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Wind | Free Full-Text | Wind Power Forecasting in a Semi-Arid Region Based on Machine Learning Error Correction

Wind power forecasting is pivotal in promoting a stable and sustainable grid operation by estimating future power outputs from past meteorological and turbine data. The inherent unpredictability in wind patterns poses substantial challenges in synchronizing supply with demand, with inaccuracies potentially destabilizing the grid and potentially causing energy shortages or excesses. This study develops a data-driven approach to forecast wind power from 30 min to 12 h ahead using historical wind power data collected by the Supervisory Control and Data Acquisition (SCADA) system from one wind turbine, the Enercon/E92 2350 kW model, installed at Casa Nova, Bahia, Brazil. Those data were measured from January 2020 to April 2021. Time orientation was embedded using sine/cosine or cyclic encoding, deriving 16 normalized features that encapsulate crucial daily and seasonal trends. The research explores two distinct strategies: error prediction and error correction, both employing a sequential ....

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Concordia Study: Enhanced Wind Forecasts Boost Urban Power

Concordia Study: Enhanced Wind Forecasts Boost Urban Power
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Ramanunni Menon , Ursula Eicker , Pilar Monsalvete , Anton Kaifel , Fuzhan Nasiri , Recurrent Neural Networks , Canada Excellence Research , Agency Institutional Program Secretariat , Centre For Solar Energy , Generation Cities Institute , Hydrogen Research In Stuttgart , Department Of Building , Concordia University , Gina Cody School Of Engineering , International Energy Agency , Environmental Engineering , Gina Cody School , Computer Science , Navid Shirzadi , Long Short Term Memory , Next Generation Cities Institute , Net Zero Emissions , Canada Excellence Research Chair , Resilient Cities , Solar Energy , Hydrogen Research ,

Improved wind speed forecasts can help urban

Researchers at Concordia University present a new wind speed forecasting model that they say can help urban microgrids plan and manage electricity generation. Navid Shirzadi Ph.D. used data from a Weibull probability distribution and numerical weather prediction models and fed it into a recurrent neural network. He says his model improves forecasting by over 30 percent over a 48-horizon. ....

Pilar Monsalvete , Anton Kaifel , Ursula Eicker , Ramanunni Menon , Fuzhan Nasiri , Recurrent Neural Networks , Gina Cody School Of Engineering , Centre For Solar Energy , Department Of Building , International Energy Agency , Hydrogen Research In Stuttgart , Canada Excellence Research , Agency Institutional Program Secretariat , Concordia University , Method Of Research , Generation Cities Institute , Environmental Engineering , Gina Cody School , Computer Science , Navid Shirzadi , Long Short Term Memory , Next Generation Cities Institute , Net Zero Emissions , Canada Excellence Research Chair , Resilient Cities , Solar Energy ,

Improved wind speed forecasts can help urban power generation, according to new research

Improved wind speed forecasts can help urban power generation, according to new research
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Fuzhan Nasiri , Ursula Eicker , Generation Cities Institute , Concordia University , Recurrent Neural Networks , Gina Cody School Of Engineering , Canada Excellence Research , Department Of Building , International Energy Agency , Environmental Engineering , Gina Cody School , Computer Science , Long Short Term Memory , Next Generation Cities Institute , Net Zero Emissions , Canada Excellence Research Chair , Resilient Cities , Navid Shirzadi , Smart Urban Wind Power Forecasting , Integrating Weibull Distribution , Numerical Weather Prediction ,

What is Multimodal Artificial Intelligence (AI)?

Learn what Multimodal AI is and how it is changing our world dramatically on a daily basis. Artificial intelligence that is capable of vision ....

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