# Application of Artificial Intelligence in Wind Power Systems
## 4 Artificial Intelligence Algorithms
### Computational Demands  
The computational demands of AI in large wind farms can be large in some situations. AI-driven wind farms rely on continuous data streams from thousands of sensors monitoring turbine performance, weather conditions, and grid interactions. Predictive maintenance algorithms, for instance, analyze vibration, temperature, and power output data to prevent component failures. These are large data sets used to train such models, and the simplest sets with a resolution of 10 min are hundreds of megabytes in size on an annual basis [47, 86]. The National Renewable Energy Laboratory (NREL) developed the Wind Plant Graph Neural Network (WPGNN), a surrogate model trained on 250,000 simulated wind plant layouts, to optimize design and turbine placement [87]. Such large-scale simulations demand high-performance computing (HPC) clusters capable of parallel processing [87, 88]. Real-time analytics further strain computational resources. For example, GE's AI/ML logistics optimization tool employs digital twins to simulate turbine installation scenarios, necessitating edge computing infrastructure to minimize logistic costs in remote offshore environments [89]. The shift toward decentralized computing--where data preprocessing occurs at turbine-level edge devices--reduces cloud dependency but requires robust onboard processing units [90]. Machine learning models for wind forecasting and turbine control require large datasets for training. DNV's CFD.ML, a machine learning surrogate for \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Algorithm** & **Learning Approach** & **Functionality** & **Probabilistic vs. Non-Probabilistic** & **Model Type** & **Data Representation** & **Key Wind Application** & **References** \\ \hline
**Feedforward neural network (FFNN)** & Supervised & Regression/ forecasting & Non-probabilistic & Neural network & Structured (wind speed, SCADA) & Performance (foundations, loads) & [35] \\ \hline
**Long short-term memory (LSTM)** & Supervised & Sequence modeling/ forecasting & Non-probabilistic & Recurrent neural network & Time-series SCADA/weather & Short-term power prediction, anomaly & [38, 49, 50, 52, 53, 55, 57] \\ \hline
**Gated recurrent unit (GRU)** & Supervised & Sequence modeling/ forecasting & Non-probabilistic & Recurrent neural network & Time-series SCADA/weather & Wind speed/power forecasting & [38, 49] \\ \hline
**Extreme gradient boost (CGBoost)** & Supervised & Regression/ classification & Non-probabilistic & Ensemble model & Structured (SCADA, weather) & Anomaly detection, power forecasting & [49, 54, 79] \\ \hline
**Random forest (RF)** & Supervised & Regression/ classification & Non-probabilistic & Ensemble model & Structured (SCADA, sensor data) & Fault diagnosis (pitch, gearbox), feature selection & [32, 75, 78, 79] \\ \hline
**Naive Bayes** & Supervised & Classification & Probabilistic model & Statistical model & Structured (sensor data) & Blade fault detection (logistic regression) & [77] \\ \hline
**Adaptive neuro-fuzzy inference** & Supervised & Control/ forecasting & Non-probabilistic & Hybrid (fuzzy + neural net) & Structured, time-series & MPPT control, voltage stabilization & [50, 56, 57, 58] \\ \hline
**Decision trees** & Supervised & Classification/ decision rules & Non-probabilistic & Tree-based model & Structured (financial, sensor data) & End-of-life refurbishment & [85] \\ \hline
**Clustering (K-means, etc.)** & Unsupervised & Anomaly detection/ grouping & Non-probabilistic & Instance-based & Structured (sensor data) & Blade damage grouping, EoL resource allocation & [77, 85] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: \(Cont.\)computational fluid dynamics (CFD), uses graph neural networks to emulate high-fidelity wind flows. The speedup is significant: CFD.ML can produce results 2 million times faster than the CFD model it is trained on with only 2% of the computing resources [91].