Open AccessComparative Study of Photovoltaic Power Forecasting Methods
Author(s) -
Angelo Pelisson,
Thiago Ferreira Covões,
Anderson Wedderhoff Spengler,
Pablo Andretta Jaskowiak
Publication year - 2020
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/eniac.2020.12159
Subject(s) - photovoltaic system , renewable energy , computer science , electricity generation , grid parity , reliability (semiconductor) , solar power , production (economics) , reliability engineering , solar irradiance , solar energy , probabilistic forecasting , distributed generation , environmental science , power (physics) , meteorology , engineering , artificial intelligence , economics , quantum mechanics , probabilistic logic , electrical engineering , macroeconomics , physics
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.