Short-term forecast of
onshore windturbines oscillation kinematics

Intuitive and helpful models for
statistical analysis and shortterm forecasting
of measurement data.

📖 CONTENTS OF THIS FILE

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• Introduction
• Tech stack
• Folder Structure
• Dataset
• Forecasting Models
• CONTACT Elements for IoT Integration
• Installation
• Usage
• References
• License

📝 Introduction

Onshore wind already provides a substantial part of the energy mix ⚡ today. In order to further reduce the costs 💲, the installation process in particular should be improved. The installation of the blades is the greatest challenge. Relative movements between the nacelle and the blade root make the installation difficult. If the relative movement exceeds a certain threshold value, installation is no longer possible and there is an expensive delay. Based on measurement data 📊 that were recorded during the installation of wind turbines, machine learning 🤖 models and neuronal networks are intended to predict the oscillation kinematics for a defined period of time.

This repository contains three different forecasting models, which are available as jupyter notebooks, a CONTACT Elements for IoT integration and an associated thesis.

👨‍💻 Tech Stack

Front-End

Back-End

Database

Machine Learning & Neuronal Networks

⚠️ License

This repository uses the MIT license. Please see the LICENSE.md file for more details.