2026-05-28T17:46:53Zhttps://riuma.uma.es/rest/oai/requestoai:riuma.uma.es:10630/187702026-02-03T12:29:03Zcom_10630_2254col_10630_37959
RIUMA. Repositorio Institucional de la Universidad de Málaga
author
Mora-López, Llanos
author
Sidrach-de-Cardona-Ortin, Mariano
2019-11-13T12:30:23Z
2019-11-13T12:30:23Z
2019-11-13
https://hdl.handle.net/10630/18770
The results obtained for the modeling and optimization of photovoltaic self-consumption facilities are presented. The study has been carried out for three Spanish cities with different climatic conditions. The self-consumption and self-sufficiency curves for different hourly consumption profiles have been obtained based on the installed peak power and the size of the battery. Different models of machine learning are proposed to predict these parameters. The input variables of these models are related to the configuration of the installation, its location and the type of consumption profile. The model with best predictions of self-sufficiency is Random Forest, which in cross-validation has a relative error of 5%. For the prediction of self-consumption, the model that performs best is the multilayer perceptron, with an average absolute error of 0.55 and an absolute relative error of 3%.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Energía fotovoltaica
Models for the Optimization and Evaluation of Photovoltaic Self-Consumption Facilities
conference output
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URL
https://riuma.uma.es/bitstreams/a8ce1e87-f2cb-4436-b087-db00b973afca/download
File
MD5
84fd10c5aa7d68e07ad0dfe8a6d7669b
318390
application/pdf
ISES_LM_MS_SWC2019.pdf