Publicação
Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction
| authorProfile.email | biblioteca@isel.pt | |
| datacite.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | |
| dc.contributor.author | Seman, Laio Oriel | |
| dc.contributor.author | Yow, Kin-Choong | |
| dc.contributor.author | Stefenon, Stefano Frizzo | |
| dc.date.accessioned | 2026-01-28T09:34:20Z | |
| dc.date.available | 2026-01-28T09:34:20Z | |
| dc.date.issued | 2026-06 | |
| dc.description | This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), funding reference number DDG2024-00035. Cette recherche a été financée par le Conseil de recherches en sciences naturelles et en génie du Canada (CRSNG), numéro de référence DDG-2024-00035. We thank the Brazilian National Council for Scientific and Technological Development (CNPq). | |
| dc.description.abstract | In hydroelectric-based systems, effective energy generation planning relies heavily on precise forecasting of reservoir water levels. This paper proposes a novel hybrid forecasting framework that integrates multiple preprocessing strategies with a sparse Mixture of Experts enhanced Transformer architecture for short-term reservoir volume prediction. When evaluated on 19 interconnected reservoirs across two major river basins in southern Brazil using real operational data from the Brazilian National System Operator, the proposed model achieves a mean squared error of 0.062 and a mean absolute error of 0.145. Comprehensive benchmarking against 18 state-of-the-art deep learning methods demonstrates that the proposed approach significantly outperforms existing methods while maintaining computational efficiency through sparse expert routing. Our results confirm that combining diverse preprocessing strategies with conditional computation mechanisms provides superior forecasting accuracy for reservoir management in hydroelectric power systems. | eng |
| dc.identifier.citation | Seman, L. O., You, K. C., Stefenon, S. F. (2026). Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction. Electric Power Systems Research, 255, 1-13. https://doi.org/10.1016/j.epsr.2026.112754 | |
| dc.identifier.doi | 10.1016/j.epsr.2026.112754 | |
| dc.identifier.issn | 0378-7796 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/22585 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier BV | |
| dc.relation.hasversion | https://www.sciencedirect.com/science/article/pii/S0378779626000477?via%3Dihub | |
| dc.relation.ispartof | Electric Power Systems Research | |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Electrical power system | |
| dc.subject | Hydroelectric reservoir | |
| dc.subject | Transformer architecture | |
| dc.subject | Mixture-of-experts | |
| dc.title | Sparse mixture of experts enhanced transformer architecture for short-term hydroelectric reservoir volume prediction | eng |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 13 | |
| oaire.citation.startPage | 1 | |
| oaire.citation.title | Electric Power Systems Research | |
| oaire.citation.volume | 255 | |
| oaire.version | http://purl.org/coar/version/c_be7fb7dd8ff6fe43 |