperature ◽  
Support Vector Regression ◽  
Density Functional ◽  
Absolute Error ◽  
Percentage Error ◽  
Support Vector ◽  
Quantum Chemical Descriptors ◽  
Leave One Out

. Based on two quantum chemical descriptors (the thermal energy Ethermal and the total energy of the whole system EHF) calculated from the structures of the repeat units of polyacrylamides by density functional theory (DFT), the support vector regression (SVR) approach combined with particle swarm optimization (PSO), is proposed to establish a model for prediction of the glass transition temperature (Tg) of polyacrylamides. The prediction performance of SVR was compared with that of multivariate linear regression (MLR). The results show that the mean absolute error (MAE=4.65K), mean absolute percentage error (MAPE=1.28%) and correlation coefficient (R2=0.9818) calculated by leave-one–out cross validation (LOOCV) via SVR models are superior to those achieved by QSPR (MAE=14.25K, MAPE=4.39% and R2=0.9211) and QSPR-LOO (MAE=17.01K, MAPE=5.66% and R2=0.8823) models for the identical samples, respectively. The prediction results strongly demonstrate that the modeling and generalization abilities of SVR model consistently surpass those of QSPR and QSPR-LOO models. It is revealed that the established SVR model is more suitable to be used for prediction of the Tg values for unknown polymers possessing similar structure than the conventional MLR approach. These suggest that SVR is a promising and practical methodology to predict the glass transition temperature of polyacrylamides.

scholarly journals Power Prediction of Combined Cycle Power Plant (CCPP) Using Machine Learning Algorithm-Based Paradigm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Raheel Siddiqui ◽  
Hafeez Anwar ◽  
Farman Ullah ◽  
Rehmat Ullah ◽  
Muhammad Abdul Rehman ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Power Plant ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Combined Cycle ◽  
Machine Learning Algorithms ◽  
Power Prediction ◽  
Boosted Regression Tree ◽  
Combined Cycle Power Plant

Power prediction is important not only for the smooth and economic operation of a combined cycle power plant (CCPP) but also to avoid technical issues such as power outages. In this work, we propose to utilize machine learning algorithms to predict the hourly-based electrical power generated by a CCPP. For this, the generated power is considered a function of four fundamental parameters which are relative humidity, atmospheric pressure, ambient temperature, and exhaust vacuum. The measurements of these parameters and their yielded output power are used to train and test the machine learning models. The dataset for the proposed research is gathered over a period of six years and taken from a standard and publicly available machine learning repository. The utilized machine algorithms are K -nearest neighbors (KNN), gradient-boosted regression tree (GBRT), linear regression (LR), artificial neural network (ANN), and deep neural network (DNN). We report state-of-the-art performance where GBRT outperforms not only the utilized algorithms but also all the previous methods on the given CCPP dataset. It achieves the minimum values of root mean square error (RMSE) of 2.58 and absolute error (AE) of 1.85.

scholarly journals A Comparative Analysis on Prediction Performance of Regression Models during Machining of Composite Materials

Materials ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 6689
Author(s):  
Shibaprasad Bhattacharya ◽  
Kanak Kalita ◽  
Robert Čep ◽  
Shankar Chakraborty
Keyword(s):  
Composite Materials ◽  
Regression Models ◽  
Polynomial Regression ◽  
Prediction Performance ◽  
Wilcoxon Test ◽  
Percentage Error ◽  
Support Vector ◽  
Machining Processes ◽  
Median Regression ◽  
Input Parameters

Modeling the interrelationships between the input parameters and outputs (responses) in any machining processes is essential to understand the process behavior and material removal mechanism. The developed models can also act as effective prediction tools in envisaging the tentative values of the responses for given sets of input parameters. In this paper, the application potentialities of nine different regression models, such as linear regression (LR), polynomial regression (PR), support vector regression (SVR), principal component regression (PCR), quantile regression, median regression, ridge regression, lasso regression and elastic net regression are explored in accurately predicting response values during turning and drilling operations of composite materials. Their prediction performance is also contrasted using four statistical metrics, i.e., mean absolute percentage error, root mean squared percentage error, root mean squared logarithmic error and root relative squared error. Based on the lower values of those metrics and Friedman rank and aligned rank tests, SVR emerges out as the best performing model, whereas the prediction performance of median regression is worst. The results of the Wilcoxon test based on the drilling dataset identify the existence of statistically significant differences between the performances of LR and PCR, and PR and median regression models.

scholarly journals Path Smoothing With Support Vector Regression

2020 ◽  
Vol 4 (1) ◽  
pp. 142-150
Author(s):  
Donni Richasdy ◽  
Saiful Akbar
Keyword(s):  
Support Vector Regression ◽  
Learning Algorithm ◽  
Absolute Error ◽  
Moving Object ◽  
Percentage Error ◽  
Support Vector ◽  
Average Value ◽  
Path Smoothing ◽  
Aircraft Tracking ◽  
Smoothing Process

One of moving object problems is the incomplete data that acquired by Geo-tracking technology. This phenomenon can be found in aircraft ground-based tracking with data loss come near to 5 minutes. It needs path smoothing process to complete the data. One solution of path smoothing is using physics of motion, while this research performs path smoothing process using machine learning algorithm that is Support Vector Regression (SVR). This study will optimize the SVR configuration parameters such as kernel, common, gamma, epsilon and degree. Support Vector Regression will predict value of the data lost from aircraft tracking data. We use combination of mean absolute error (MAE) and mean absolute percentage error (MAPE) to get more accuracy. MAE will explain the average value of error that occurs, while MAPE will explain the error percentage to the data. In the experiment, the best error value MAE 0.52 and MAPE 2.07, which means error data ± 0.52, this is equal to 2.07% of the overall data value.Keywords: Moving Object, Path Smoothing, Support Vector Regression, MAE

scholarly journals Predicting and Analysing the Behaviour of COVID-19

2021 ◽  
pp. 40-46
Author(s):  
Gaurav Singh ◽  
Shivam Rai ◽  
Himanshu Mishra ◽  
Manoj Kumar
Keyword(s):  
Machine Learning ◽  
Polynomial Regression ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Systems Science ◽  
Data Repository ◽  
Support Vector ◽  
Squared Error

The prime objective of this work is to predicting and analysing the Covid-19 pandemic around the world using Machine Learning algorithms like Polynomial Regression, Support Vector Machine and Ridge Regression. And furthermore, assess and compare the performance of the varied regression algorithms as far as parameters like R squared, Mean Absolute Error, Mean Squared Error and Root Mean Squared Error. In this work, we have used the dataset available on Covid-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at John Hopkins University. We have analyzed the covid19 cases from 22/1/2020 till now. We applied a supervised machine learning prediction model to forecast the possible confirmed cases for the next ten days.

Sign in / Sign up

Export Citation Format

Share Document