Abstract
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.
| Translated title of the contribution | Comparative Electrical Load Prediction with Machine Learning and Fractional Calculus Applications |
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| Original language | Turkish |
| Title of host publication | Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798331518035 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2024 Electrical, Electronics and Biomedical Engineering Conference at 15th National Conference on Electrical and Electronics Engineering, ELECO 2024 - Bursa, Turkey Duration: 28 Nov 2024 → 30 Nov 2024 |
Publication series
| Name | Electrical-Electronics and Biomedical Engineering Conference, ELECO 2024 - Proceedings |
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Conference
| Conference | 2024 Electrical, Electronics and Biomedical Engineering Conference at 15th National Conference on Electrical and Electronics Engineering, ELECO 2024 |
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| Country/Territory | Turkey |
| City | Bursa |
| Period | 28/11/24 → 30/11/24 |
Bibliographical note
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