scholarly journals Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

2020 ◽  
Author(s):  
Juan Sebastian Giraldo ◽  
Mauricio Salazar ◽  
Pedro P. Vergara ◽  
Georgios Tsaousoglou ◽  
Nikolaos Paterakis
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Synthetic Data ◽  
Optimal Operation ◽  
Stochastic Gradient ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Optimal Solutions ◽  
Stochastic Gradient Boosting ◽  
Pv Penetration

This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model, by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the operation points of a community ESS. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the sensitivity of the ML model has been tested considering the sampling size and the number of predictors. Results showed an accuracy of 98% for the SGBT model compared to optimal solutions, even after a reduction of 83% in the number of predictors.

scholarly journals Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

2021 ◽  
Author(s):  
Juan Sebastian Giraldo ◽  
Mauricio Salazar ◽  
Pedro P. Vergara ◽  
Georgios Tsaousoglou ◽  
Nikolaos Paterakis ◽  
...  
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Synthetic Data ◽  
Optimal Operation ◽  
Stochastic Gradient ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Optimal Solutions ◽  
Stochastic Gradient Boosting ◽  
Pv Penetration

This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model, by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the operation points of a community ESS. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the sensitivity of the ML model has been tested considering the sampling size and the number of predictors. Results showed an accuracy of 98% for the SGBT model compared to optimal solutions, even after a reduction of 83% in the number of predictors.

scholarly journals Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

2020 ◽  
Author(s):  
Juan Sebastian Giraldo ◽  
Mauricio Salazar ◽  
Pedro P. Vergara ◽  
Georgios Tsaousoglou ◽  
Nikolaos Paterakis
Keyword(s):  
Energy Storage ◽  
Distribution System ◽  
Synthetic Data ◽  
Optimal Operation ◽  
Stochastic Gradient ◽  
Training Dataset ◽  
Gradient Boosting ◽  
Optimal Solutions ◽  
Stochastic Gradient Boosting ◽  
Pv Penetration

This paper proposes an algorithm for the optimal operation of community energy storage systems (ESSs) using a machine learning (ML) model, by solving a nonlinear programming (NLP) problem iteratively to obtain synthetic data. The NLP model minimizes the network's total energy losses by setting the operation points of a community ESS. The optimization model is solved recursively by Monte Carlo simulations in a distribution system with high PV penetration, considering uncertainty in exogenous parameters. Obtained optimal solutions provide the training dataset for a stochastic gradient boosting trees (SGBT) ML algorithm. The predictions obtained from the ML model have been compared to the optimal ESS operation to assess the model's accuracy. Furthermore, the sensitivity of the ML model has been tested considering the sampling size and the number of predictors. Results showed an accuracy of 98% for the SGBT model compared to optimal solutions, even after a reduction of 83% in the number of predictors.

Optimal Operation of Community Energy Storage using Stochastic Gradient Boosting Trees

2021 ◽  
Author(s):  
Juan S. Giraldo ◽  
Mauricio Salazar ◽  
Pedro P. Vergara ◽  
Georgios Tsaousoglou ◽  
J.G. Slootweg ◽  
...  
Keyword(s):  
Energy Storage ◽  
Optimal Operation ◽  
Stochastic Gradient ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting

Enhanced prediction of vulnerable Web components using Stochastic Gradient Boosting Trees

2019 ◽  
Vol 15 (2) ◽  
pp. 201-214 ◽  
Author(s):  
Mahmoud Elish
Keyword(s):  
Web Applications ◽  
Prediction Models ◽  
Stochastic Gradient ◽  
Gradient Boosting ◽  
Data Sets ◽  
Content Type ◽  
Stochastic Gradient Boosting ◽  
Security Inspection ◽  
Novel Model ◽  
Efficient Software

Purpose Effective and efficient software security inspection is crucial as the existence of vulnerabilities represents severe risks to software users. The purpose of this paper is to empirically evaluate the potential application of Stochastic Gradient Boosting Trees (SGBT) as a novel model for enhanced prediction of vulnerable Web components compared to common, popular and recent machine learning models. Design/methodology/approach An empirical study was conducted where the SGBT and 16 other prediction models have been trained, optimized and cross validated using vulnerability data sets from multiple versions of two open-source Web applications written in PHP. The prediction performance of these models have been evaluated and compared based on accuracy, precision, recall and F-measure. Findings The results indicate that the SGBT models offer improved prediction over the other 16 models and thus are more effective and reliable in predicting vulnerable Web components. Originality/value This paper proposed a novel application of SGBT for enhanced prediction of vulnerable Web components and showed its effectiveness.

scholarly journals Stochastic Gradient Boosting Model for Twitter Spam Detection

2022 ◽  
Vol 41 (2) ◽  
pp. 849-859
Author(s):  
K. Kiruthika Devi ◽  
G. A. Sathish Kumar
Keyword(s):  
Stochastic Gradient ◽  
Gradient Boosting ◽  
Spam Detection ◽  
Stochastic Gradient Boosting

Mobile Phone Customer Type Discrimination via Stochastic Gradient Boosting

2007 ◽  
Vol 3 (2) ◽  
pp. 32-53 ◽  
Author(s):  
Dan Steinberg ◽  
Mikhaylo Golovnya ◽  
Nicholas Scott Cardell
Keyword(s):  
Mobile Phone ◽  
Stochastic Gradient ◽  
Gradient Boosting ◽  
Stochastic Gradient Boosting ◽  
Customer Type

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation

Neurosurgery ◽  
2019 ◽  
Vol 85 (4) ◽  
pp. E671-E681 ◽  
Author(s):  
Aditya V Karhade ◽  
Quirina C B S Thio ◽  
Paul T Ogink ◽  
Christopher M Bono ◽  
Marco L Ferrone ◽  
...  
Keyword(s):  
Metastatic Disease ◽  
Web Application ◽  
Performance Status ◽  
Predictive Performance ◽  
Operative Management ◽  
Stochastic Gradient ◽  
Patient Specific ◽  
Gradient Boosting ◽  
Support Vector ◽  
Stochastic Gradient Boosting

Abstract BACKGROUND Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality. OBJECTIVE To develop predictive algorithms for spinal metastatic disease at these time points and to provide patient-specific explanations of the predictions generated by these algorithms. METHODS Retrospective review was conducted at 2 large academic medical centers to identify patients undergoing initial operative management for spinal metastatic disease between January 2000 and December 2016. Five models (penalized logistic regression, random forest, stochastic gradient boosting, neural network, and support vector machine) were developed to predict 90-d and 1-yr mortality. RESULTS Overall, 732 patients were identified with 90-d and 1-yr mortality rates of 181 (25.1%) and 385 (54.3%), respectively. The stochastic gradient boosting algorithm had the best performance for 90-d mortality and 1-yr mortality. On global variable importance assessment, albumin, primary tumor histology, and performance status were the 3 most important predictors of 90-d mortality. The final models were incorporated into an open access web application able to provide predictions as well as patient-specific explanations of the results generated by the algorithms. The application can be found at https://sorg-apps.shinyapps.io/spinemetssurvival/ CONCLUSION Preoperative estimation of 90-d and 1-yr mortality was achieved with assessment of more flexible modeling techniques such as machine learning. Integration of these models into applications and patient-centered explanations of predictions represent opportunities for incorporation into healthcare systems as decision tools in the future.

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