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A review on classification and comparison of different models in solar radiation estimation
Author(s) -
Tolabi Hajar Bagheri,
Moradi M.H.,
Ayob Shahrin Bin Md
Publication year - 2014
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3161
Subject(s) - empirical modelling , computer science , matlab , estimation , machine learning , software , data mining , novelty , artificial intelligence , algorithm , engineering , simulation , systems engineering , philosophy , theology , programming language , operating system
SUMMARY This paper introduces a new classification scheme for the solar radiation estimation techniques based on three categories: empirical models (based on statistical regression techniques), simulated models (based on training), and optimized models (based on optimization algorithms). For the optimized model category, a novelty bees algorithm estimation based on a linear empirical model is developed. Eight different methods from three classes have been tested on three sample geographic positions of Iran in order to compare the efficiency, complexity, sensed parameters, and required prior training of each category with others by implementing in the Matlab software. Among all tested models, the best properties are obtained for optimized empirical models by optimization algorithms. The main advantages of this model type are that it eliminates the training stage and therefore reduces the complexity rather than simulated models yet offers high accuracy estimation. Copyright © 2014 John Wiley & Sons, Ltd.