Özet
Forecasting short-term electrical loads is crucial for power supply system management, sustainability and upkeep. There are two main objectives for this study. First, it seeks to provide a thorough comparison of deep learning and machine learning techniques while forecasting for 1-h-ahead and 1-d-ahead. To remove any disruptions from the time series and comprehend its dynamics, the study also concentrates on mathematically modelling electricity load data using fractional calculus.
| Tercüme edilen katkı başlığı | Comparative Electrical Load Prediction with Machine Learning and Fractional Calculus Applications |
|---|---|
| Orijinal dil | Türkçe |
| Ana bilgisayar yayını başlığı | Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings |
| Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Elektronik) | 9798331518035 |
| DOI'lar | |
| Yayın durumu | Yayınlandı - 2024 |
| Etkinlik | 2024 Electrical, Electronics and Biomedical Engineering Conference at 15th National Conference on Electrical and Electronics Engineering, ELECO 2024 - Bursa, Turkey Süre: 28 Kas 2024 → 30 Kas 2024 |
Yayın serisi
| Adı | Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings |
|---|
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| ???event.eventtypes.event.conference??? | 2024 Electrical, Electronics and Biomedical Engineering Conference at 15th National Conference on Electrical and Electronics Engineering, ELECO 2024 |
|---|---|
| Ülke/Bölge | Turkey |
| Şehir | Bursa |
| Periyot | 28/11/24 → 30/11/24 |
Bibliyografik not
Publisher Copyright:© 2024 IEEE.
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