A hybrid approach to forecasting wind power using Artificial Neural Networks and Numeric Weather Prediction

Loading...
Thumbnail Image
Authors
Pfeifer, Mark B.
Advisors
Jewell, Ward T.
Issue Date
2011-07
Type
Thesis
Keywords
Research Projects
Organizational Units
Journal Issue
Citation
Abstract

A methodology to forecast wind power production 24 hours ahead is developed using a hybrid approach of an artificial neural network (ANN) and numerical weather prediction (NWP). The methodology is simple and designed to be applicable to any wind farm on the globe, using publicly available NWP data and basic historical power production data from wind farm. Notably, no historical wind data from on-farm sensors is required as the 0 hour forecast data is used to train the ANN. The results are encouraging, with a root-mean-square-error of 0.2267 for a 24 hour ahead forecast, corresponding to a forecast error standard deviation of 0.23 per unit.

Table of Contents
Description
Thesis (M.S.)--Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science.
Publisher
Wichita State University
Journal
Book Title
Series
PubMed ID
DOI
ISSN
EISSN