WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7150
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Article Univariate Deep Learning Models for Short-Term Electricity Load Forecasting from Renewables(Ankara University, Faculty of Science, 2025) Kabran, Fatma Basoglu; Unlu, Kamil DemirberkRenewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.Conference Object Renewable Energy Powered Artificial Mixing of the Reservoirs(IAHR-Int Assoc Hydro-Environment Engineering Research, 2023) Hazar, Oguz; Elci, SebnemReservoirs are essential and critical infrastructures and require proper management practices to improve water quality. Thermal stratification observed in the reservoirs impairs the water quality affecting the algae population and the solubility of heavy metals from sediment particles. Artificial mixing methods are widely used to improve water quality in thermally stratified eutrophic lakes and reservoirs. Air diffuser systems, water pumps, and water jets are commonly applied for aeration and mixing purposes. Although these methods proved to be effective in the literature, aeration and pumping equipment consume a great amount of electricity and require complementary infrastructures and facilities resulting in high costs. The presented study focuses on aeration of the water column powered by renewable energy. A Savonius turbine is implemented to an artificial mixing setup tested in the laboratory. The shaft of the turbine is directly connected to the pump so that the motion is transferred to the pump shaft eliminating the need for the production/storage of the electricity. The effectiveness of the wind turbine on destratification of the water column is investigated based on various wind conditions. In the experiments, static and dynamic torque values are monitored using a modified design of a rope brake dynamometer composed of a highly precise torque sensor, pulleys, and, a platform for connecting this torque measurement system to the wind turbine. The system is further evaluated for its destratification efficiency of the water column through the experiments.