Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICCAE.2010.5451972
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dc.titleAutocorrelation based weighing strategy for short-term load forecasting with the self-organizing map
dc.contributor.authorYadav, V.
dc.contributor.authorSrinivasan, D.
dc.date.accessioned2014-06-19T03:01:02Z
dc.date.available2014-06-19T03:01:02Z
dc.date.issued2010
dc.identifier.citationYadav, 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
dc.identifier.isbn9781424455850
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/69465
dc.description.abstractIn 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.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/ICCAE.2010.5451972
dc.sourceScopus
dc.subjectAutocorrelation
dc.subjectLoad forecasting
dc.subjectLocal models
dc.subjectSelf-organizing map(SOM)
dc.subjectTime series prediction
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/ICCAE.2010.5451972
dc.description.sourcetitle2010 The 2nd International Conference on Computer and Automation Engineering, ICCAE 2010
dc.description.volume1
dc.description.page186-192
dc.identifier.isiut000397216200041
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