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crossrefEnergies2021-09-28Cited by 82

Electric Vehicles Charging Management Using Machine Learning Considering Fast Charging and Vehicle-to-Grid Operation

Mostafa Shibl, Loay Ismail, Ahmed Massoud

Electric vehicles (EVs) have gained in popularity over the years. The charging of a high number of EVs harms the distribution system. As a result, increased transformer overloads, power losses, and voltage fluctuations may occur. Thus, management of EVs is required to address the…

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crossrefEnergies2023-06-11Cited by 8

On-Road Experimental Campaign for Machine Learning Based State of Health Estimation of High-Voltage Batteries in Electric Vehicles

Edoardo Lelli, Alessia Musa, Emilio Batista, Daniela Anna Misul, Giovanni Belingardi

The present study investigates the use of machine learning algorithms to estimate the state of health (SOH) of high-voltage batteries in electric vehicles. The analysis is based on open-circuit voltage (OCV) measurements from 12 vehicles with different mileage conditions and focu…

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crossrefEnergies2024-11-28Cited by 4

Transfer Learning for the Prediction of Energy Performance of Water-Cooled Electric Chillers: Grey-Box Models Versus Deep Neural Network (DNN) Models

Hongwen Dou, Radu Zmeureanu

The development of data-driven prediction models of energy performance of HVAC equipment, such as chillers, depends on the quality and quantity of measurement data for the model training. The practical applications always struggle with the credibility of results when the training…

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crossrefEnergies2022-02-09Cited by 45

Advanced Ensemble Methods Using Machine Learning and Deep Learning for One-Day-Ahead Forecasts of Electric Energy Production in Wind Farms

Paweł Piotrowski, Dariusz Baczyński, Marcin Kopyt, Tomasz Gulczyński

The ability to precisely forecast power generation for large wind farms is very important, since such generation is highly unstable and creates problems for Distribution and Transmission System Operators to properly prepare the power system for operation. Forecasts for the next 2…

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crossrefEnergies2024-03-18Cited by 3

Model for Identification of Electrical Appliance and Determination of Patterns Using High-Resolution Wireless Sensor NETWORK for the Efficient Home Energy Consumption Based on Deep Learning

Fernando Ulloa-Vásquez, Victor Heredia-Figueroa, Cristóbal Espinoza-Iriarte, José Tobar-Ríos, Fernanda Aguayo-Reyes, Dante Carrizo, et al.

The growing demand for electricity and the constant increase in electricity rates have intensified the interest of residential and non-residential energy consumers to reduce their energy consumption. The introduction of non-conventional renewable energies (photovoltaic and wind,…

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crossrefEnergies2021-01-22Cited by 193

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Tianze Lan, Kittisak Jermsittiparsert, Sara T. Alrashood, Mostafa Rezaei, Loiy Al-Ghussain, Mohamed A. Mohamed

Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse…

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