# Real-Time Sea State Estimation for Wave Energy Converter Control via Machine Learning
## 6 Discussion  
Our proposed control approach is compared with several other traditional machine learning models and control strategies. Table 4 presents a comparative analysis of different models for both fixed and variable \(T_{p}\), emphasizing the effectiveness of the proposed  
Figure 8: WaveBot performance in variable sea states, \(T_{p}\in[2,6]\). The sea state linearly varies over the 400 s simulation time, starting at 2 s period and ending at 6 s period.  
Figure 9: Instantaneous and average power produced by fixed \(T_{p}=4\) and variable \(T_{p}\in[2,6]\). The instantaneous sea state period detection approach significantly outperforms the fixed case by 34.09 W average power to 27.13 W.  
LSTM-based control technique. The results demonstrate that LSTM outperforms ANN and SVR across all performance metrics, achieving the highest \(R^{2}\)-score and the lowest RMSE for both cases. For the fixed \(T_{p}\) scenario, LSTM attains an \(R^{2}\)-score of 0.98 with an RMSE of 0.005, leading to an average power capture (\(P_{\text{avg}}\)) of 35.26 W, the highest among all models. Similarly, under the variable \(T_{p}\) approach, LSTM maintains superior performance with an \(R^{2}\)-score of 0.96 and an RMSE of 0.03, achieving an average power capture of 34.09 W. This power capture outperforms the baseline PI controller, which yields 27.13 W. The key outcome of this study is the demonstrated effectiveness of the LSTM-based control approach in adapting to varying sea states. Conventional PI control with fixed \(K_{P}\) and \(K_{I}\) struggles with dynamic wave conditions, showing similar power capture in fixed sea states but poor performance in variable sea states. However, the proposed method dynamically adjusts control parameters, leading to enhanced power extraction. These results validate the potential of machine learning-driven adaptive control strategies in optimizing energy capture for wave energy converters. The proposed LSTM model demonstrates promising results in predicting ocean wave parameters from WaveBot measurements; however, certain limitations remain. One of the major challenges is the MATLAB-Python co-design integration, which requires specific version compatibility for both training and testing phases. Also, the addition of AWGN to the WaveBot measurements can cause discrepancies, especially at peak force amplitudes; hence, the model's sensitivity to noise intensity requires further investigation.  
Future work could address these limitations by exploring alternative noise models, such as colored noise, to better simulate oceanic environments. Additionally, we plan to leverage direct CWT images as inputs to train CNNs for improved feature extraction and wave parameter predictions. Pretrained CNN models will also be explored to enhance the accuracy and efficiency of \(T_{p}\) estimation, leveraging transfer learning techniques for better generalization across varying sea states. Furthermore, integrating the model with real-time data acquisition from WaveBot could provide insights into practical implementation challenges and further optimize performance for live ocean energy applications. Addressing these directions would improve the model's robustness, generalizability, and practical value in ocean wave prediction and energy harvesting.