Day-ahead price forecasting in restructured power systems using artificial neural networks

By V. Vahidinasab, Shahram Jadid, Ahad Kazemi. 2008

135 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. L. J. Soares
2. Marcelo C. Medeiros
3. Rafal Weron
4. James W. Taylor
5. Florian Ziel
6. Jakub Nowotarski
7. Bartosz Uniejewski
8. Grzegorz Marcjasz

Citations in the corpus

• Modeling and forecasting short-term electricity load: A comparison of methods with an application to Brazilian data (2008, methodology): “Theoretical basis and details of MLP with LM learning algorithm is discussed in [ 10 ] and”
• Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “For instance, Wallis (2005) proposes a finite mixture distribution as an appropriate statistical model for a combined density forecast, then discusses its implications for combining interval forecasts.”