| Texto completo | |
| Autor(es): |
Cortez, Juan Carlos
;
Lopez, Juan Camilo
;
Ullon, Hernan R.
;
Giesbrecht, Mateus
;
Rider, Marcos J.
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS; v. 35, n. 4, p. 15-pg., 2024-06-07. |
| Resumo | |
Accurate forecasting is crucial for successfully integrating photovoltaic (PV) power plants into electrical grids and microgrids. Accordingly, this work presents a hybrid methodology for day-ahead PV power forecasting (PPF). It begins by examining three deep learning (DL) techniques, long short-term memory (LSTM), gated recurrent unit (GRU), and multilayer perceptron (MLP), as potential forecasting models. To find a robust forecasting model, feature selection is employed to select the most relevant input features, and additionally, hyperparameter optimization is performed using the Chu-Beasley genetic algorithm to automatically set the hyperparameters for each technique. An initial day-ahead PPF is computed recursively after selecting the optimal forecasting model. Subsequently, this initial forecast is refined using a long-short-term memory autoencoder (LSTM-AE) that corrects the initial PPF. To further enhance the interpretability of the final forecast, the k-means algorithm, incorporating a soft-dynamic time warping (DTW) metric, is utilized. The efficacy of the methodology is validated using real data from a solar farm at the State University of Campinas (UNICAMP) in Brazil. Empirical results demonstrate that the proposed methodology improves the forecast accuracy by more than 3.5% when LSTM-AE is applied for correction compared to state-of-the-art models. (AU) | |
| Processo FAPESP: | 21/11380-5 - CPTEn - Centro Paulista de Estudos da Transição Energética |
| Beneficiário: | Luiz Carlos Pereira da Silva |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Ciência para o Desenvolvimento |
| Processo FAPESP: | 19/01906-0 - Restauração ótima distribuída de sistema de distribuição de energia elétrica utilizando o algoritmo da direção alternada de multiplicadores de Lagrange |
| Beneficiário: | Juan Camilo Lopez Amezquita |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 21/11310-7 - Aplicação de técnicas heurísticas para o tratamento de variáveis binárias no problema de restauração ótima |
| Beneficiário: | Juan Camilo Lopez Amezquita |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Pós-Doutorado |