Dataset for Machine Learning: Explicit All-Sky Image Features to Enhancing Indirect Solar Photovoltaic Forecasting
Description
Attention: This sample dataset will be updated after the review of the manuscript which was submitted for peer review. This sample dataset contains a set of measurements explicitly extracted from all-sky images by means of image processing. The measurements and global solar irradiance are shown for each all-sky image, at a temporal resolution of 1 minute, over three complete years (2014 to 2016). The dataset can be used as a benchmark and in studies of artificial intelligence methods applied to solar irradiance prediction and photovoltaic energy generation. The dataset will be detailed in the study entitled "Dataset for Machine Learning: Explicit All-Sky Image Features to Enhancing Indirect Solar Photovoltaic Forecasting", which was submitted for peer review. This dataset uses data derived from: Pedro, H. T. C., Larson, D. P., & Coimbra, C. F. M. (2019). A comprehensive dataset for the accelerated development and benchmarking of solar forecasting methods. Journal of Renewable and Sustainable Energy, 11(3), 036102. https://doi.org/10.1063/1.5094494. The dataset is related with the paper Maciel, J.N.; Ledesma, J.J.G.; Ando Junior, O.H. Hybrid Prediction Method of Solar Irradiance Applied to Short-Term Photovoltaic Energy Generation. Renewable and Sustainable Energy Reviews 2024, 192, 114185, doi:10.1016/j.rser.2023.114185.