Predicting the energy output of wind farms based on weather data: Important variables and their correlation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8110
- @Article{Vladislavleva:2013:RE,
-
author = "Ekaterina Vladislavleva and Tobias Friedrich and
Frank Neumann and Markus Wagner",
-
title = "Predicting the energy output of wind farms based on
weather data: Important variables and their
correlation",
-
journal = "Renewable Energy",
-
volume = "50",
-
year = "2013",
-
pages = "236--243",
-
keywords = "genetic algorithms, genetic programming, Wind energy,
Prediction, Data Modeller",
-
ISSN = "0960-1481",
-
URL = "http://www.sciencedirect.com/science/article/pii/S0960148112003874",
-
DOI = "doi:10.1016/j.renene.2012.06.036",
-
size = "8 pages",
-
abstract = "Wind energy plays an increasing role in the supply of
energy world wide. The energy output of a wind farm is
highly dependent on the weather conditions present at
its site. If the output can be predicted more
accurately, energy suppliers can coordinate the
collaborative production of different energy sources
more efficiently to avoid costly overproduction. In
this paper, we take a computer science perspective on
energy prediction based on weather data and analyse the
important parameters as well as their correlation on
the energy output. To deal with the interaction of the
different parameters, we use symbolic regression based
on the genetic programming tool DataModeler. Our
studies are carried out on publicly available weather
and energy data for a wind farm in Australia. We report
on the correlation of the different variables for the
energy output. The model obtained for energy prediction
gives a very reliable prediction of the energy output
for newly supplied weather data.",
-
notes = "See oai:arXiv.org:1109.1922
http://arxiv.org/abs/1109.1922",
- }
Genetic Programming entries for
Ekaterina (Katya) Vladislavleva
Tobias Friedrich
Frank Neumann
Markus Wagner
Citations