# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 3 Results
### Prediction Models
For the ensemble method, no wind turbine prediction model performs better on the bad timeline dataset compared to the good timeline dataset. Figure 8: LSTM Model prediction output for both good and bad data periods (blue: true values, orange: predicted values). Figure 9: FNN Model prediction output for both good and bad data periods (blue: true values, orange: predicted values). Figure 11 shows the prediction performance for our previous example wind turbine no. 15 for the good timeline and bad timeline test datasets. The performance stays robust for the good timeline dataset but prediction performance deterioration for the bad timeline dataset is now much clearer. In Figure 12, we show the prediction results (RMSE) for the \"good timeline\" test dataset over all wind turbines for the ensemble prediction model. The first column of the table is the wind turbine no. on whose training data the wind turbine prediction model was built. Each wind turbine prediction model was used to not only predict the power output for its own test dataset but for all other wind turbines' \"good timeline\" test datasets as well. The obtained results are interesting, there's an overall tendency we can observe that each wind turbine is performing comparably well on its own test dataset as shown by the light green highlights indicating the minimum performance error of each column that often aligns with a wind turbine's own prediction model. Also, we can observe that performance results are mostly worst for wind turbine 8 (red highlight indicates max performance error of each row) and best for wind turbine 15 (dark green highlight indicates minimum performance error of each row). Wind turbine 8 might be affected by factors that are not considered in the selected features (or some unknown  
Figure 10: Ensemble Prediction Performance Comparison. external features) making the power prediction more difficult while wind turbine 15 is the most straight forward to predict. In Figure 13, we can see the ensemble model prediction results (RMSE) for the bad timeline test dataset over all wind turbines. The first column of the table is the wind turbine no. on whose good timeline training dataset the prediction model was built. We used the prediction models for each wind turbine to not only predict the power output for its own test dataset but for all other wind turbines' \"bad timeline\" test datasets. Overall, we can observe that each wind turbine is performing worse compared to the \"good timeline\" test datasets in Figure 12. Also, we can observe that prediction performance results are worst for wind turbine 8 and best for wind turbine 2 regardless of the chosen prediction model. On the other hand, the prediction model for wind turbine no. 3 achieves the overall best prediction performance (light green highlights for the best entry in each column) and wind turbine no. 5 the worst (orange highlights for worst entry in each column). In summary, Figure 12 showed us that the best prediction model for a wind turbine is based on its own training data. Results of Figure 13 on the other hand showed that there might be one wind turbine prediction model that is overachieving on the bad timeline of another wind turbine. Therefore, we can't use any one of the wind turbine's prediction model for other wind turbines' performance prediction. This supports our claim that each wind turbine (or equipment) is unique and has different feature settings, hence they can't be considered as homogeneous. Therefore, we have decided to predict power output for each wind turbine separately. Figure 11: Ensemble Model prediction output for both good and bad data periods (blue: true values, orange: predicted values). Figure 7.0 Ensemble Prediction Results over all good timeline test datasets (best and worst values are highlighted per row/column as dark green: best in row, red: worst in row, light green: best in column, orange: worst in column).