An analog ensemble for short-term probabilistic solar power forecast

By Stefano Alessandrini, Luca Delle Monache, S. Sperati, Guido Cervone. 2015

331 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/db682c6ca6e92ad943ee8d0698bd1cb581a20ffe

How researchers see this paper

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

In Artificial Intelligence

1. F. J. Martínez-de-Pisón
2. Fernando Antoñanzas-Torres
3. Javier Antoñanzas
4. R. Escobar
5. Rubén Urraca
6. Pierre Pinson
7. Faranak Golestaneh
8. N. Osório

In Electrical and Electronic Engineering

1. Pierre Pinson
2. Faranak Golestaneh
3. Hoay Beng Gooi
4. Rafal Weron
5. Henrik Madsen

In Renewable Energy, Sustainability and the Environment

1. Pierre Pinson
2. Faranak Golestaneh
3. Hoay Beng Gooi

Citations in the corpus

• Generation and evaluation of space–time trajectories of photovoltaic power (2016, background): “There are handful practices on probabilistic forecasts of PV generation available in the literature [6, 7, 8, 9].”
• Review of photovoltaic power forecasting (2016, methodology): “Some of the benefits of probabilistic forecasts are the better allocation of power reserves to overcome solar power uncertainty and greater economical revenue in the dayahead market compared to deterministic predictions (Alessandrini et al., 2015). Probabilistic forecasts provide a broader knowledge”