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.
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.
","abstract_html":"<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>","abstract_has_math":false,"creators":["Rezaee Garacani, Daniel"],"institution":null,"degree_name":"MS in Electrical Engineering","degree_level":null,"degree_discipline":"Electrical Engineering","degree_department":null,"school":null,"contributors":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-01T07:00:00Z","date_published":"2021-09-01T07:00:00Z","updated_at":"2026-07-24T01:32:29Z","subjects":[],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["10.15368/theses.2021.176"],"render_values":[{"text":"10.15368/theses.2021.176","href":"https://doi.org/10.15368/theses.2021.176","code":true}]}]},"links":{"outbound_url":"https://digitalcommons.calpoly.edu/theses/2612","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"]},{"key":"dc:creator","label":"Author","values":["Rezaee Garacani, Daniel"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2025-07-25T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical Engineering"]},{"key":"thesis:degree_name","label":"Degree Name","values":["MS in Electrical Engineering"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.calpoly.edu/theses/2612","10.15368/theses.2021.176"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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.
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.
"]},{"key":"dc:title","label":"Title","values":["Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks"]}]}],"canonical_facts":{"dc:contributor":["Xiao-Hua (Helen) Yu","Electrical Engineering","College of Engineering"],"dc:creator":["Rezaee Garacani, Daniel"],"dc:date.available":["2025-07-25T07:00:00Z"],"dc:description.abstract":["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.
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.
"],"dc:identifier":["https://digitalcommons.calpoly.edu/theses/2612","10.15368/theses.2021.176"],"dc:title":["Short-Term Wind Speed Time Series Forecasting Using Artificial Neural Networks"],"thesis:degree_discipline":["Electrical Engineering"],"thesis:degree_name":["MS in Electrical Engineering"]},"updated_at":"2026-07-24T01:32:29Z"}