Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/31271
Autoria: | Martins, A. A. A. F. Lagarto, J. Canacsinh, H. Reis, F. Cardoso, M. G. M. S. |
Data: | 2022 |
Título próprio: | Short‑term load forecasting using time series clustering |
Título da revista: | Optimization and Engineering |
Volume: | 23 |
Número: | 4 |
Paginação: | 2293 - 2314 |
Referência bibliográfica: | Martins, A. A. A. F., Lagarto, J., Canacsinh, H., Reis, F., & Cardoso, M. G. M. S. (2022). Short‑term load forecasting using time series clustering. Optimization and Engineering, 23(4), 2293-2314. https://dx.doi.org/10.1007/s11081-022-09760-1 |
ISSN: | 1389-4420 |
DOI (Digital Object Identifier): | 10.1007/s11081-022-09760-1 |
Palavras-chave: | Clustering time series Distance measures Load pattern Sequence pattern Similar pattern method Short-term load forecasting |
Resumo: | Short-term load forecasting plays a major role in energy planning. Its accuracy has a direct impact on the way power systems are operated and managed. We propose a new Clustering-based Similar Pattern Forecasting algorithm (CSPF) for short-term load forecasting. It resorts to a K-Medoids clustering algorithm to identify load patterns and to the COMB distance to capture differences between time series. Clusters’ labels are then used to identify similar sequences of days. Temperature information is also considered in the day-ahead load forecasting, resorting to the K-Nearest Neighbor approach. CSPF algorithm is intended to provide the aggregate forecast of Portugal’s national load, for the next day, with a 15-min discretization, based on data from the Portuguese Transport Network Operator (TSO). CSPF forecasting performance, as evaluated by RMSE, MAE and MAPE metrics, outperforms three alternative/baseline methods, suggesting that the proposed approach is promising in similar applications. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | BRU-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_90070.pdf | 788,32 kB | Adobe PDF | Ver/Abrir |
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