Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013-14 Solar Energy Prediction Contest

Energy

Volumn 78, Issue , 2014, Pages 247-256

  Aggarwal, S K ^a   Saini, L M ^b  

^a MAHARISHI MARKANDESHWAR DEEMED TO BE UNIVERSITY   (India)

^b NATIONAL INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial neural network; Ensemble learning; Least square regression; Regularization; Solar energy forecasting; Variable segmentation

Indexed keywords

ARTIFICIAL INTELLIGENCE; FORECASTING; INTERPOLATION; METEOROLOGY; NEURAL NETWORKS; REGRESSION ANALYSIS; SOLAR ENERGY; TREES (MATHEMATICS);

AMERICAN METEOROLOGICAL SOCIETIES; ANN (ARTIFICIAL NEURAL NETWORK); ENERGY FORECASTING; ENSEMBLE LEARNING; GENERALIZATION CAPABILITY; LEAST SQUARE REGRESSION; NUMERICAL WEATHER PREDICTION MODELS; REGULARIZATION;

WEATHER FORECASTING;

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; ENSEMBLE FORECASTING; GAUSSIAN METHOD; INTERPOLATION; METEOROLOGY; NUMERICAL MODEL; SOLAR POWER;

EID: 84920720385     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2014.10.012     Document Type: Article

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