Global model for short-term load forecasting using artificial neural networks

IEE Proceedings: Generation, Transmission and Distribution

Volumn 149, Issue 2, 2002, Pages 121-125

  Marin F J ^a   Garcia Lagos F ^a   Joya, G ^a   Sandoval, F ^a  

^a UNIVERSITY OF MÁLAGA   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BACKPROPAGATION; ELECTRIC POWER UTILIZATION; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; LINEAR SYSTEMS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; PARAMETER ESTIMATION; RECURSIVE FUNCTIONS; SELF ORGANIZING MAPS; STATISTICAL METHODS;

KOHONEN SELF-ORGANISING MAP; SELF-ORGANISING FEATURE MAP; SHORT-TERM LOAD FORECASTING;

ELECTRIC LOAD FORECASTING;

EID: 0036504851     PISSN: 13502360     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-gtd:20020224     Document Type: Article

References (9)

1. Application of the decomposition technique for forecasting the load of a large electric power network
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4. ANNSTLF - Artificial neural network short-term load forecaster - Generation three
5. Design of artificial neural networks for short-term load forecasting. Part I: Self-organizing feature maps for day type identification
6. Short-term peak load forecasting: Statistical methods versus artificial neural networks
7. Distributed representations, simple recurrent networks, and grammatically structure
8. Self-organized map
9. Neural networks for short-term load forecasting: A review and evaluation