Please use this identifier to cite or link to this item:
|Title:||Computations of diffuse fraction of global irradiance: Part 2 – Neural Networks||Authors:||Tapakis, Rogiros
Charalambides, Alexandros G.
|Keywords:||Solar altitude;Clearness index;Diffuse fraction;Neural Networks;Solar irradiance;Time||Category:||Environmental Engineering||Field:||Engineering and Technology||Issue Date:||1-Dec-2016||Publisher:||Elsevier Ltd||Source:||Solar Energy,1 December 2016, Volume 139, Pages 723-732||metadata.dc.doi:||http://dx.doi.org/10.1016/j.solener.2015.12.042||Abstract:||Solar energy is the feedstock for various applications of renewable energy systems, thus, the necessity of calculating and using global tilted irradiance is acknowledged for the computations of the performance and monitoring of Photovoltaic (PV) Parks and other solar energy applications. Thus, the aim of our research is to develop a model for the correlation of diffuse fraction (kd) and the clearness index (kt), that can then be used for the evaluation of the diffuse irradiance given the global irradiance. In a companion paper, existing simple empirical models were reviewed and compared based on 10 years of data from Cyprus and then, analytical approaches for the computation of diffuse fraction were employed, where solar altitude was introduced as an additional parameter in the calculations. In the present paper, the same dataset was used, and three additional parameters were introduced to the calculations: global irradiance on the horizontal plane, extraterrestrial irradiance on the horizontal plane and the time of the day. These parameters were chosen due to the strong dependence of the diffuse fraction/clearness index correlation to the season/day of the year and time of day. Due to the non-linear influence of these parameters to the kt–kd correlations and the additional interaction between them, the employment of analytical methods is not applicable. Thus, non-parametric regression analysis was adopted, using supervised machine learning methodologies such as Artificial Neural Networks, which are able to learn the key information patterns from multivariate input. Comparing the non-parametric regression to the analytical models developed in the companion paper, it is shown herewith that the accuracy of the models was slightly improved. The statistical indicators MBE, RMSE and R2 of the best fit model were −4.69%, 21.54% and 0.90 respectively.||URI:||http://ktisis.cut.ac.cy/handle/10488/9017||ISSN:||0038-092X||Rights:||© 2016 Elsevier Ltd||Type:||Article|
|Appears in Collections:||Άρθρα/Articles|
Show full item record
Page view(s) 2075
checked on Dec 9, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.