Wind Speed Forecasting for Power Generation Using a Self-Assembling Closed-Loop Recurrent Neural Network

This thesis presents the self-assembling recurrent neural network, SFA (Sequential Function Approximation), as a time series forecasting method for wind speed prediction. We compare its multi-step prediction performance against a proven recurrent neural network, NARX (Non-linear Auto-Regressive neural network with eXogenous inputs), on several univariate and multivariate time series, including weather measurements from the Bogdanci Wind Park in Macedonia. Artificial neural networks, such as NARX, require a good deal of trial and error in finding the optimal network configuration. Training these types of networks also comes with high fluctuations in closed-loop prediction performance on each training initialization due to parameter randomization. The SFA method sidesteps these drawbacks while providing comparable or better prediction. This is achieved with the SFA algorithm assembling the input-output mapping by itself to achieve a tolerance set by the user.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Engineering

Grantor

Rice University

Year dc:date.issued

2018

Author dc:creator

• Qormemeti, Arti

Advisor dc:contributor.advisor

• Meade, Andrew J

• Machine learning
• Artificial Intelligence
• Neural Networks
• NARX
• SFA
• Sequential Function Approximation
• Nonlinear Autoregressive network with Exogenous inputs
• Time Series
• Forecasting
• Prediction
• Wind speed
• Wind Power

Statement dc:rights

• Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/105768

OAI identifier oai:identifier

oai:repository.rice.edu:1911/105768