Machine Learning for Predicting Photovoltaic Power Generation: an Application on a University Campus

Fabio Lopez, Markus Mock, Abraham Davila

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

This paper describes the application of machine learning in an energy management system set up to monitor the use and generation of energy at a university campus. We explain how we built precise forecasting models for producing photovoltaic energy produced by solar panels at the University of Applied Sciences Landshut, Germany. We describe the practical challenges of dealing with data dropout and erroneous data when working with data from an actual in-production setting outside a simple lab scenario. Applying several data cleaning methods based on statistical methods, we obtained models that allow us to predict electrical energy produced by the solar panels with a precision of 0.97 as measured by the R2 metric. More importantly, the process described to arrive at this result, more than the result per se, serves as a real-world example of applying data science and machine learning methodology in an in-production setting.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2023 49th Latin American Computing Conference, CLEI 2023
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9798350318876
DOI
EstadoPublicada - 2023
Evento49th Latin American Computing Conference, CLEI 2023 - La Paz, Estado Plurinacional de Bolivia
Duración: 16 oct. 202320 oct. 2023

Serie de la publicación

NombreProceedings - 2023 49th Latin American Computing Conference, CLEI 2023

Conferencia

Conferencia49th Latin American Computing Conference, CLEI 2023
País/TerritorioEstado Plurinacional de Bolivia
CiudadLa Paz
Período16/10/2320/10/23

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