Abstract
Solar energy is currently among the most important and convenient renewable sources, with a great potential to reduce the use of fossil fuels. However, power generation from solar panels is very irregular and highly dependent on weather conditions. Therefore, solar irradiance forecasting is a fundamental task to ensure an efficient power management. In power plants, besides the temporal observations, it is essential to consider the spatial relationship between close photovoltaic panels. In this work, we study the importance of feature selection for forecasting solar irradiance time series using spatio-temporal data. The experimental study considers nine feature selection techniques and compares the predictive performance of four regression algorithms using the different subsets of features. The data used comes from two different locations in Canada with multiple solar panels. The results demonstrate that including the proper spatial information using feature selection, particularly the methods based on evolutionary computation, enhances significantly the forecasting accuracy.
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Funding
This research has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación/Proyecto TIN2017-88209-C2 and by the Andalusian Regional Government under the projects: BIDASGRI: Big Data technologies for Smart Grids (US-1263341), Adaptive hybrid models to predict solar and wind renewable energy production (P18-RT-2778).
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Carranza-García, M., Lara-Benítez, P., Luna-Romera, J.M., Riquelme, J.C. (2022). Feature Selection on Spatio-Temporal Data for Solar Irradiance Forecasting. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_62
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DOI: https://doi.org/10.1007/978-3-030-87869-6_62
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