Artificial neural networks for the performance prediction of large solar systems

Renewable Energy

Volumn 63, Issue , 2014, Pages 90-97

  Kalogirou, S A ^a   Mathioulakis, E ^b   Belessiotis, V ^b  

^a CYPRUS UNIVERSITY OF TECHNOLOGY   (Cyprus)

^b INSTITUTE OF NANOSCIENCE AND NANOTECHNOLOGY   (Greece)

Author keywords

Artificial neural networks; Daily energy output; Large solar thermal systems; Maximum temperature of storage tank

Indexed keywords

ENERGY OUTPUT; ENERGY PERFORMANCE; MULTIPLE LINEAR REGRESSIONS; PERFORMANCE PARAMETERS; PERFORMANCE PREDICTION; SOLAR THERMAL SYSTEMS; STORAGE TANK; TYPICAL OPERATING CONDITIONS;

DIGITAL STORAGE; FORECASTING; LINEAR REGRESSION; NEURAL NETWORKS; TANKS (CONTAINERS);

SOLAR SYSTEM;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DATA ACQUISITION; ENERGY EFFICIENCY; EXPERIMENTAL STUDY; PERFORMANCE ASSESSMENT; REGRESSION ANALYSIS; SOLAR POWER; SOLAR RADIATION; STATISTICAL ANALYSIS; STORAGE TANK; DATA SET; DIURNAL VARIATION; ENERGY DISSIPATION; MODEL VALIDATION; PREDICTION; SOLAR SYSTEM; TEMPERATURE PROFILE;

EID: 84884571934     PISSN: 09601481     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.renene.2013.08.049     Document Type: Article

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