Yazar "Elweddad, M." seçeneğine göre listele

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    Designing an irrigation system using photovoltaic energy by considering crop type in Fergana Valley
    (EDP Sciences, 2021) Kuzey, S.; Seker, C.; Elweddad, M.; Güneser, M.T.
    Today, the importance of energy cost and efficiency is gradually increase. The decrease in drinking water and agricultural water resources, increases the interest in drip irrigation systems in agricultural irrigation. Environmentally friendly photovoltaic drip irrigation systems (PVDIS) are the appropriate solution in regions where there is no electricity distribution network, where it is far away, or where power cuts are frequently. This study is carried out in the Fergana Valley of Uzbekistan. Regional climate data obtained from Climwat 2.0 software are processed in Cropwat 8.0 software. Crops that are both the source of livelihood of the people of the region and that can be used in this study have been determined. Annual and daily water needs are analyzed so that these crops are irrigated every seven days. A system is designed by taking the data of the crop with the highest water requirement as a reference. The drip irrigation system is set up in a PVsyst 7.1.7 simulation environment to pump 114.24 m3 of water daily from a 5-meter-deep river with a 1.8 kW photovoltaic system. The efficiency of the system is 58.7% and the efficiency of the pump is 34.5%. Crop water need is met at the rate of 98.87%. It is predicted that the designed and analyzed PVDIS will provide efficiency in energy and water resources. © The Authors.
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    Energy management and optimization of microgrid system using particle swarm optimization algorithm
    (American Institute of Physics Inc., 2022) Elweddad, M.; Guneser, M.T.; Yusupov, Z.
    An optimization model is proposed to manage a day-ahead optimal energy management strategy for economic operation of Microgrids. The model is based on a using particle swarm optimization algorithm (PSO) for scheduling four energy sources (grid, PV system, wind system, energy storage system) with 24 hours' time step, considering forecasted electrical demands, weather, and renewable energy generations. In this paper, the objective function is to minimize the cost of electricity generation and to manage delivering power from hybrid sources to the demand. The results showed that scheduling and controlling of different energy sources in efficient way reduce the total cost of power generation and ensure sustainable power flow. It is important to enhance the usage of solar and wind sources, optimize the operation of storage systems. © 2022 Author(s).
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    Intelligent Energy Management and Prediction of Micro Grid Operation Based On Machine Learning Algorithms and Genetic Algorithm
    (Gazi Universitesi, 2022) Elweddad, M.; Güneser, M.T.
    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%. © 2022, International Journal of Renewable Energy Research. All Rights Reserved.