# Equipment Health Assessment: Time Series Analysis for Wind Turbine Performance
## 4 Discussion  
In the following section, we will discuss the results and benefits of the proposed method. In summary, we have proposed a wind turbine output prediction modeling and deterioration detection method that takes data from a wind turbine and then trains a prediction model to conduct deterioration detection on newly observed data. While the approach processes each wind turbine separately, the workflow is automatized in a manner where fine-tuning to the needs of each turbine is not necessary. The results for the power output prediction show that not all wind turbines achieve the same prediction performance due to differences in their feature settings even though the turbines were physically placed in a similar environment within a short distance from each other. It is therefore important to create separate prediction models for each wind turbine. Furthermore, our previous discussion based on Figure 10 showed that it is hard to choose a specific time series prediction method, LSTM or FNN, over another for all the wind turbines and an ensemble is a good approach to stabilize the prediction results achieving good prediction accuracy on the good timelines and distinguishably worse prediction performance on the bad timelines. For the deterioration detection, we have shown an overview of all wind turbines' power output prediction performance plotted for a continuous test dataset in Figure 15. The overall periods of good and bad performance are similar for the deterioration detection of all wind turbines showing that the wind turbines are operating under similar conditions. On the other hand, our experiments showed the uniqueness of each wind turbine. This makes us confident that there is value in using our ensemble model for prediction to detect deterioration of wind turbine individually. The overall F1-scores as shown in Table 3 are between 0.634 and 0.892 for all wind turbines for a mixed error metrics. In addition, it is not necessary for deterioration detection to identify all labels correctly. If we can keep the number of false positives low during \"good timeline\" data periods while detecting continuous phases of bad performance during \"bad timeline\" periods, it will be possible for a wind turbine operator to use these results to improve overall wind turbine power output in a quick manner. Even a fraction of an improvement will result in an improved business value. Our work can identify deteriorating performance in wind turbines therefore enabling better planning for the maintenance and repairs of wind turbines. The proposed deterioration detection approach can also be applied to other equipment and help improve the performance of those industry machinery. Running machines at lower efficiency leads to faster wear and tear as well as revenue loss, and our work supports to mitigate these risks. The obtained results are promising and it will be interesting to apply our work to other machines/equipment. The accurate prediction of power output over a group of wind turbines using the same method is challenging because of the differences in external and internal factors and how they affect the wind turbines. Currently, data is limited, and training/validation has to be conducted on a relatively small dataset, especially for  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Wind Turbine** & **RMSE** & **RMSPE** & **Mixed** \\ \hline
2 & 0.661 & 0.737 & **0.783** \\
3 & 0.710 & 0.704 & 0.775 \\
4 & 0.537 & 0.747 & 0.793 \\
5 & 0.407 & 0.516 & 0.634 \\
6 & 0.549 & 0.890 & 0.878 \\
7 & 0.490 & 0.876 & 0.876 \\
8 & 0.675 & 0.600 & 0.720 \\
9 & 0.526 the second part of deterioration detection. More experiments need to be done over a longer horizon to understand the performance better. Also, it is essential to consider the implications of reducing or increasing the number of features in wind turbine deterioration analysis. A sensitivity analysis could provide valuable insights into the impact of different features on the model's performance.