Day-Ahead Photovoltaic Power Forecasting Using Deep Learning with an Autoencoder-Based Correction Strategy

Autor(es)

Cortez, Juan Carlos ; Lopez, Juan Camilo ; Ullon, Hernan R. ; Giesbrecht, Mateus ; Rider, Marcos J.

• 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
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• 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