Yazar "Elweddad, Mohamed" seçeneğine göre listele

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    Energy Management Techniques in Off Grid Energy Systems: A Review
    (Springer International Publishing Ag, 2022) Elweddad, Mohamed; Guneser, Muhammet Tahir; Yusupov, Ziyodulla
    Energy management system (EMS) algorithms and strategies are improved to make sure power continuity in all circumstances, minimizing energy production cost and protect grid components from being damaged. Energy management presents a viable solution to issues relating to the energy sector, such as rising demand, rising energy costs, sustainable supply, and environmental impact. The approaches performing energy management strategies, solution algorithms, and systems simulations to overcome many problems in low voltage distribution systems. Furthermore, in this paper some techniques and methodologies are considered to improve energy management of off-grid power systems with microgrid. The reviewed works in this paper cover the various structures of off-grid hybrid microgrids. The most common technologies and strategies have been used in the field of power management, in addition, providing of some future research directions.
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    Intelligent Energy Management and Prediction of Micro Grid Operation Based On Machine Learning and Genetic Algorithm
    (Int Journal Renewable Energy Research, 2022) Elweddad, Mohamed; Guneser, Muhammet Tahir
    Micro grid energy management has become critically important due to inefficient power use in the residential sector. High energy consumption necessitates developing a strategy to manage the power flow efficiently. For this purpose, this work has been divided into two phases: The first is the ON/OFF operation, which has been executed using a genetic algorithm for the hybrid system, including diesel generator, solar photovoltaic (PV), wind turbine, and battery. Then, in the second phase, the output results were used as input in three algorithms to predict load and supply dispatch one month ahead. This study has two objectives; the first is to decide which energy source should meet the load one month ahead. The second is to compare the outcomes of machine-learning techniques, namely Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbours (KNN), to determine the one that performs the best. The results indicated that the DT technique has the best performance in the application of classification with an accuracy of 100%. The findings also show that the RF approach gives acceptable results with an accuracy of up to 98%, and the KNN algorithm was poor in terms of accuracy with a value of 28%.