Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information

By Julija Tastu, Pierre Pinson, Pierre-Julien Trombe, Henrik Madsen. 2014

127 citations recorded by the source corpus.

Subfields: Aerospace Engineering, 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 Aerospace Engineering

1. A. Taşcıkaraoǧlu
2. Borhan Molazem Sanandaji

In Electrical and Electronic Engineering

1. Jakub Nowotarski
2. Rafal Weron
3. Pravin Pratap Varaiya
4. Kameshwar Poolla
5. Amir Ghaderi
6. Faezeh Ghaderi
7. A. Taşcıkaraoǧlu
8. Borhan Molazem Sanandaji

Citations in the corpus

• Deep Forecast: Deep Learning-based Spatio-Temporal Forecasting (2017, methodology): “A methodology with probabilistic wind power forecasts in the form of predictive densities taking the spatial information into account was developed in (Tastu et al., 2014).”
• Recent advances in electricity price forecasting: A review of probabilistic forecasting (2016, background): “[115] and Baran and Lerch [116], among others, conduct DM tests for probabilistic wind forecasts.”
• Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform (2016, methodology): “A method with probabilistic wind generation power forecasts is developed in [26] taking the spatial information into account.”
• Low-dimensional models in spatio-temporal wind speed forecasting (2015, unknown): “A methodology with probabilistic wind power forecasts in the form of predictive densities taking the spatial information into account was developed in [19].”