Dataset for Machine Learning: Explicit All-Sky Image Features to Enhance Solar Irradiance Prediction

Joylan Nunes Maciel (), Jorge Javier Gimenez Ledesma and Oswaldo Hideo Ando Junior ()
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Joylan Nunes Maciel: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
Jorge Javier Gimenez Ledesma: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil
Oswaldo Hideo Ando Junior: Interdisciplinary Postgraduate Program in Energy & Sustainability (PPGIES), Federal University of Latin American Integration–UNILA, Paraná City 85867-000, Brazil

Data, 2024, vol. 9, issue 10, 1-12

Abstract: Prediction of solar irradiance is crucial for photovoltaic energy generation, as it helps mitigate intermittencies caused by atmospheric fluctuations such as clouds, wind, and temperature. Numerous studies have applied machine learning and deep learning techniques from artificial intelligence to address this challenge. Based on the recently proposed Hybrid Prediction Method (HPM), this paper presents an original and comprehensive dataset with nine attributes extracted from all-sky images developed using image processing techniques. This dataset and analysis of its attributes offer new avenues for research into solar irradiance forecasting. To ensure reproducibility, the data processing workflow and the standardized dataset have been meticulously detailed and made available to the scientific community to promote further research into prediction methods for photovoltaic energy generation.

Keywords: all-sky image features; solar irradiance prediction; image processing; photovoltaic energy forecasting (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2024
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