Next Article in Journal
Impact of an Energy Monitoring System on the Energy Efficiency of an Automobile Factory: A Case Study
Previous Article in Journal
Operational Profile Based Optimization Method for Maritime Diesel Engines
Previous Article in Special Issue
Exploring the Potentials of Artificial Neural Network Trained with Differential Evolution for Estimating Global Solar Radiation
Open AccessArticle

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

1
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
2
Tecnológico Nacional de México/Centro Nacional de Investigación y Desarrollo Tecnológico, Interior Internado Palmira S/N, Col. Palmira, Cuernavaca 62490, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Energies 2020, 13(10), 2576; https://doi.org/10.3390/en13102576
Received: 23 March 2020 / Revised: 14 May 2020 / Accepted: 14 May 2020 / Published: 19 May 2020
(This article belongs to the Special Issue Solar and Wind Power and Energy Forecasting)
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 % . View Full-Text
Keywords: NARX model; collinearity tests; Engle–Granger causality technique; solar radiation forecasting NARX model; collinearity tests; Engle–Granger causality technique; solar radiation forecasting
Show Figures

Figure 1

MDPI and ACS Style

Rangel, E.; Cadenas, E.; Campos-Amezcua, R.; Tena, J.L. Enhanced Prediction of Solar Radiation Using NARX Models with Corrected Input Vectors. Energies 2020, 13, 2576.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop