Enginerify was approached for a project in Berlin Germany for a wind turbine power plant. This project was to build a model to predict power production from one wind turbine over the next 3 years by predicting wind speed and variations. The project was performed by choosing Long-Short-Term Memory (LSTM) neural networks approach. The model was developed in Python. The model developed was then validated from historical data.
The main objective of this project was to analyze wind turbine performance patterns with weather conditions. To perform this project, the following activities were conducted:
Predict turbine power production based on wind speed forecasts for the upcoming three years.
Utilize machine learning techniques, particularly LSTM neural networks, for accurate wind speed predictions.
Analyze historical data, including wind speed for model training and validation.
Develop and validate the prediction model using Python to ensure accuracy and reliability.
Optimize turbine performance by identifying patterns in wind speed data and minimizing forecasting errors by Root Mean Square Error (RMSE).
Provide actionable insights for efficient wind energy production planning and operational improvements.
Support sustainable energy initiatives through precise forecasting and enhanced power generation.
Data Collection, wind speed data was gathered for Berlin from Germany's weather department, and wind turbine historical data from the power plant
Clean and normalize datasets in data processing
Build LSTM neural network in Python.
Train model and validate with historical data.
Forecast wind speed and power
Error Forecast by Root Mean Square Error
Result Analysis
Accurate wind speed prediction.
Power production was forecasted successfully.
Performance validation of the model against historical data accurately.
Data collected (Arranged)
Python model
Data visualization (Graphs and plots)
Validation and error results
Technical report
