Short-term Load Forecasting Using Artificial Neural Network

By Moon-Hee Park. 1997

How researchers see this paper

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

1. Reinaldo Castro Souza
2. Carlos Eduardo Pedreira
3. Michael Smith
4. Henrique S. Hippert
5. Remy Cottet
6. James W. Taylor
7. Rob J Hyndman
8. Han Lin Shang

Citations in the corpus

• Neural networks for short-term load forecasting: a review and evaluation (2001, methodology): “(The exceptions were [2], that forecast loads at each half-hour and so needed 48 output neurons; and [ 55 ], that divided the profile into three parts, forecasted by separated MLPs.) This method, however, has two serious drawbacks.”
• Bayesian Modeling and Forecasting of Intraday Electricity Load (2003, background): “…one of the most popular involvesusing back-propagation algorithms to select parsimonious radial basis function representations of the nonlinear periodic daily and weekly effects (Hsu and Yang 1995; Lee, Cha, and Park 1992; Peng, Hubele, and Karady 1992; Ranaweera, Hubele, and Papalexopoulos 1995).”