Long-term load forecasting based on adaptive neural fuzzy inference system using real energy data

Energy Procedia

Volumn 14, Issue , 2012, Pages 794-799

  Akdemir, Bayram ^a   Çetinkaya, Nurettin ^a  

^a SELCUK UNIVERSITY   (Turkey)

Author keywords

Adaptive neural fuzzy inference system; Long term forecasting; Mean absolute error; Mean absolute error percentage; Real data set

Indexed keywords

ADAPTIVE NEURAL FUZZY INFERENCE SYSTEM (ANFIS); ARTIFICIAL INTELLIGENCE METHODS; DATA SETS; ECONOMICAL PARAMETERS; ENERGY DATA; ENERGY DEMAND FORECASTING; ENERGY DEMANDS; ENERGY PLANT; ENERGY PRODUCTIONS; ENGINEERING PROBLEMS; ERROR LEVELS; FORECASTING MODELS; FUTURE ENERGIES; LONG-TERM FORECASTING; LONG-TERM LOAD FORECASTING; MATHEMATICAL FORMULAS; MATHEMATICAL METHOD; MEAN ABSOLUTE ERROR; MEAN ABSOLUTE PERCENTAGE ERROR; NUMERICAL INPUTS; PRACTICAL METHOD; REGRESSION MODEL; STATISTICAL MODELS;

ARTIFICIAL INTELLIGENCE; DEVELOPING COUNTRIES; ELECTRIC LOAD FORECASTING; ENERGY UTILIZATION; FORECASTING; FUZZY SYSTEMS; MATHEMATICAL MODELS; POPULATION STATISTICS; PROFILOMETRY; REGRESSION ANALYSIS;

INVESTMENTS;

EID: 84858384979     PISSN: 18766102     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1016/j.egypro.2011.12.1013     Document Type: Conference Paper

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