Improving Onshore Windfarm Siting is an industrial colloboration research that aims to locate windier sites for windfarm development Worldwide in a data-driven approach with the help of 'classical machine learning'. It was supported by Engineering Design Centre https://www-edc.eng.cam.ac.uk/ and Wind Pioneers http://www.wind-pioneers.com/.
Student Researcher: Yanhong Zhao (Cambridge University Engineering Part IIB Project) Supervisor: Dr. Ioannis Lestas, Dr Timoleon Kipouros Industrial Contact: Jerry Randall
Technical Abstract:
Final Report:
Award: CAPE (Centre for Advanced Photonics and Electronics) Acron IIB Acorn Award 2018 (https://twitter.com/CAPECambridge/status/995973467819462656)
Final Project Presentation:
All the codebase used in the project are here barring the original data which belongs to Wind Pioneers.
- Linear regression
- Bayesian linear regression
- Polynomial regression
- Ridge regression
- Lasso regression
- PCA regression
- FA regression
- Gaussian process regression
- Support vector machine regression
- Random forrest regression
- Multi-layer perceptron regression