scholarly journals Machine learning based energy management system for grid disaster mitigation

2019 ◽  
Vol 2 (2) ◽  
pp. 172-182 ◽  
Author(s):  
Lizon Maharjan ◽  
Mark Ditsworth ◽  
Manish Niraula ◽  
Carlos Caicedo Narvaez ◽  
Babak Fahimi
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Management System ◽  
Disaster Mitigation ◽  
Energy Management System

scholarly journals Development of Machine Learning Methods in Hybrid Energy Storage Systems in Electric Vehicles

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Tzu-Chia Chen ◽  
Fouad Jameel Ibrahim Alazzawi ◽  
John William Grimaldo Guerrero ◽  
Paitoon Chetthamrongchai ◽  
Aleksei Dorofeev ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Energy Management ◽  
Management System ◽  
Storage Systems ◽  
Energy Management System ◽  
Energy Storage Systems ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Machine Learning Methods

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies.

scholarly journals Machine Learning-based Energy Management System for Prosumer

Evergreen ◽  
2020 ◽  
Vol 7 (2) ◽  
pp. 309-313
Author(s):  
Gde Dharma Nugraha ◽  
Budi Sudiarto ◽  
Kalamullah Ramli
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Management System ◽  
Energy Management System

scholarly journals Energy management System using Multi Agent Systems with IoT and Machine learning

2021 ◽  
Vol 1916 (1) ◽  
pp. 012013
Author(s):  
R Leo ◽  
V Vishwa Raj ◽  
E Vishal ◽  
K M Vimalan ◽  
G Dhanush ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Management System ◽  
Energy Management System ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Application of Machine Learning and Mathematical Programming in the Optimization of the Energy Management System for Hybrid-Electric Vessels Having Cyclic Operations

2018 ◽  
Author(s):  
N Mohammadzadeh ◽  
F Baldi ◽  
E Boonen
Keyword(s):  
Machine Learning ◽  
Energy Storage ◽  
Mathematical Programming ◽  
Energy Management ◽  
Management System ◽  
Storage System ◽  
Energy Storage System ◽  
Energy Management System ◽  
Operational Profile ◽  
Charging Stations

Shipping contributes today to 2.1% of global anthropogenic greenhouse gas emissions and its share is expected to grow in the coming years. At the same time, fuel prices are increasing and companies of the related increase in operational costs. This demands for higher efficiency in ship operations. In these regards, batterypowered vessels are often regarded as a promising solution. The existence of an energy storage element in the system, however, introduces additional challenges in its efficient control. This paper presents the application of machine learning and mathematical programming to the optimization of the energy management system of Diesel-electric vessels with an energy storage system operating according to a cyclical operational profile. The proposed energy management system uses unsupervised exclusive machine learning algorithms,k-means or k-medoids, to learn from prior operations. Then mathematical programming based on mixed-integer linear programming is used to address the problem of the optimal unit commitment by means of optimizing the system’s operations for minimizing fuel consumption. The calculated optimal state of charge of the energy storage system is used as the reference value for a proportional-integral controller during the real-time operations. The proposed energy management system is evaluated through its application to a case study corresponding to a hybrid-electric ferry operating in a urban area having cyclic operations through several stations. The results show that the efficiency of the control action is high with an accuracy ranging between 87% and 99%, when compared to an ideal controller, even in presence of large variations in the operational profile and the charging stations. Between the two tested clustering algorithms, k-means showed higher efficiency in the reduction of fuel consumption in presence of charging stations, while in absence of these, k-medoids showed to provide a better performance. 

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