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    Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting
    (Taylor & Francis Inc, 2021) Bilgili, Mehmet; Yildirim, Alper; Ozbek, Arif; Celebi, Kerimcan; Ekinci, Firat
    Renewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country's energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.
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    Öğe
    The role of hydropower installations for sustainable energy development in Turkey and the world
    (Pergamon-Elsevier Science Ltd, 2018) Bilgili, Mehmet; Bilirgen, Harun; Ozbek, Arif; Ekinci, Firat; Demirdelen, Tugce
    Hydropower has the largest share among renewable energy sources in the world, supplying more than 16.6% of total global electricity to over 160 countries around the world. Global hydropower capacity increased to approximately 1096 GW with the addition of 25 GW of new hydropower capacity in 2016. With a 216 TWh per year generation capacity, Turkey's hydropower potential is the largest in Europe. The increased rate of installed capacity in Turkey was ranked 7th in the world in 2016 with an annual installed hydroelectric capacity of 0.8 GW. The main objective of this paper is to review the developments of hydropower installations around the world and in Turkey with an emphasis on the potential of small scale hydropower systems such as waterwheels in utilizing low head water flow for household electricity usage. In the first part of this study, the growth of worldwide hydropower capacity is reviewed and the countries with the largest installed and new built hydropower capacities are reported. In the second part of this study, the current status of Turkey's hydropower plants is discussed in detail with respect to annual regional rainfall, gross water mass flow and potential of Turkey's major water basins to demonstrate the potential energy output that can be harnessed from small-scale systems implemented in low-head water sources. In addition, the most recent information on Turkey's electricity generation and consumption rates are reported. (C) 2018 Elsevier Ltd. All rights reserved.