Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks

<p>Renewable energies such as wind power have become integral parts of modern power networks. Short-term wind speed prediction is crucial for smart grids, as it can help balance the demand and supply, as well as set the energy price in the market.</p> <p>In this thesis, we simulate and compare various neural network models for short-term wind speed prediction, including multi-layer feedforward neural networks, convolutional neural networks, long-short term memory networks, and hybrid models such as CNN- LSTM and ConvLSTM. Computer simulation results show that all artificial neural networks are able to provide satisfactory prediction. Among them, the multi-layer feedforward neural networks require less training time and often give reasonable results; while the ConvLSTM networks need longer time to train and implement, but it may provide a slightly better accuracy in some cases.</p>

Name thesis:degree_name

MS in Electrical Engineering

Discipline thesis:degree_discipline

Electrical Engineering

Year dc:date.available

2021

Author dc:creator

• Rezaee Garacani, Daniel

Contributors dc:contributor

• Xiao-Hua (Helen) Yu
• Electrical Engineering
• College of Engineering

Identifier

10.15368/theses.2021.176

OAI identifier oai:identifier

oai:digitalcommons.calpoly.edu:theses-4133