Artificial neural networks and regression approaches comparison for forecasting Iran's annual electricity load

POWERENG 2009 - 2nd International Conference on Power Engineering, Energy and Electrical Drives Proceedings

Volumn , Issue , 2009, Pages 675-679

  Ghanbari, A ^a   Naghavi, A ^a   Ghaderi, S F ^a   Sabaghian, M ^a  

^a UNIVERSITY OF TEHRAN   (Iran)

Author keywords

Artificial neural networks (ANN); Data preprocessing; Electrical load forecasting; Linear regression; Log linear regression

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS (ANN); CONVENTIONAL APPROACH; DATA PREPROCESSING; DATA PREPROCESSING TECHNIQUE; ECONOMICAL PARAMETERS; ELECTRICAL LOAD; ELECTRICAL LOAD FORECASTING; ELECTRICITY LOAD; FORECASTING ACCURACY; FORECASTING CAPABILITY; LOG-LINEAR REGRESSION; LONG TERM; LONG-TERM LOAD FORECASTING; MEAN ABSOLUTE ERROR; MEAN ABSOLUTE PERCENTAGE ERROR; MEDIUM TERM; POWER SYSTEMS; PRE-PROCESSED DATA; ROOT MEAN SQUARE ERRORS; SHORT TERM; STATISTICAL EVALUATION; TIME INTERVAL;

BACKPROPAGATION; ELECTRIC LOADS; FUNCTION EVALUATION; LINEAR REGRESSION; NEURAL NETWORKS;

ELECTRIC LOAD FORECASTING;

EID: 70349324800     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/POWERENG.2009.4915245     Document Type: Conference Paper

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