scholarly journals Solar Radiation Prediction Using NARX Model

Author(s):  
Ines Sansa ◽  
Najiba Mrabet Bellaaj
Keyword(s):  
Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2576 ◽  
Author(s):  
Eduardo Rangel ◽  
Erasmo Cadenas ◽  
Rafael Campos-Amezcua ◽  
Jorge L. Tena

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 % .


2020 ◽  
Vol 3 (2) ◽  
Author(s):  
Olusola Samuel Ojo ◽  
Babatunde Adeyemi

In this paper, surface data meteorological were used as input variables to create, train and validate the network in which global solar radiation serves as a target. These surface data were obtained from the archives of the European centre for Medium-Range weather forecast for a span of 36 years (1980-2015) over Nigeria. The research aims to evaluate the predictive ability of the nonlinear autoregressive neural network with exogenous input (NARX) model compared with the multivariate linear regression (MLR) model using the statistical metrics. Model selection analysis using the index of agreement (dr) metric showed that the MLR and NARX models have values of 0.710 and 0.853 in the Sahel, 0.748 and 0.849 in the Guinea Savannah, 0.664 and 0.791 in the Derived Savannah, 0.634 and 0.824 in the Coastal regions, and 0.771 and 0.806 in entire Nigeria respectively. Meanwhile, error analyses of the models using root mean square errors (RMSE) showed the values of 1.720 W/m2 and 1.417 in the Sahel region, 2.329 W/m2 and 1.985 W/m2 in the Guinea Savannah region, 2.459 W/m2 and 2.272 W/m2 in the Derived Savannah region, 2.397 W/m2 and 2.261 W/m2 in the Coastal region and 1.691 W/m2 and 1.600 W/m2 in entire Nigeria for MLR and NARX models respectively. These showed that the NARX model has higher dr values and lower RMSE values over all the climatic regions and entire Nigeria than the MLR model. Finally, it can be inferred from these metrics that the NARX model gives a better prediction of global solar radiation than the traditional common MLR models in all the zones in Nigeria.


Space Weather ◽  
2006 ◽  
Vol 4 (6) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tracy Staedter
Keyword(s):  

2003 ◽  
Vol 107 ◽  
pp. 743-747
Author(s):  
D. R.S. Lean ◽  
SD. Siciliano
Keyword(s):  

2019 ◽  
pp. 53-65
Author(s):  
Renata Domingos ◽  
Emeli Guarda ◽  
Elaise Gabriel ◽  
João Sanches

In the last decades, many studies have shown ample evidence that the existence of trees and vegetation around buildings can contribute to reduce the demand for energy by cooling and heating. The use of green areas in the urban environment as an effective strategy in reducing the cooling load of buildings has attracted much attention, though there is a lack of quantitative actions to apply the general idea to a specific building or location. Due to the large-scale construction of high buildings, large amounts of solar radiation are reflected and stored in the canyons of the streets. This causes higher air temperature and surface temperature in city areas compared to the rural environment and, consequently, deteriorates the urban heat island effect. The constant high temperatures lead to more air conditioning demand time, which results in a significant increase in building energy consumption. In general, the shade of the trees reduces the building energy demand for air conditioning, reducing solar radiation on the walls and roofs. The increase of urban green spaces has been extensively accepted as effective in mitigating the effects of heat island and reducing energy use in buildings. However, by influencing temperatures, especially extreme, it is likely that trees also affect human health, an important economic variable of interest. Since human behavior has a major influence on maintaining environmental quality, today's urban problems such as air and water pollution, floods, excessive noise, cause serious damage to the physical and mental health of the population. By minimizing these problems, vegetation (especially trees) is generally known to provide a range of ecosystem services such as rainwater reduction, air pollution mitigation, noise reduction, etc. This study focuses on the functions of temperature regulation, improvement of external thermal comfort and cooling energy reduction, so it aims to evaluate the influence of trees on the energy consumption of a house in the mid-western Brazil, located at latitude 15 ° S, in the center of South America. The methodology adopted was computer simulation, analyzing two scenarios that deal with issues such as the influence of vegetation and tree shade on the energy consumption of a building. In this way, the methodological procedures were divided into three stages: climatic contextualization of the study region; definition of a basic dwelling, of the thermophysical properties; computational simulation for quantification of energy consumption for the four facade orientations. The results show that the façades orientated to north, east and south, without the insertion of arboreal shading, obtained higher values of annual energy consumption. With the adoption of shading, the facades obtained a consumption reduction of around 7,4%. It is concluded that shading vegetation can bring significant climatic contribution to the interior of built environments and, consequently, reduction in energy consumption, promoting improvements in the thermal comfort conditions of users.


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