Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition

Energy

Volumn 165, Issue , 2018, Pages 1220-1227

  Li, Chuan ^a,b   Tao, Ying ^a   Ao, Wengang ^a   Yang, Shuai ^a   Bai, Yun ^a  

^a CHONGQING TECHNOLOGY AND BUSINESS UNIVERSITY   (China)

^b UNIVERSITY OF ALGARVE   (Portugal)

Author keywords

Electricity consumption; Ensemble empirical mode decomposition; Fast Fourier transformation; Forecast; Random forest

Indexed keywords

BACKPROPAGATION; DECISION TREES; ELECTRIC POWER UTILIZATION; ERRORS; FOURIER SERIES; FREQUENCY DOMAIN ANALYSIS; MARKETING; MEAN SQUARE ERROR; NEURAL NETWORKS; SIGNAL PROCESSING;

BACK-PROPAGATION NEURAL NETWORKS; ELECTRICITY-CONSUMPTION; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; ENSEMBLE EMPIRICAL MODE DECOMPOSITIONS (EEMD); FAST FOURIER TRANSFORMATIONS; LEAST SQUARES SUPPORT VECTOR REGRESSION; MEAN ABSOLUTE PERCENTAGE ERROR; RANDOM FORESTS;

FORECASTING;

ALGORITHM; DECOMPOSITION ANALYSIS; DEMAND-SIDE MANAGEMENT; ELECTRICITY; EMPIRICAL ANALYSIS; ENERGY MARKET; ENERGY PLANNING; ENERGY USE; FORECASTING METHOD; MARKETING; NUMERICAL METHOD;

CHINA; JIANGSU;

EID: 85055974537     PISSN: 03605442     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.energy.2018.10.113     Document Type: Article

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