A comparative study for optimum short-term forecasting of electricity price with uncertainty

Ali Azadeh, Babak H. Tabrizi and Vahid Ebrahimipour

International Journal of Industrial and Systems Engineering, 2012, vol. 12, issue 4, 394-428

Abstract: This paper presents an integrated algorithm consisting of artificial neural network (ANN), fuzzy linear regression (FLR) and conventional linear regression (CLR) for optimum short-term electricity price forecasting. Eight ANN models and seven well-known FLR models as well as CLR model are considered simultaneously to provide a robust framework for electricity price forecasting. Thus, it can be easily applied to uncertain and complex environments due to its flexibility. The input data are divided into train and test parts to test the results for further times. Data sets are compared with ANN, fuzzy regression (FR) and conventional regression (CR) techniques in a comprehensive empirical process, with respect to mean absolute percent error measure. It is shown that ANN leads to the most efficient outputs, although, sometimes FR models could yield suitable results. Then, the procedure is repeated for normalised data, and recent results are obtained again, which puts a validation on what was concluded before. At last, the research has been practiced in Iran and proved to be efficient, as the circumstances dominating the society do not follow a steady trend. This is the first study that investigates intelligent models for short-term electricity price forecasting with uncertainty.

Keywords: short-term forecasting; electricity prices; fuzzy linear regression; conventional linear regression; ANNs; artificial neural networks; uncertainty; optimisation; electricity pricing; fuzzy logic; price forecasting; intelligent modelling. (search for similar items in EconPapers)
Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.inderscience.com/link.php?id=50121 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijisen:v:12:y:2012:i:4:p:394-428

Access Statistics for this article

More articles in International Journal of Industrial and Systems Engineering from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().