Open AccessEstimating missing data in historic series of global radiation through neural network algorithms
Author(s) -
Franklin García Acevedo,
Juan Rojas Serrano,
Alejandro Vásquez Vega,
Diego Parra Peñaranda,
Erney Castro Becerra
Publication year - 2016
Publication title -
sistemas and telematica
Language(s) - English
Resource type - Journals
ISSN - 1692-5238
DOI - 10.18046/syt.v14i37.2239
Subject(s) - autoregressive integrated moving average , artificial neural network , time series , missing data , series (stratigraphy) , interpolation (computer graphics) , backpropagation , computer science , algorithm , matlab , autoregressive model , data mining , machine learning , artificial intelligence , statistics , mathematics , geology , paleontology , motion (physics) , operating system
In data processing time series of meteorological data problems, you are incomplete in some time intervals; it addresses the issue commonly using the autoregressive integrated moving average (ARIMA) or the method by regression analysis (interpolation), both with certain limitations under particular conditions. This paper presents the results of an investigation aimed at solving the problem using neural networks reported. The analysis of a time series of global radiation obtained at the Francisco de Paula Santander University (UFPS) is presented, with basis in the recorded data by the weather station attached to the Department of Fluids and Thermals. Having a series of ten-year study for 125,658 records of temperature, radiation and energy with a percentage of 9.98 missing data, which were duly cleared and completed by a neural network using algorithms backpropagation in the mathematical software MATLAB