Título: Deep Learning and Satellite Images for Photovoltaic Power Forecasting: A Case Study
Autores: Luiz Henrique Buzzi, Lucas Weihmann and Pablo Andretta Jaskowiak
Resumo: The growing demand for renewable energy resources presents a supply management challenge, as photovoltaic (PV) energy exhibits intermittent generation due to meteorological factors. The unpredictability of these variations leaves power grids vulnerable to instability, quality, and balance issues. In this context, accurate forecasting of PV power generation can improve management through generation planning, allowing for the balancing of different energy sources, which is crucial for achieving widespread PV energy adoption. The rapid development and significant advancements in deep learning present new possibilities for the use of satellite imagery in PV power forecasting. In this work we build and evaluate several deep learning models in the context of PV power forecasting, aiming at 30 and 60 minutes horizons. Our models are built for the prediction of the Global Horizontal Irradiance (GHI) component which, due to its strong correlation with PV power generation, can be employed not only to derive the actual PV plant output, but also as a measure generation potential, regardless of the actual PV plant. The models take as input images from the GOES-16 satellite and ground-based meteorological measurements, which are considered as desired outcomes. Several model configurations demonstrated the viability of GHI forecasting based on satellite imagery, with the best models achieving relative root mean squared errors (rRMSE) of 15.6% and 17.2% for 30-minute and 60-minute forecast horizons, respectively.
Palavras-chave: photovoltaic power forecasting, deep learning, convolutional neural network, satellite image, GOES-16
Páginas: 8
Código DOI: 10.21528/CBIC2023-120
Artigo em pdf: CBIC_2023_paper120.pdf
Arquivo BibTeX: CBIC_2023_120.bib