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Öğe Advanced forecast models for the climate and energy crisis: The case of the California independent system operator(Pergamon-Elsevier Science Ltd, 2025) Bulut, Merve; Aydilek, Huseyin; Erten, Mustafa Yasin; Ozcan, EvrencanClimate change across the globe, especially extreme temperature events, is increasing pressures on energy systems. The extraordinary situation that California and the West faced in late August and early September 2022, when record temperatures led to a spike in electricity demand, provided an important backdrop for the resilience and sustainability of clean energy technologies. The electricity market managed by the California Independent System Operator is considered in this study to examine the potential impacts on electricity demand spikes and system resilience. The methodology of the research involves analyzing the system operator's responses to electricity demand using advanced deep learning algorithms, convolutional neural network - long-short term memory and attention mechanism models. 1, 3 and 7-days forecasts of electricity demand were made using models in the day ahead market. In 1-day forecasts, while the former models have a mean absolute percentage error value of 12.40%, the latter model has a lower error rate of 10.36%. Overall findings obtained from various scenarios show that the long-short term memory - attention mechanism can more effectively understand complex patterns in energy demand and has the potential to increase system stability against such extreme weather events. The advanced horizon of the study offers an important perspective on how clean energy technologies, especially battery energy storage systems, can provide solutions to today's priority problems such as climate change and extreme temperature.Öğe Automatic Detection and Classification of Non Metallic Inclusions in Steel via Image Processing(Ieee, 2014) Aydilek, Huseyin; Polat, EdizNon-metallic chemical components (silicate, sulfide, alumina, globular oxide) originated from production process cause inclusions in steel. These inclusions are one of the important factors that directly affect quality of steel. Before raw steel is used, the detection and classification of the inclusions according to international standards ASTM E45 will prevent faults that may occur during or after production. In this paper, an accurate, fast and reliable image processing based system that automatically detects and classifies non metallic inclusions in steel according to ASTM E45 standards is developed. The system is tested for detection and classification of Type A inclusions and successful results are obtained.