Enhanced Prediction of Solar Radiation Using NARX Models with Corrected Input Vectors

Eduardo Rangel, Erasmo Cadenas, Rafael Campos-Amezcua and Jorge L. Tena
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Eduardo Rangel: División de Estudios de Posgrado de la Facultad de Ingeniería Mecánica, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, Col. Felicitas del Río, Morelia 58040, Mexico
Erasmo Cadenas: División de Estudios de Posgrado de la Facultad de Ingeniería Mecánica, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, Col. Felicitas del Río, Morelia 58040, Mexico
Rafael Campos-Amezcua: Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca 62490, Mexico
Jorge L. Tena: División de Estudios de Posgrado de la Facultad de Ingeniería Mecánica, Universidad Michoacana de San Nicolás de Hidalgo, Gral. Francisco J. Múgica S/N, Col. Felicitas del Río, Morelia 58040, Mexico

Energies, 2020, vol. 13, issue 10, 1-22

Abstract: The main objective of this work is to analyze and configure appropriately the input vectors to enhance the performance of NARX models to forecast solar radiation one hour ahead. For this study, Engle–Granger causality tests were implemented. Additionally, collinearity among the meteorological variables of the databases was examined. Different databases were used to test the contribution of these analyses in the improvement of the input vectors. For that, databases from three cities of Mexico with different climates were obtained, namely: Chihuahua, Temixco, and Zacatecas. These databases consisted of hourly measurements of the following variables: solar radiation (SR), wind speed (WS), relative humidity (RH), pressure (P), and temperature (T). Results showed that, in all three cases, proper NARX models were produced even when using input vectors formed only with solar radiation and temperature data. Consequently, it was inferred that pressure, wind speed, and relative humidity could be excluded from the input vectors of the forecasting models since, according to the causality tests, they did not provide relevant information to improve the solar radiation forecast in the studied cases. Conversely, these variables could generate spurious results. Forecasting results obtained with the NARX model were compared to the smart persistence model, commonly used to validate SR prediction. Error measures, such as mean absolute error (MAE) and root mean squared error (RMSE), were used to compare prediction results obtained from different models. In all cases, results obtained from the enhanced NARX model surpassed the results of the smart persistence, namely: in Chihuahua up to 11.5 % , in Temixco up to 15.7 % , and in Zacatecas up to 27.2 % .

Keywords: NARX model; collinearity tests; Engle–Granger causality technique; solar radiation forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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