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Short‐term load forecasting based on support vector regression and load profiling
Author(s) -
Sousa João C.,
Jorge Humberto M.,
Neves Luís P.
Publication year - 2013
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3048
Subject(s) - support vector machine , simulated annealing , profiling (computer programming) , artificial neural network , electrical load , regression , computer science , regression analysis , data mining , machine learning , artificial intelligence , statistics , engineering , mathematics , voltage , electrical engineering , operating system
SUMMARY The article proposes a methodology to forecast the electric load for the 24 h of the following day based on support vector regression. The study considers 24 distinct models, one for each predicted hour, where each individual model is treated independently. Its objective is to find the optimal combination of support vector machine parameters that could generalize low forecasting errors, using simulated annealing as a metaheuristic. The adopted methodology is compared to concurrent methods based on neural networks when applied to a simulated load diagram (to illustrate a distribution feeder supplying a sample of 740 consumers). The results have proven its effectiveness with mean absolute percentage errors being less than 5% for testing samples. The study also focuses on evaluating the potential benefits of adopting load profiling information as input in support vector regression, giving a consistent proof of its importance. Copyright © 2013 John Wiley & Sons, Ltd.