{"id":{"repo_id":"rice","oai_identifier":"oai:repository.rice.edu:1911/105768"},"canonical_url":"https://search.dev.ndltd.org/etd/rice/oai:repository.rice.edu:1911/105768","repository":{"repo_id":"rice","name":"Rice University","base_url":"https://repository.rice.edu/server/oai/request"},"display":{"title":"Wind Speed Forecasting for Power Generation Using a Self-Assembling Closed-Loop Recurrent Neural Network","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Qormemeti, Arti"],"institution":"Rice University","degree_name":"Master of Science","degree_level":"Masters","degree_discipline":"Engineering","degree_department":null,"school":null,"contributors":[],"advisors":["Meade, Andrew J"],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-04-19","date_published":"2018-04-19","updated_at":"2026-07-24T04:10:39Z","subjects":["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"],"languages":["eng"],"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."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1911/105768","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Meade, Andrew J"]},{"key":"dc:creator","label":"Author","values":["Qormemeti, Arti"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2019-05-17T15:20:04Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2019-05-17T15:20:04Z"]},{"key":"dc:date.issued","label":"Date","values":["2018-04-19"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Rice University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["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"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright is held by the author, unless otherwise indicated. 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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. 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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. 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