A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants

Applied Energy

Volumn 108, Issue , 2013, Pages 392-401

  Massi Pavan, A ^a   Mellit, A ^b,c,f   De Pieri, D ^d   Kalogirou, S A ^e  

^a UNIVERSITY OF TRIESTE   (Italy)

^b UNIVERSITY OF JIJEL   (Algeria)

^c CENTRE DE DÉVELOPPEMENT DES ENERGIES RENOUVELABLES   (Algeria)

^d Suntrust Srl   (Italy)

^e CYPRUS UNIVERSITY OF TECHNOLOGY   (Cyprus)

^f ABDUS SALAM INTERNATIONAL CENTRE FOR THEORETICAL PHYSICS   (Italy)

Author keywords

Bayesian NN; Large scale photovoltaic plant; Maintenance; Pollution; Polynomial regression; Soiling

Indexed keywords

MAINTENANCE; NEURAL NETWORKS; PERSONNEL TESTING; POLLUTION; REGRESSION ANALYSIS;

BAYESIAN; BAYESIAN NEURAL NETWORKS; OPERATION AND MAINTENANCE; PHOTOVOLTAIC PLANT; POLYNOMIAL REGRESSION; REGRESSION POLYNOMIALS; SOILING; STANDARD TEST CONDITION (STC);

PHOTOVOLTAIC EFFECTS;

ARTIFICIAL NEURAL NETWORK; BAYESIAN ANALYSIS; CLEANUP; COMPARATIVE STUDY; COST-BENEFIT ANALYSIS; ENERGY RESOURCE; MAINTENANCE; NUMERICAL MODEL; PHOTOVOLTAIC SYSTEM; REGRESSION ANALYSIS; RENEWABLE RESOURCE; TESTING METHOD;

ITALY;

EID: 84876425421     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.03.023     Document Type: Article

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