Developing an integrated approach for optimum prediction and forecasting of renewable and non-renewable energy consumption in Iran Online publication date: Tue, 03-Mar-2020
by Reza Babazadeh; Shima Pashapour; Abbas Keramati
International Journal of Energy Technology and Policy (IJETP), Vol. 16, No. 2, 2020
Abstract: Energy planning for mid and long term periods needs forecasting the energy demands in the future. The artificial neural network (ANN) is an efficient forecasting tool which have been widely applied in different fields. One of the weaknesses of the ANN method is appeared when the studied case has many input parameters affecting on the performance of output factor. Noteworthy, there is not reliable data in many applications of real world. The canonical correlation analysis (CCA) method is an efficient tool for data reduction purpose keeping useful information of the used data. The purpose of this paper is to estimate and predict the renewable and non-renewable energy consumption considering environmental and economic factors. To this aim, an integrated approach based on the CCA and ANN method is utilised. The results show that the proposed approach reduces dimension of data without losing valuable information.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Energy Technology and Policy (IJETP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook