Operational Forecasting  - Wind Turbine Power Generation

Blog Post created by gopal on Jan 29, 2020

The following is from the lab notes for the hands-on lab "Operational Forecasting" at OSIsoft Users Conference 2017, San Francisco, CA.  Lab VM is available via OSIsoft Learning

The Lab manual is attached; the manual is intended for an instructor led interactive workshop in a classroom setting.


The lab's objective is to step through an end-to-end data science/machine learning task -  collect data, publish historical data, develop a predictive model and deploy the model in real-time for wind turbine operations .  

The predictive model is to forecast power generation for each turbine in our fleet as shown below


Operational Forecasting - Wind Farm

Figure shows a graph of Active Power vs. Time - actual power in purple and forecasted power in yellow.

The predictive model is based on forecasted wind speed and air temperature.


The tools used are: 

  • PI Integrator -  publish historical turbine operations data to a SQL endpoint
  • Power BI and its built-in support for R scripts  -  data munging, data diagnostics and exploring the features
  • Azure ML - develop and deploy the model (as web services)
  • Windows script (or, alternatively a .Net C# code via AF-SDK) is used to read/write forecast data to PI 


Wind turbine Power vs Windspeed, also correlation plot

Figure shows a graph of Active Power vs. Wind Speed from operations data. 

For additional details, please see the Lab  Manual.