A computational intelligence scheme for the prediction of the daily peak load

Applied Soft Computing Journal

Volumn 11, Issue 8, 2011, Pages 4773-4788

  Nagi, Jawad ^a   Yap, Keem Siah ^b   Nagi, Farrukh ^b   Tiong, Sieh Kiong ^b   Ahmed, Syed Khaleel ^b  

^a DALLE MOLLE INSTITUTE FOR ARTIFICIAL INTELLIGENCE IDSIA   (Switzerland)

^b Universiti Tenaga Nasional   (Malaysia)

Author keywords

Computational intelligence; Daily peak load; Mid term load forecasting; Self organizing map; Support vector machine

Indexed keywords

DAILY PEAK LOAD; DATA SETS; ELECTRICITY DEMANDS; ELECTRICITY LOAD; ELECTRICITY PRODUCTION; EUROPEAN NETWORKS; INTELLIGENT TECHNOLOGY; KOHONEN RULES; LOAD DATA; LOAD FORECASTING; LOAD FORECASTING MODEL; MACHINE LEARNING TECHNIQUES; MALAYSIA; MALAYSIANS; POWER INDUSTRY; POWER LOAD; POWER SYSTEM OPERATIONS; PREDICTION ACCURACY; RESEARCH AREAS; SELF ORGANIZING; SIMILARITY DEGREE; SUPPORT VECTOR; SUPPORT VECTOR REGRESSIONS; TESTING DATA; TRAINING DATA; UNITED STATES OF AMERICA;

ARTIFICIAL INTELLIGENCE; CONFORMAL MAPPING; DECISION MAKING; ELECTRIC LOADS; ELECTRICITY; FORECASTING; LOAD TESTING; PRIVATIZATION; SUPPORT VECTOR MACHINES; TIME SERIES;

ELECTRIC LOAD FORECASTING;

EID: 81155159719     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2011.07.005     Document Type: Article

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