On the public citation graph

Hybrid Physics-Informed Deep Learning Models for Wind Turbine Power Estimation

https://doi.org/10.2139/ssrn.6598002
0 signed · 28 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/we.509openalex-snapshot
Accounting for the speed shear in wind turbine power performance measurement
inferredcites →https://doi.org/10.1007/978-3-662-10944-1openalex-snapshot
Wind Turbine Operation in Electric Power Systems
inferredcites →https://doi.org/10.1007/s10311-022-01532-8openalex-snapshot
Cost, environmental impact, and resilience of renewable energy under a changing climate: a review
inferredcites →https://doi.org/10.1016/0003-2670(86)80028-9openalex-snapshot
Partial least-squares regression: a tutorial
inferredcites →https://doi.org/10.1016/j.chemolab.2015.12.011openalex-snapshot
Review of soft sensor methods for regression applications
inferredcites →https://doi.org/10.1016/j.compchemeng.2008.12.012openalex-snapshot
Data-driven Soft Sensors in the process industry
inferredcites →https://doi.org/10.1016/j.energy.2021.120617openalex-snapshot
Wind turbine power output prediction using a new hybrid neuro-evolutionary method
inferredcites →https://doi.org/10.1016/j.jprocont.2014.01.012openalex-snapshot
Data-driven soft sensor development based on deep learning technique
inferredcites →https://doi.org/10.1016/j.renene.2015.08.023openalex-snapshot
Research on power coefficient of wind turbines based on SCADA data
inferredcites →https://doi.org/10.1038/s41598-025-85619-6openalex-snapshot
Machine learning enhanced grey box soft sensor for melt viscosity prediction in polymer extrusion processes
inferredcites →https://doi.org/10.1080/23744731.2017.1318008openalex-snapshot
A review of fault detection and diagnostics methods for building systems
inferredcites →https://doi.org/10.1109/jas.2019.1911375openalex-snapshot
Adaptive and predictive control strategies for wind turbine systems: a survey
inferredcites →https://doi.org/10.1109/jsen.2021.3056210openalex-snapshot
Sampling-Interval-Aware LSTM for Industrial Process Soft Sensing of Dynamic Time Sequences With Irregular Sampling Measurements
inferredcites →https://doi.org/10.1109/mmit.2008.45openalex-snapshot
Research on Control System of High Power DFIG Wind Power System
inferredcites →https://doi.org/10.1109/tie.2020.2984443openalex-snapshot
Deep Learning With Spatiotemporal Attention-Based LSTM for Industrial Soft Sensor Model Development
inferredcites →https://doi.org/10.1109/tii.2019.2902129openalex-snapshot
Nonlinear Dynamic Soft Sensor Modeling With Supervised Long Short-Term Memory Network
inferredcites →https://doi.org/10.1109/tii.2019.2945411openalex-snapshot
Automatic Deep Extraction of Robust Dynamic Features for Industrial Big Data Modeling and Soft Sensor Application
inferredcites →https://doi.org/10.1109/tii.2021.3110507openalex-snapshot
A Novel Soft Sensor Modeling Approach Based on Difference-LSTM for Complex Industrial Process
inferredcites →https://doi.org/10.1109/tste.2022.3204453openalex-snapshot
Accounting for Environmental Conditions in Data-Driven Wind Turbine Power Models
inferredcites →https://doi.org/10.1162/neco.1997.9.8.1735openalex-snapshot
Long Short-Term Memory
inferredcites →https://doi.org/10.1177/0309524x241263600openalex-snapshot
Toward a physics-based model of power coefficient in horizontal-axis wind turbine
inferredcites →https://doi.org/10.1186/s42162-024-00373-9openalex-snapshot
Predictive digital twin for wind energy systems: a literature review
inferredcites →https://doi.org/10.2523/16604-msopenalex-snapshot
Employing First Principles Model-Based Soft Sensors for Superior Process Control and Optimisation
inferredcites →https://doi.org/10.3390/en12142716openalex-snapshot
Wind Farm Modeling with Interpretable Physics-Informed Machine Learning
inferredcites →https://doi.org/10.3390/en16062774openalex-snapshot
Comparison of Power Coefficients in Wind Turbines Considering the Tip Speed Ratio and Blade Pitch Angle
inferredcites →https://doi.org/10.5194/wes-5-427-2020openalex-snapshot
Analytical model for the power–yaw sensitivity of wind turbines operating in full wake
inferredcites →https://doi.org/10.5194/wes-9-1547-2024openalex-snapshot
On the power and control of a misaligned rotor – beyond the cosine law
inferredcites →https://doi.org/10.69997/sct.195540openalex-snapshot
Wind Turbines Power Coefficient Estimation Using Manufacturer�s Information and Real Data
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.