On the role of lagged exogenous variables and spatio-temporal correlations in improving the accuracy of solar forecasting methods

Renewable Energy

Volumn 78, Issue , 2015, Pages 203-218

  Zagouras, Athanassios ^a   Pedro, Hugo T C ^a   Coimbra, Carlos F M ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Exogenous input data; Genetic algorithms; Gridded data; Solar forecasting; Time lagged correlation

Indexed keywords

GENETIC ALGORITHMS; INFORMATION MANAGEMENT; NEURAL NETWORKS; SATELLITES; SOLAR RADIATION; TIME SERIES;

CALIFORNIA IRRIGATION MANAGEMENT INFORMATION SYSTEMS; EXOGENOUS INPUT; EXOGENOUS VARIABLES; FORECAST PERFORMANCE; GRIDDED DATA; SOLAR FORECASTING; SPATIOTEMPORAL CORRELATION; SUPPORT VECTOR REGRESSION (SVR);

FORECASTING;

ACCURACY ASSESSMENT; FORECASTING METHOD; GENETIC ALGORITHM; INFORMATION SYSTEM; MICROCLIMATE; SOLAR RADIATION; SPATIOTEMPORAL ANALYSIS; TIME SERIES;

CALIFORNIA; UNITED STATES;

EID: 84921507512     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2014.12.071     Document Type: Article

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