# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 3 Results  
### Prediction Models  
Figure 6 shows the overall flow of our proposed prediction method. In the following, we present the obtained results for prediction and deterioration detection. We have conducted many trial and error studies, where we experimented with LSTM and FNN time series modeling and compared the prediction outputs based on the RMSE. For these studies, we separated the data into \"good\" and \"bad\" performing time periods based on domain knowledge obtained from the data provider. A power output prediction model for each wind turbine was trained based on it's \"good\" data timeline and then tested on both a subset of \"good\" and \"bad\" data timelines. Figure 5: Deterioration Detection Method Overview. The training, validation, and test data for the good timeline are created based on randomly ordered three-day time windows to account for different seasons in the training data. To increase the number of samples in the training, validation, and test data, we have used a sliding window approach as depicted in Figure 7. First, we assign hourly resampled data to a 3-day long day group and then apply a sliding window approach within each 3-day group. This makes sure there is no data leakage. With this approach, we can increase the number of total samples while enabling the random assignment the training, validation, or test dataset. For our first prediction evaluation, we created the test dataset for the bad timeline in a similar manner, and we have aligned the randomization over all wind turbines to compare the same timelines in the same training, validation or test categories. An example of how such power output predictions would look like for a sample wind turbine no. 15 using LSTM and FNN model is shown in Figures 8 and 9 respectively for both \"good\" and \"bad\" test datasets. We can clearly observe that the prediction model is often overpredicting the power output for the bad timeline datasets, which indicates that the maximum power output could be potentially higher and there is an issue with the wind turbine performance. The poor performance can also be confirmed on the bad timeline data's power curves for the wind and generator speed (right side of Figures 8 and 9), as it deviates away from it's sigmoid behaviour. Comparing the predictions of LSTM and FNN, we observe that LSTM achieves a higher prediction accuracy on both good and bad test datasets whereas FNN offers more separation between them. Figure 6: Workflow for wind turbine power output prediction. Figure 7: Sliding window approach for 3-day time periods. This observation can be further confirmed in Figure 10 that shows the RMSE for predictions of three different models LSTM, FNN and an ensembled prediction model where LSTM and FNN predictions are accounted for with equal weights. Overall, we can conclude that the LSTM model performs better on all 13 wind turbine indices for both good and bad test datasets in comparison to FNN. However, the LSTM model also performs suspiciously well on the bad test datasets compared to the good datasets for the wind turbines no. 3, 4, 13, 14 and 17 (highlighted in red in Figure 10). The model might be overfitting. Since we want to leverage the prediction accuracy as an indicator of performance deterioration, we need a high performance accuracy on good timelines and a poor performance accuracy on bad timelines, the latter part is provided by FNN. We achieve this by combining the LSTM and FNN model predictions in an equally weighted ensemble shown on the right side of Figure 10. The green highlights indicate the best performing model for each of the wind turbine on the good timeline test datasets. The ensemble model achieves a similar performance on good timeline datasets and even outperforms LSTM prediction accuracy in four cases (for wind turbine no. 4, 5, 9 and 13). We also achieve a bigger gap between prediction accuracy's for the good timeline and bad timeline datasets.