Please use this identifier to cite or link to this item:
|Title:||Autocorrelation based weighing strategy for short-term load forecasting with the self-organizing map||Authors:||Yadav, V.
Time series prediction
|Issue Date:||2010||Citation:||Yadav, V., Srinivasan, D. (2010). Autocorrelation based weighing strategy for short-term load forecasting with the self-organizing map. 2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010 1 : 186-192. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCAE.2010.5451972||Abstract:||In this paper, we introduce a load forecasting method for short-term load forecasting which is based on a two-stage hybrid network with weighted self-organizing maps (SOM) and autoregressive (AR) model. In the first stage, a weighted SOM network is applied to split the past dynamics into several clusters in an unsupervised manner. Then in the second stage, a local linear AR model is associated with each cluster to fit its training data in a supervised way. Though this method can be used for forecasting any time series, it is best suited for processes which are non-linear and non-stationary and show cluster effects, such as the electricity load time series. Data of the electricity demand from Britain and Wales is used to verify the effectiveness of the learning and prediction of the proposed method. ©2010 IEEE.||Source Title:||2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010||URI:||http://scholarbank.nus.edu.sg/handle/10635/69465||ISBN:||9781424455850||DOI:||10.1109/ICCAE.2010.5451972|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 14, 2020
WEB OF SCIENCETM
checked on Feb 6, 2020
checked on Feb 17, 2020
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.