Title :
A SOM neural network approach to load forecasting. Meteorological and time frame influence
Author :
López, M. ; Valero, S. ; Senabre, C. ; Aparicio, J.
Author_Institution :
Dipt. de Ing. de Sist. Ind., Univ. Miguel Hernandez de Elche (UMH), Elche, Spain
Abstract :
An artificial neural network based on Kohonen self-organizing maps (SOM) and its application to short-term load forecasting (STLF) is presented. The proposed model is capable of forecasting up to 24 hour long profiles, up to 24 hours ahead of the beginning of the period. The input used by the model depends on the available information at the time of the forecast, and it may contain meteorological variables and previous hourly load values. Also, different time frames for the input training data are analyzed. The output of the model is a curve of the forecasted load for the specified period. The test of forecasting 2009 data from the Spanish power system resulted in a 2.67% MAPE (mean absolute percentage error).
Keywords :
load forecasting; self-organising feature maps; Kohonen self-organizing maps; SOM neural network; artificial neural network; forecasted load; mean absolute percentage error; short-term load forecasting; Forecasting; Load forecasting; Load modeling; Predictive models; Training; Training data; Weather forecasting;
Conference_Titel :
Power Engineering, Energy and Electrical Drives (POWERENG), 2011 International Conference on
Conference_Location :
Malaga
Print_ISBN :
978-1-4244-9845-1
Electronic_ISBN :
2155-5516
DOI :
10.1109/PowerEng.2011.6036553
