A Comparison Study of Machine Learning Methods for Energy Consumption Forecasting in Industry

2021 ◽  
pp. 165-175
Author(s):  
Mouad Bahij ◽  
Moussa Labbadi ◽  
Mohamed Cherkaoui ◽  
Chakib Chatri ◽  
Soufian Lakrit
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Comparison Study ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals A Systematic Review of Energy Management Strategies for Resource Allocation in the Cloud: Clustering, Optimization and Machine Learning

Energies ◽  
2021 ◽  
Vol 14 (17) ◽  
pp. 5322
Author(s):  
Stanly Jayaprakash ◽  
Manikanda Devarajan Nagarajan ◽  
Rocío Pérez de Prado ◽  
Sugumaran Subramanian ◽  
Parameshachari Bidare Divakarachari
Keyword(s):  
Machine Learning ◽  
Resource Allocation ◽  
Energy Consumption ◽  
Energy Management ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Cloud Services ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Clustering Optimization

Nowadays, many organizations and individual users are employing cloud services extensively due to their efficiency, reliability and low cost. A key aspect for cloud data centers is to achieve management methods to reduce energy consumption, increasing the profit and reducing the environmental impact, which is critical in the deployment of leading-edge technologies today such as blockchain and digital finances, IoT, online gaming and video streaming. In this review, various clustering, optimization, and machine learning methods used in cloud resource allocation to increase the energy efficiency and performance are analyzed, compared and classified. Specifically, on the one hand, we discuss how clustering methods and optimization techniques are widely applied in energy management due to their capacity to provide solutions for energy consumption reduction. On the other hand, we study how multi-objective optimization methods focus on reducing energy consumption as well as service level agreement (SLA) violation, and improving quality of services (QoS) simultaneously. Also, we discuss how optimization methods such as the firefly algorithm, whale optimization algorithm (WOA), particle swarm optimization (PSO) and genetic algorithm (GA) provide the highest performance in the field. Moreover, we analyze how machine learning methods such as deep neural network (DNN), random forest, and support vector machine (SVM) are applied to the prediction of energy consumption in the cloud, showing an accurate performance in this prediction. Nevertheless, we study how the existing methods still have limitations of low convergence, trap into local optima and overfitting.

scholarly journals Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study

10.2196/11361 ◽  
2018 ◽  
Vol 4 (4) ◽  
pp. e11361 ◽  
Author(s):  
Canelle Poirier ◽  
Audrey Lavenu ◽  
Valérie Bertaud ◽  
Boris Campillo-Gimenez ◽  
Emmanuel Chazard ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Real Time ◽  
Comparison Study ◽  
Learning Methods ◽  
Methods Comparison ◽  
Machine Learning Methods

Application of machine learning methods for automatic interpretation of open hole logging data

2020 ◽  
pp. 44-47
Author(s):  
M.A. Basyrov ◽  
◽  
A.V. Akinshin ◽  
I.R. Makhmutov ◽  
Yu.D. Kantemirov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Automatic Interpretation ◽  
Open Hole
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