Effect of input variables selection on energy demand prediction based on intelligent hybrid neural networks
Numerous techniques have been applied by the researchers to predict the future electrical energy demand, which can be broadly categorized as parametric (statistical) and non-parametric (intelligent) techniques. The non-parametric or intelligent methods which are based on artificial intelligence are...
Saved in:
Main Authors: | Islam, B., Baharudin, Z., Nallagownden, P. |
---|---|
Format: | Article |
Published: |
Asian Research Publishing Network
2015
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949985436&partnerID=40&md5=26af41038764c477d9109fc767bc89d2 http://eprints.utp.edu.my/26033/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A meta-heuristics based input variable selection technique for hybrid electrical energy demand prediction models
by: ul Islam, B., et al.
Published: (2017) -
Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
by: Sovann, N., et al.
Published: (2017) - Hybrid and integrated intelligent system for load demand prediction
-
INPUT VARIABLE SELECTION FOR HOURLY OZONE (O3) CONCENTRATION PREDICTION IN URBAN AREA
by: Napi N.N.L.M., et al.
Published: (2023) -
Multi-input DC-DC converter for hybrid renewable energy generation system
by: Rosli, M.A., et al.
Published: (2014)