{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/24081"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/24081","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"A short-term ensemble wind-speed forecasting system for wind power applications","abstract":"Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy’s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.","abstract_html":"Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy’s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.","abstract_has_math":false,"creators":["Traiteur, Justin J."],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Atmospheric Sciences","degree_department":null,"school":null,"contributors":["Roy, Somnath B."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2011,"date_issued":"2011-05-25T14:57:57Z","date_published":"2011-05-25T14:57:57Z","updated_at":"2026-07-22T22:25:23Z","subjects":["Short-term wind speed forecasting","Bayesian Model Averaging","Weather Research and Forecasting Single-Column Model (WRF-SCM)"],"languages":["en"],"rights":["Copyright 2011 Justin J. Traiteur"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/24081","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Roy, Somnath B."]},{"key":"dc:creator","label":"Author","values":["Traiteur, Justin J."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2011-05-25T14:57:57Z","2011-05"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Atmospheric Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Short-term wind speed forecasting","Bayesian Model Averaging","Weather Research and Forecasting Single-Column Model (WRF-SCM)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2011 Justin J. Traiteur"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/24081"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy’s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-29T13:40:55Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Traiteur_Justin.docx: 2928365 bytes, checksum: f9acb638c2c4587fb7caa2393f1923a3 (MD5) Traiteur_Justin.pdf: 1603546 bytes, checksum: fb74ee384da80daa7f46a5a4f588db4d (MD5)","Made available in DSpace on 2011-05-25T14:57:57Z (GMT). No. of bitstreams: 4 Traiteur_Justin.pdf: 1600709 bytes, checksum: b732e02a946caf85f450830df16f7e3f (MD5) license.txt: 4065 bytes, checksum: 41e68900e680b7920844d65933ebd9a8 (MD5) Traiteur_Justin.docx: 2928826 bytes, checksum: 9f913bd05de63dece754e63619bc89bc (MD5) 1_Traiteur_Justin.docx: 2928826 bytes, checksum: 9f913bd05de63dece754e63619bc89bc (MD5)"]},{"key":"dc:title","label":"Title","values":["A short-term ensemble wind-speed forecasting system for wind power applications"]}]}],"canonical_facts":{"dc:contributor":["Roy, Somnath B."],"dc:creator":["Traiteur, Justin J."],"dc:date":["2011-05-25T14:57:57Z","2011-05"],"dc:description":["Accurate short-term wind speed forecasts for utility-scale wind farms will be crucial for the U.S. Department of Energy’s (DOE) goal of providing 20% of total power from wind by 2030. For typical pitch-controlled wind turbines, power output varies as the cube of wind speed over a significant portion of the power output curve. Therefore, small improvements in wind-speed forecasts would constitute much larger improvements in wind power forecasts. In addition, communicating the level of uncertainty in these wind speed forecasts will allow the industry to better quantify the level of financial risk inherent with these forecasts. In this study, a computationally efficient and accurate forecasting system is developed. This system uses a 21-member ensemble of the Weather Research and Forecasting Single-Column Model (WRF-SCM V3.1.1) to generate a probability distribution function (PDF) of 1-hour forecasts at a 90m height location in West/Central Illinois. The WRF-SCM ensemble was initialized by the 20 km Rapid update Cycle (RUC) 00h forecast and perturbed by both perturbations in the initial conditions and physics options. The PDF was calibrated using Bayesian Model Averaging (BMA) where the individual forecasts were weighted according to their performance. This combination of a mesoscale numerical weather prediction ensemble system and Bayesian statistics allowed for both accurate prediction of 1-hour wind speed forecasts and their level of uncertainty.","Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2011-04-29T13:40:55Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 2 Traiteur_Justin.docx: 2928365 bytes, checksum: f9acb638c2c4587fb7caa2393f1923a3 (MD5) Traiteur_Justin.pdf: 1603546 bytes, checksum: fb74ee384da80daa7f46a5a4f588db4d (MD5)","Made available in DSpace on 2011-05-25T14:57:57Z (GMT). No. of bitstreams: 4 Traiteur_Justin.pdf: 1600709 bytes, checksum: b732e02a946caf85f450830df16f7e3f (MD5) license.txt: 4065 bytes, checksum: 41e68900e680b7920844d65933ebd9a8 (MD5) Traiteur_Justin.docx: 2928826 bytes, checksum: 9f913bd05de63dece754e63619bc89bc (MD5) 1_Traiteur_Justin.docx: 2928826 bytes, checksum: 9f913bd05de63dece754e63619bc89bc (MD5)"],"dc:identifier":["http://hdl.handle.net/2142/24081"],"dc:language":["en"],"dc:rights":["Copyright 2011 Justin J. Traiteur"],"dc:subject":["Short-term wind speed forecasting","Bayesian Model Averaging","Weather Research and Forecasting Single-Column Model (WRF-SCM)"],"dc:title":["A short-term ensemble wind-speed forecasting system for wind power applications"],"thesis:degree_discipline":["Atmospheric Sciences"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:23Z"}