Abstract
The aim of this paper is to propose the use of regularized cascade correlation neural networks to forecast the monthly Brazilian electricity spot price. The cascade correlation models have been regularized with weight decay, weight elimination and ridge regression techniques, and several regularized models have been estimated. The results show that the regularized cascade correlation network represents the dynamic series better than other similar models such as the multilayer perceptron (MLP) and ARIMA. Then the regularized cascade correlation neural networks allow finding a suitable model to forecast the monthly Brazilian electricity spot price.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callSimilar Papers
More From: Revista U.D.C.A Actualidad & Divulgación Científica
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
