Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data

Neurocomputing

Volumn 211, Issue , 2016, Pages 72-83

  Malvoni, M ^a   De Giorgi, M G ^a   Congedo, P M ^a  

^a UNIVERSITY OF SALENTO   (Italy)

Author keywords

Dimensionality reduction; Least squares support vector machines; Photovoltaic forecast; Principal component analysis; Quadratic Renyi entropy

Indexed keywords

CLIMATE MODELS; CLUSTERING ALGORITHMS; FORECASTING; PHOTOVOLTAIC CELLS; PRINCIPAL COMPONENT ANALYSIS; SUPPORT VECTOR MACHINES;

DIMENSIONALITY REDUCTION; FORECASTING ACCURACY; HIGH DIMENSIONAL DATA; LEAST SQUARES SUPPORT VECTOR MACHINES; MEDITERRANEAN CLIMATES; PHOTOVOLTAIC; PREDICTED PERFORMANCE; RENYI ENTROPY;

DATA REDUCTION;

ALGORITHM; ARTICLE; CLIMATE; ENTROPY; FORECASTING; INPUT VECTOR; LEAST SQUARES SUPPORT VECTOR MACHINE; MATHEMATICAL ANALYSIS; MATHEMATICAL PARAMETERS; MEASUREMENT ACCURACY; PHOTOVOLTAIC POWER FORECASTING; PREDICTION; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; SEASONAL VARIATION; SUPPORT VECTOR MACHINE;

EID: 84991396324     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2016.01.104     Document Type: Article

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