Autor(es)

Cortez, Juan Carlos ; Terada, Lucas Zenichi ; Barros Bandeira, Bruno Vitor ; Soares, Joao ; Vale, Zita ; Rider, Marcos J.

• Tipo de documento: Artigo Científico
• Fonte: 2023 15TH SEMINAR ON POWER ELECTRONICS AND CONTROL, SEPOC; v. N/A, p. 6-pg., 2023-01-01.

Resumo

Photovoltaic (PV) forecasting is required to optimize grid management and boost renewable energy integration. This paper presents a comparative analysis of three forecast models, autoregressive integrated moving average (ARIMA), eXtreme Gradient Boosting (XGBoost), and long short-term memory (LSTM) for very short-term PV forecasting applied to two real datasets. The main objective is to evaluate the complexity, performance, and accuracy of these algorithms in predicting very short-term PV power based only on the historical PV time series data. Furthermore, an additional study incorporates weather and calendar features for the LSTM model to examine their impact on forecasting quality. The performance of the models is evaluated in terms of root mean squared error (RMSE) and mean absolute error (MAE). Feature selection and hyperparameter optimization are also considered. The results highlight the strengths and limitations of each forecasting model in accurately capturing very short-term PV variations and show that XGBoost had the most accurate forecasting metrics for both datasets for univariate models. The research outcomes provide valuable insights for energy system operators, grid managers, and renewable energy stakeholders to make informed decisions regarding grid stability and efficient integration of PV generation. (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: 23/00297-5 - Previsão de Energia Fotovoltaica usando Métodos de Aprendizado de Máquina
• Beneficiário: Bruno Vitor Barros Bandeira
• Modalidade de apoio: Bolsas no Brasil - Iniciação Científica
• Processo FAPESP: 22/09171-1 - Desenvolvimento de modelo preditivo para recarga inteligente de veículos elétricos baseado em dados na nuvem
• Beneficiário: Lucas Zenichi Terada
• Modalidade de apoio: Bolsas no Exterior - Estágio de Pesquisa - Mestrado
• Processo FAPESP: 20/13002-5 - Algoritmo de Recarga Inteligente de Veículos Elétricos Considerando a Integração de Recursos Elétricos Distribuídos: Microsserviço para Plataformas de Eletromobilidade IoT
• Beneficiário: Lucas Zenichi Terada
• Modalidade de apoio: Bolsas no Brasil - Mestrado