2013 1st International Conference on Artificial Intelligence, Modelling and Simulation 2013
DOI: 10.1109/aims.2013.11
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Short Term Load Forecasting Using a Neural Network Based Time Series Approach

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Cited by 14 publications
(7 citation statements)
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“…Recently, a number of studies have gone beyond traditional statistical forecasting schemes (e.g. as in [3], [4]) and adopt some of the benefits offered by machine learning (ML)oriented techniques for STLF. Mainly, the direction towards ML solutions is seen as beneficial due to the ability of such approaches to model underlying patterns among several linear and non-linear data features gathered by heterogeneous inputs.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Recently, a number of studies have gone beyond traditional statistical forecasting schemes (e.g. as in [3], [4]) and adopt some of the benefits offered by machine learning (ML)oriented techniques for STLF. Mainly, the direction towards ML solutions is seen as beneficial due to the ability of such approaches to model underlying patterns among several linear and non-linear data features gathered by heterogeneous inputs.…”
Section: Background and Related Workmentioning
confidence: 99%
“…Additionally, for testing accuracy rate of model mean absolute percentage error (MAPE) is utilized. MAPE is the better measure performance evaluation [13]. There are various factors which affects load forecasting i.e.…”
Section: Table-1 Various Hybrid Forecasting Approaches Forecasting Momentioning
confidence: 99%
“…Other comparative studies, considering different ANNs, have been presented in the specialized literature with this emphasis, e.g., in [34][35][36][37]. The results from the comparative studies show that the ANN model is a superior method for load forecasting, owing to its ability to handle load data and its lower MAPE.…”
Section: Wherementioning
confidence: 99%