Short-term electricity prices forecasting in a competitive market: A neural network approach

By João P. S. Catalão, S. Mariano, Victor M. F. Mendes, L. Ferreira. 2007

430 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/834c498787b4506743ab4d0a6ddf95766f48d33b

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. Jakub Nowotarski
2. Rafal Weron
3. Victor M. F. Mendes
4. João P. S. Catalão
5. Shiyan Hu
6. Xiaodao Chen
7. Hugo M. I. Pousinho
8. Tongquan Wei

In Renewable Energy, Sustainability and the Environment

1. Rafal Weron
2. João P. S. Catalão
3. Florian Ziel
4. Bartosz Uniejewski

Citations in the corpus

• Electricity price forecasting: A review of the state-of-the-art with a look into the future (2014, background): “Catalão et al. (2007); Pindoriya, Singh, and Singh”
• Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “Catalão (University of Beira Interior, Portugal), who has published nine EPF articles, including the highly cited Shortterm electricity prices forecasting in a competitive market: A neural network approach [27] (154”
• Short-term wind power forecasting in Portugal by neural networks and wavelet transform (2011, methodology): “While backpropagation is a steepest descent algorithm, the Levenberg-Marquardt algorithm is a variation of Newton's method [14,20].”
• Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home (2013, methodology): “scheme is independent of pricing models or pricing prediction models as is described in [38], [39].”