{"id":{"repo_id":"unr","oai_identifier":"oai:scholarwolf.unr.edu:11714/2580"},"canonical_url":"https://search.dev.ndltd.org/etd/unr/oai:scholarwolf.unr.edu:11714/2580","repository":{"repo_id":"unr","name":"University of Nevada - Reno","base_url":"https://scholarwolf.unr.edu/server/oai/request"},"display":{"title":"Optimizing Local Least Squares Regression for Short Term Wind Prediction","abstract":"Highly variable wind velocities in many geographical areas make wind farm integration into the electrical grid difficult. Since a turbine's electricity output is directly related to wind speed, predicting wind speed will help grid operators predict wind farm electricity output. The goal of experimentation was to discover a way to combine machine learning techniques into an algorithm which is faster than traditional approaches, as accurate or even more so, and easy to implement, which would makes it ideal for industry use. Local Least Squares Regression satisfies these constraints by using a predetermined time window over which a model can be trained, then at each time step trains a new model to predict wind speed values which could subsequently be transmitted to utilities and grid operators. This algorithm can be optimized by finding parameters within the search space which create a model with the lowest root mean squared error.","abstract_html":"Highly variable wind velocities in many geographical areas make wind farm integration into the electrical grid difficult. Since a turbine's electricity output is directly related to wind speed, predicting wind speed will help grid operators predict wind farm electricity output. The goal of experimentation was to discover a way to combine machine learning techniques into an algorithm which is faster than traditional approaches, as accurate or even more so, and easy to implement, which would makes it ideal for industry use. Local Least Squares Regression satisfies these constraints by using a predetermined time window over which a model can be trained, then at each time step trains a new model to predict wind speed values which could subsequently be transmitted to utilities and grid operators. This algorithm can be optimized by finding parameters within the search space which create a model with the lowest root mean squared error.","abstract_has_math":false,"creators":["Keith, Erin S."],"institution":null,"degree_name":null,"degree_level":"Master's Degree","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Harris, Frederick C."],"committee_chairs":[],"committee_members":["Kelley, Richard","Panorska, Ana"],"year":2015,"date_issued":"2015","date_published":"2015","updated_at":"2026-07-27T21:47:58Z","subjects":["hyper parameters","linear regression","simulated annealing","wind turbines"],"languages":[],"rights":["In Copyright(All Rights Reserved)"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/11714/2580","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Harris, Frederick C."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Kelley, Richard","Panorska, Ana"]},{"key":"dc:creator","label":"Author","values":["Keith, Erin S."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2018-02-12T18:04:03Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-12T18:04:03Z"]},{"key":"dc:date.issued","label":"Date","values":["2015"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master's Degree"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["hyper parameters","linear regression","simulated annealing","wind turbines"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["In Copyright(All Rights Reserved)"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/11714/2580"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Highly variable wind velocities in many geographical areas make wind farm integration into the electrical grid difficult. Since a turbine's electricity output is directly related to wind speed, predicting wind speed will help grid operators predict wind farm electricity output. The goal of experimentation was to discover a way to combine machine learning techniques into an algorithm which is faster than traditional approaches, as accurate or even more so, and easy to implement, which would makes it ideal for industry use. Local Least Squares Regression satisfies these constraints by using a predetermined time window over which a model can be trained, then at each time step trains a new model to predict wind speed values which could subsequently be transmitted to utilities and grid operators. This algorithm can be optimized by finding parameters within the search space which create a model with the lowest root mean squared error."]},{"key":"dc:format","label":"Dc Format","values":["PDF"]},{"key":"dc:title","label":"Title","values":["Optimizing Local Least Squares Regression for Short Term Wind Prediction"]}]}],"canonical_facts":{"dc:contributor.advisor":["Harris, Frederick C."],"dc:contributor.committeemember":["Kelley, Richard","Panorska, Ana"],"dc:creator":["Keith, Erin S."],"dc:date.accessioned":["2018-02-12T18:04:03Z"],"dc:date.available":["2018-02-12T18:04:03Z"],"dc:date.issued":["2015"],"dc:description.abstract":["Highly variable wind velocities in many geographical areas make wind farm integration into the electrical grid difficult. Since a turbine's electricity output is directly related to wind speed, predicting wind speed will help grid operators predict wind farm electricity output. The goal of experimentation was to discover a way to combine machine learning techniques into an algorithm which is faster than traditional approaches, as accurate or even more so, and easy to implement, which would makes it ideal for industry use. Local Least Squares Regression satisfies these constraints by using a predetermined time window over which a model can be trained, then at each time step trains a new model to predict wind speed values which could subsequently be transmitted to utilities and grid operators. This algorithm can be optimized by finding parameters within the search space which create a model with the lowest root mean squared error."],"dc:format":["PDF"],"dc:identifier.uri":["http://hdl.handle.net/11714/2580"],"dc:rights":["In Copyright(All Rights Reserved)"],"dc:subject":["hyper parameters","linear regression","simulated annealing","wind turbines"],"dc:title":["Optimizing Local Least Squares Regression for Short Term Wind Prediction"],"dc:type":["Thesis"],"thesis:degree_level":["Master's Degree"]},"updated_at":"2026-07-27T21:47:58Z"}