- author = "Gul Muhammad Khan and Jawad Ali and Sahibzada Ali Mahmud",
- booktitle = "International Joint Conference on Neural Networks (IJCNN 2014)",
- title = "Wind power forecasting: An application of machine learning in renewable energy",
- year = "2014",
- month = jul,
- pages = "1130--1137",
-
DOI = "
doi:10.1109/IJCNN.2014.6889771",
- size = "8 pages",
- abstract = "The advancement in renewable energy sector being the focus of research these days, a novel neuro evolutionary technique is proposed for modelling wind power forecasters. The paper uses the robust technique of Cartesian Genetic Programming to evolve ANN for development of forecasting models. These Models predicts power generation of a wind based power plant from a single hour up to a year - taking a big lead over other proposed models by reducing its MAPE to as low as 1.049percent for a single day hourly prediction. Results when compared with other models in the literature demonstrated that the proposed models are among the best estimators of wind based power generation plants proposed to date.",
- keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
-
notes = "Dept. of Electr. Eng., Univ. of Eng. & Technol.,
Peshawar, Pakistan
Also known as \cite{6889771}",
