A hybrid forecasting approach applied in the electrical power system based on data preprocessing, optimization and artificial intelligence algorithms

Applied Mathematical Modelling

Volumn 40, Issue 23-24, 2016, Pages 10631-10649

  Jiang, Ping ^a   Ma, Xuejiao ^a  

^a DONGBEI UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

Electrical power system; Fast ensemble empirical mode decomposition; Forecasting validity degree; Improved simulated annealing; Modified particle swarm optimization; Time series forecasting

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIG DATA; DATA HANDLING; ELECTRIC POWER SYSTEMS; OPTIMIZATION; PARTICLE SWARM OPTIMIZATION (PSO); SIMULATED ANNEALING; STOCHASTIC SYSTEMS; TIME SERIES; WIND;

ARTIFICIAL INTELLIGENCE ALGORITHMS; ELECTRICAL POWER SYSTEM; ENSEMBLE EMPIRICAL MODE DECOMPOSITION; FORECASTING PERFORMANCE; HIGH RATE OF CONVERGENCE; MODIFIED PARTICLE SWARM OPTIMIZATION; OPTIMIZATION ALGORITHMS; TIME SERIES FORECASTING;

FORECASTING;

EID: 84994716124     PISSN: 0307904X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apm.2016.08.001     Document Type: Article

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