Electricity consumption is growing rapidly worldwide. Renewable
energy resources, such as solar energy, play a crucial role in this scenario,
contributing to satisfy demand sustainability. Although the share of
Photovoltaic (PV) power generation has increased in the past years, PV
systems are quite sensitive to climatic and meteorological conditions, leading
to undesirable power production variability. In order to improve energy grid
stability, reliability, and management, accurate forecasting models that relate
operational conditions to power output are needed. In this work we evaluate
the performance of regression methods applied to forecast short term (next
day) energy production of a PV Plant. Specifically, we consider five regression
methods and different configurations of feature sets. Our results suggest that
MLP and SVR provide the best forecasting results, in general. Also, although
features based on different solar irradiance levels play a key role in predicting
power generation, the use of additional features can improve prediction results.

Comparative Study of Photovoltaic Power Forecasting Methods

ENIAC: Technical Session 4

technical paper

The 9th Brazilian Conference on Intelligent Systems (BRACIS), the 16h Encontro Nacional de Inteligência Artificial e Computacional (ENIAC), and the 12th Concurso de Teses e Dissertações em Inteligência Artificial e Computacional (CTDIAC) was held from 20 to 23 October, 2020.

BRACIS originated from the combination of the two most important scientific events in Brazil in Artificial Intelligence (AI) and Computational Intelligence (CI): the Brazilian Symposium on Artificial Intelligence - SBIA (22 editions), and the Brazilian Symposium on Neural Networks - SBRN (13 editions). ENIAC is a national forum for researchers, practitioners, educators and students to present and discuss innovations, trends, experiences, developments and work in progress in the fields of Artificial and Computational Intelligence. CTDIAC is a satellite event whose goals are to disseminate and award the best dissertations and theses (MSc and PhDs concluded in Brazilian universities from June 2018 to May 2020) in Artificial and Computational Intelligence areas.

In this year, the Symposium on Knowledge Discovery, Mining and Learning (KDMiLe) will be held in conjunction with BRACIS 2020. This sysposium aims at integrating researchers, practitioners, developers, students and users to present their research results, to discuss ideas, and to exchange techniques, tools, and practical experiences – related to the Data Mining and Machine Learning areas.

Angelo Abdallah Pelisson

1

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Comparative Study of Photovoltaic Power Forecasting Methods