Zaman Serileri Tahminlenmesinde Makine Öğrenimi ve Derin Öğrenme Tekniklerinin Kıyaslanması: Türkiye Elektirik Üretimi için En İyi Tahmin Modelinin Seçilmesine Yönelik Bir Vaka Çalışması
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SDU Journal of Natural and Applied Sciences
Cited by 9 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Predicting world electricity generation by sources using different machine learning algorithms;International Journal of Oil, Gas and Coal Technology;2024
2. The Time-Series Production Simulation in Cost Management of New Energy Grid Connection Under the Internet of Things;IEEE Access;2024
3. Gross electricity consumption forecasting using LSTM and SARIMA approaches: A case study of Türkiye;Energy;2023-12
4. Predictive modeling of marine fish production in Brunei Darussalam's aquaculture sector: A comparative analysis of machine learning and statistical techniques;International Journal of ADVANCED AND APPLIED SCIENCES;2023-07
5. Deep learning based electricity demand forecasting to minimize the cost of energy imbalance: A real case application with some fortune 500 companies in Türkiye;Engineering Applications of Artificial Intelligence;2023-02
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