Transfer learning for short-term wind speed prediction with deep neural networks

Renewable Energy

Volumn 85, Issue , 2016, Pages 83-95

  Hu, Qinghua ^a   Zhang, Rujia ^a   Zhou, Yucan ^a  

^a TIANJIN UNIVERSITY   (China)

Author keywords

Deep neural networks; Stacked denoising autoencoder; Transfer learning; Wind speed prediction

Indexed keywords

DATA MINING; DEEP NEURAL NETWORKS; ELECTRIC UTILITIES; NEURAL NETWORKS; PREDICTIVE ANALYTICS; SPEED; TRANSFER LEARNING; WEATHER FORECASTING; WIND; WIND POWER;

AUTO ENCODERS; FORECASTING MODELING; LARGE-SCALE WIND POWER; NUMERICAL WEATHER PREDICTION; PREDICTION ERRORS; RENEWABLE ENERGY SOURCE; SHORT-TERM WIND SPEED PREDICTIONS; WIND SPEED PREDICTION;

DEEP LEARNING;

ACCURACY ASSESSMENT; ARTIFICIAL NEURAL NETWORK; DATA SET; ERROR ANALYSIS; EXPERIMENTAL STUDY; FORECASTING METHOD; MAPPING METHOD; NUMERICAL MODEL; PARADIGM SHIFT; PREDICTION; RENEWABLE RESOURCE; TECHNOLOGICAL CHANGE; WIND FARM; WIND POWER; WIND VELOCITY;

EID: 84934887601     PISSN: 09601481     EISSN: 18790682     Source Type: Journal    
DOI: 10.1016/j.renene.2015.06.034     Document Type: Article

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