# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Date and Time** & **Wind Speed (m/s)** & **Wind Direction (\({}^{\circ}\))** & **Rotor Speed (rpm)** & **Yaw Angle (\({}^{\circ}\))** & **Actual Power (kW)** \\ \hline
1 January 2021 0:00 & 5.20465 & 304.833 & 0 & 313.365 & 0 \\
1 January 2021 0:10 & 5.85888 & 299.338 & 0 & 313.365 & 0 \\
1 January 2021 0:20 & 6.06903 & 299.173 & 0 & 313.365 & 0 \\
1 January 2021 0:30 & 5.91886 & 296.647 & 0 & 313.365 & 0 \\
1 January 2021 0:40 & 5.40831 & 291.827 & 0 & 313.365 & 0 \\
1 January 2021 0:50 & 5.58617 & 287.716 & 0 & 313.365 & 0 \\
1 January 2021 1:00 & 5.62896 & 289.537 & 0 & 313.365 & 0 \\ \multicolumn{6}{c}{...} &... &... &... \\
31 December 2021 23:00 & 8.60170 & 35.803 & 14.903 & 36.780 & 2449.45 \\
31 December 2021 23:10 & 9.07816 & 38.179 & 15.013 & 36.780 & 2451.86 \\
31 December 2021 23:20 & 9.11971 & 44.452 & 14.968 & 36.780 & 2361.74 \\
31 December 2021 23:30 & 8.91951 & 40.3834 & 15.013 & 36.780 & 2298.64 \\
31 December 2021 23:40 & 9.55689 & 44.214 & 14.966 & 36.780 & 2361.43 \\
31 December 2021 23:50 & 10.06568 & 41.613 & 15.160 & 37.987 & 3795.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Raw SCADA data for wind power output prediction of a wind turbine [31]. This directional and magnitude characterization provides essential contextual information for interpreting SCADA measurements and enhancing the precision of xLSTM-based power output predictions. The significant proportion of measurements exceeding 6 m/s (60.6% cumulatively) suggests favorable conditions for energy generation, while the well-defined directional prevalence enables more precise modeling of the relationship between meteorological variables and power output. Table 2 presents a categorization of SCADA data distributed across eight directional sectors, each spanning 45\({}^{\circ}\) of wind directions. This SCADA data organization facilitates analysis of data quality and distribution patterns across different wind directions, which is crucial for accurate wind power forecasting models. Identifying and removing outliers in SCADA data serves to eliminate atypical measurements that could otherwise distort the underlying statistical distributions and compromise the validity of predictive models. These anomalous data points, which may result from sensor malfunctions, communication errors, or extreme environmental conditions, must be systematically detected and excluded to establish a more representative dataset that accurately reflects normal operational patterns of wind turbines. The raw SCADA dataset comprises 50,530 data points collected over a one-year monitoring period, as shown in Table 2. In the data preprocessing pipeline, outlier identification was implemented through statistical thresholds and domain-specific constraints that flag measurements deviating significantly from established operational patterns. A combination of univariate and multivariate methods was used for outlier identification of the SCADA data, including interquartile range analysis, modified z-score calculations with thresholds of \(\pm\)3.5, and physics-based validation rules that identify physically impossible or improbable parameter combinations, e.g., power generation during zero wind speed conditions, or power outputs exceeding theoretical maximums defined by the Betz limit. Through rigorous quality control procedures, 3214 data points (approximately 6.36% of the total) were identified as outliers and subsequently removed from the following xLSTM training for wind power forecasting. The resulting dataset consists of 47,316 valid SCADA measurements. Figure 1: Polar coordinate distribution for wind speeds and wind directions. Analysis of the wind directional distribution in the dataset reveals significant spatial heterogeneity in measurement frequency. The quadrants of 180\({}^{\circ}\)-270\({}^{\circ}\) and 270\({}^{\circ}\)-315\({}^{\circ}\) collectively exhibit the highest sampling density, comprising 16,674 measurements or 33.0% of the total dataset.