Popular GitHub Repositories for Swimlane Turbine

Machine learning applied to wind turbines incipient fault detection.

A Deep Learning model that predict forecast the power generated by wind turbine in a Wind Energy Power Plant using LSTM (Long Short Term Memory) i.e modified recurrent neural network.

Predicting Energy Output of Wind Turbine

Code for paper "Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders."

A framework for stabilizing a floating offshore wind turbine using Reinforcement Learning and a Predictive Safety Filter.

SwimPeek "connects the dots" between playbooks, components, applications, and other resources in Swimlane Turbine.

Predicting Turbine Energy Yield (TEY) using ambient variables as features.

An open-source framework demonstrating LLMs for complex semantic analysis of unstructured wind turbine maintenance logs. It generates deeper reliability insights by identifying failure modes, inferring causal chains, and uncovering site-specific operational patterns, enhancing data-driven O&M in the wind energy sector.

A Machine Learning tutorial on wind turbines data

A Flask-based web application to forecast wind turbine renewable energy generation using time-series feature engineering and a pre-trained XGBoost model. Users can input custom date ranges and visualize future energy predictions through dynamic Matplotlib plots.

A Flask-based web application to forecast wind turbine renewable energy generation using time-series feature engineering and a pre-trained XGBoost model. Users can input custom date ranges and visualize future energy predictions through dynamic Matplotlib plots.

The Vertical Axis Wind Turbine (VAWT) project is an innovative solution to harness wind energy generated by fast-moving trains and highway traffic. By installing vertical axis turbines alongside rail tracks and roads, this system can effectively convert kinetic wind energy into electricity

This project builds a machine learning model to predict Turbine Energy Yield (TEY) based on operational data. It features an interactive Streamlit dashboard and an API that serves the best-performing model tracked in MLflow.