@inproceedings{a149da3439144c59825ea1f84db7875c,
title = "Machine Learning for Predicting Photovoltaic Power Generation: an Application on a University Campus",
abstract = "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.",
keywords = "energy forecasting, machine learning, regression models, solar energy",
author = "Fabio Lopez and Markus Mock and Abraham Davila",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Latin American Computing Conference, CLEI 2023 ; Conference date: 16-10-2023 Through 20-10-2023",
year = "2023",
doi = "10.1109/CLEI60451.2023.10346189",
language = "English",
series = "Proceedings - 2023 49th Latin American Computing Conference, CLEI 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings - 2023 49th Latin American Computing Conference, CLEI 2023",
}