An ANN-based approach for forecasting the power output of photovoltaic system

Procedia Environmental Sciences

Volumn 11, Issue PART C, 2011, Pages 1308-1315

  Ding, Ming ^a   Wang, Lei ^a   Bi, Rui ^a  

^a HEFEI UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

24 hour ahead forecasting; Artificial neural network; Improved back propagation learning algorithm; Photovoltaic system; Similar day selection algorithm

Indexed keywords

BACKPROPAGATION; BACKPROPAGATION ALGORITHMS; DEEP NEURAL NETWORKS; ENVIRONMENTAL ENGINEERING; FORECASTING; LEARNING ALGORITHMS; NEURAL NETWORKS; PHOTOVOLTAIC CELLS; THERMOELECTRIC POWER;

24-HOUR-AHEAD FORECASTING; BACKPROPAGATION LEARNING ALGORITHM; COMPLEX MODEL; FORECAST ACCURACY; IMPROVED BACK-PROPAGATION LEARNING ALGORITHMS; LARGE SCALE GRIDS; PHOTOVOLTAIC SYSTEMS; SIMILAR DAY SELECTION ALGORITHMS;

WEATHER FORECASTING;

EID: 84863138074     PISSN: None     EISSN: 18780296     Source Type: Conference Proceeding    
DOI: 10.1016/j.proenv.2011.12.196     Document Type: Conference Paper

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