A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers
- DOI
- 10.1016/j.segan.2023.101171
- Published
- 2023-12
- Container
- Sustainable Energy, Grids and Networks
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.segan.2023.101171,
title = {A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers},
author = {Sotiris Pelekis and Ioannis-Konstantinos Seisopoulos and Evangelos Spiliotis and Theodosios Pountridis and Evangelos Karakolis and Spiros Mouzakitis and Dimitris Askounis},
year = {2023},
journal = {Sustainable Energy, Grids and Networks},
doi = {10.1016/j.segan.2023.101171},
url = {https://doi.org/10.1016/j.segan.2023.101171}
}RIS
TY - JOUR TI - A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers AU - Sotiris Pelekis AU - Ioannis-Konstantinos Seisopoulos AU - Evangelos Spiliotis AU - Theodosios Pountridis AU - Evangelos Karakolis AU - Spiros Mouzakitis AU - Dimitris Askounis PY - 2023 JO - Sustainable Energy, Grids and Networks DO - 10.1016/j.segan.2023.101171 UR - https://doi.org/10.1016/j.segan.2023.101171 ER -
APA
Pelekis, S., Seisopoulos, I., Spiliotis, E., Pountridis, T., Karakolis, E., Mouzakitis, S., & Askounis, D. (2023). A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers. Sustainable Energy, Grids and Networks. https://doi.org/10.1016/j.segan.2023.101171
Source records
- crossref · retrieved 2026-09-26T17:15:43.754Z