Energy management of hybrid energy system sources based on machine learning classification algorithms

Musbah, Hmeda; Aly, Hamed H.; Little, Timothy

doi:10.1016/j.epsr.2021.107436

Cited by 48 publications

(11 citation statements)

References 19 publications

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“…However, since KNN uses all the training data, it needs more time in analyzing large data and high memory for storage. The letter “K” indicates the number of nearest neighbors, and the term “nearest neighbor” indicates that the algorithm searches for the nearest point it needs to classify and label the closest point assigned to it [ 41 ]. Neural Network (NN): It is a mathematical system consisting of many processing units (neurons) interconnected in a weighted manner.…”

Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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Corn has great importance in terms of production in the field of agriculture and animal feed. Obtaining pure corn seeds in corn production is quite significant for seed quality. For this reason, the distinction of corn seeds that have numerous varieties plays an essential role in marketing. This study was conducted with 14,469 images of BT6470, Calipso, Es_Armandi, and Hiva types of corn licensed by BIOTEK. The classification of images was carried out in three stages. At the first stage, deep feature extraction of the four types of corn images was performed with the pretrained CNN model SqueezeNet 1000 deep features were obtained for each image. In the second stage, in order to reduce these features obtained from deep feature extraction with SqueezeNet, separate feature selection processes were performed with the Bat Optimization (BA), Whale Optimization (WOA), and Gray Wolf Optimization (GWO) algorithms among optimization algorithms. Finally, in the last stage, the features obtained from the first and second stages were classified by using the machine learning methods Decision Tree (DT), Naive Bayes (NB), multi-class Support Vector Machine (mSVM), k-Nearest Neighbor (KNN), and Neural Network (NN). In the classification processes of the features obtained in the first stage, the mSVM model has achieved the highest classification success with 89.40%. In the second stage, as a result of the classifications performed through the active features selected by using three types of feature selection algorithms (BA, WOA, GWO), the classification success obtained with the mSVM model was 88.82%, 88.72%, and 88.95%, respectively. The classification accuracies of the tested methods and the classification accuracies obtained in the first stage are close to each other in terms of classification success. However, with the algorithms used in feature selection, successful classification processes have been carried out with fewer features and in a shorter time. The results of the study, in which classification was carried out in the inexpensive, the objective, and the shorter time of processing for the corn types, present a different perspective in terms of classification performance.

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Section: Methodsmentioning

confidence: 99%

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Bhamidipati

Yaşar

Taşpınar

et al. 2022

Computational Intelligence and Neuroscience

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“…Machine learning algorithm is a supervised algorithm used to construct the relationship between independent and dependent variables, thereby achieving continuous prediction of data. The commonly used regression algorithms are linear regression (LR) [11], K-nearest neighbor classification algorithm (KNN) [12], Support Vector Machine (SVM) [13], Decision Tree (DT) [12], Random forest (RF) [15], Gradient Enhancement Decision Tree (GEDT) [16], Adaboost algorithm [17] and extreme random trees (ET) [18], etc. Logistic Regression is simply a machine learning method used to solve binary classification (0 or 1) problems, estimating the likelihood of a certain thing.…”

Section: Basic Definitionmentioning

confidence: 99%

Exploration of predictive applications based on machine learning

Wang,

2023

International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023)

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There is a great correlation between data and machine learning is essential to explore methods for analyzing data. As an important component of machine learning prediction and classification, logistic regression algorithms are widely applied in various disciplines. This study delves into the theoretical connotation and algorithm steps of logistic regression algorithm, and selects enterprise samples as calculation examples to construct the model and analyze the algorithm prediction, in order to gain a deeper understanding of logistic regression algorithm. The results show that the accuracy of the constructed model reaches 80%, and the area below the ROC is 0.82. The prediction model is better, providing a new method for predicting the development of enterprises.

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“…These power meters are interfaced with programmable logic controllers (PLC), and distributed control systems (DCS) [33], which apply the decisions and control directly the motors in order to optimize energy consumption. The same type of power meters shown in Figure 11 are connected to renewable energy sources in case the open-pit mine has photovoltaic, wind power, electric vehicles [34], or any other type of energy storage, for a global monitoring system integrating sources and loads. As shown in Figure 21…”

Section: Inhanced Architecture Toward Smart Grid Open-pit Minementioning

confidence: 99%

Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

Laayati

Bouzi

Chebak

2022

ASI

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Digitization in the mining industry and machine learning applications have improved the production by showing insights in different components. Energy consumption is one of the key components to improve the industry’s performance in a smart way that requires a very low investment. This study represents a new hardware, software, and data processing infrastructure for open-pit mines to overcome the energy 4.0 transition and digital transformation. The main goal of this infrastructure is adding an artificial intelligence layer to energy use in an experimental open-pit mine and giving insights on energy consumption and electrical grid quality. The achievement of these goals will ease the decision-making stage for maintenance and energy managers according to ISO 50001 standards. In order to minimize the energy consumption, which impact directly the profit and the efficiency of the industry, a design of a monitoring and peak load forecasting system was proposed and tested on the experimental open-pit mine of Benguerir. The main challenges of the application were the monitoring of typical loads machines per stage, feeding the supervisors by real time energy data on the same process SCADA view, parallel integrating hardware solutions to the same process control system, proposing a fast forest quantile regression algorithm to predict the energy demand response based on the data of different historical scenarios, finding correlations between the KPIs of energy consumption, mine production process and giving global insights on the electrical grid quality.

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Energy management of hybrid energy system sources based on machine learning classification algorithms

Cited by 48 publications

References 19 publications

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Computer-Aided Multiclass Classification of Corn from Corn Images Integrating Deep Feature Extraction

Exploration of predictive applications based on machine learning

Smart Energy Management System: Design of a Monitoring and Peak Load Forecasting System for an Experimental Open-Pit Mine

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