# Enhanced Wind Energy Forecasting Using an Extended Long Short-Term Memory Model
## 3 SCADA Data Processing for for xLSTM  
### SCADA Data Processing for Wind Power Forecasting  
Supervisory control and data acquisition (SCADA) technology enables comprehensive monitoring of operational parameters in wind power systems. For wind turbine performance analysis, SCADA systems collect two distinct categories of information: sensor measurements and operational status codes [31]. The sensor-based data encompasses critical performance metrics, including power generation levels, rotational velocities, pitch angle positions, mechanical vibrations, thermal conditions, and lubricant properties. Meanwhile, status code information provides detailed event logging and state notifications regarding various turbine subsystems and components. These diagnostic codes vary across different original equipment manufacturers (OEMs) but serve to document the operational conditions and health status of the turbine. The systematic collection and analysis of both sensor measurements and status notifications through SCADA infrastructure facilitates real-time performance assessment and operational diagnostics of wind power assets. This integrated monitoring approach supports data-driven decision-making for optimizing turbine operation and maintenance strategies. To prepare SCADA data for wind power forecasting, a systematic preprocessing approach is implemented. Raw SCADA data in Table 1 are initially collected at 10 min sampling intervals, including key wind feature parameters, such as turbine rotational speeds, power outputs, and yaw angles. For a single wind turbine, its SCADA data yield approximately 50,530 data arrays over a one-year operational period. Quality control procedures identify and remove anomalous measurements, including missing values and statistical outliers, resulting in a refined dataset of 47,316 valid data arrays. This processed dataset undergoes temporal partitioning, with 80% of the seasonal data designated for model training and the remaining 20% allocated for testing purposes. To enhance model performance, the SCADA data undergo normalization to standardize the scale of different operational parameters. Additionally, the SCADA data processing incorporates validation checks to ensure temporal consistency and proper handling of sensor measurement boundaries. This rigorous data preparation framework ensures the reliability and representativeness of SCADA measurements for predictive models. Figure 1 uses a polar coordinate system where each point's radial distance from the center represents wind speed magnitude, while its angular position corresponds to wind direction. The statistical analysis reveals that the wind regime at the monitoring location is characterized by a mean wind speed of 7.56 m/s, with values ranging from complete calm (0.00 m/s) to substantial gusts (25.21 m/s). The median wind speed of 7.10 m/s indicates a slightly right-skewed distribution. The point distribution demonstrates distinct directional preferences, with the highest frequency of occurrence (7995 observations) concentrated in the east-northeast sector (67.5\({}^{\circ}\)). This predominant wind direction constitutes approximately 15.8% of all recorded measurements.