# A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction

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## Abstract

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## 1. Introduction

## 2. Numerical Weather Prediction

^{−1}in the first few hours. After 15 h, the MAE values cluster around 1.8 ms

^{−1}for all the three models. These results suggest WRF RTFDDA provides value beyond the coarser operational models out to 15 h.

## 3. Statistical Postprocessing

## 4. Probabilistic Prediction

^{−1}), and ${o}_{n}$ is the categorical observation (0 if the event does not occur and 1 if it does occur). A lower value of the Brier score indicates better performance, with 0 corresponding to a perfect forecast. Using the DICast forecast as a reference, the Brier skill score (BSS) is calculated as:

_{DICast}is the BS of DICast. In the case of DICast, p

_{n}in (2) can only take 1 or 0 values, e.g., if the forecasted wind speed is greater or lower than 10 ms

^{−1}, respectively. The BSS represents the measure of the probabilistic forecast performance in comparison to the reference forecast and measures the ability of the model to issue a better probabilistic forecast than the reference. Positive (negative) values of BSS indicate better (worse) performances of AnEn than DICast.

## 5. Short-range Forecasting

## 6. Predicting Extreme Weather

#### 6.1. Input Forecasts

#### 6.2. Forecasts

#### 6.3. Wind and Temperature Forecasts

#### 6.4. Turbine Forecasts

#### 6.5. Case Study

## 7. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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**Figure 1.**Flowchart of the National Center for Atmospheric Research’s (NCAR’s) Xcel Energy power prediction system. NCEP: U.S. National Centers for Environmental Prediction, NAM: North American model, GFS: global forecasting system, RUC: Rapid Update Cycle, GEM Global Environmental Multiscale model, WRF RTFDDA: Weather Research and Forecasting-based real-time four-dimensional data assimilation, and VDRAS: variational doppler radar analysis system.

**Figure 2.**Comparison of hourly data assimilation and forecasting cycles without (

**left panel**) and with (

**right panel**) assimilation of wind turbine hub-height wind speed observations for a large wind farm in Northern Colorado. The black lines show the mean wind. Different color lines represent forecasts from different cycles with the first (last) two digits in the legend denoting the day of the month (hour in UTC). Conventional obs DA: conventional observation data assimilation and farm ws DA: farm wind speed data assimilation.

**Figure 3.**Comparison of the performance of RTFDDA with baseline numerical weather prediction (NWP) models. MAE: mean absolute error.

**Figure 5.**Example of an analog ensemble forecast probability density function (PDF) over one station of the dataset. The blue shadings correspond to the 25–75 (

**darker**) and 5–95 (

**lighter**) quantiles. The black and yellow dashed lines represent the wind speed observations and AnEn ensemble mean, respectively. The red line is the DICast wind speed forecast. AnEn: analog ensemble.

**Figure 6.**Root mean square error (RMSE) as a function of forecast lead time for DICast (

**red**) and AnEn means (

**black**) computed over all stations. Bootstrap 5%–95% confidence intervals are plotted for AnEn only. RMSE values listed are computed by including all lead times.

**Figure 7.**Brier skill score (BSS) of the analog ensemble (AnEn) with DICast as a reference, as a function of forecast lead time, computed over all the stations. The BSS 5%–95% bootstrap confidence intervals are also shown. The event considered is wind speed greater than 10 ms

^{−1}.

**Figure 8.**Schematic of the observations-based expert system for short-term forecasting. Available wind speed observations at 10 m in concentric circles around a wind farm are used to calculate the wind ramp metric, which is then used to compute changes in power production.

**Figure 9.**Summary schematic visualizing the various input datasets to the extreme weather system (ExWx). For DICast, C1-C4 indicate various configurations of the DICast forecast that are dependent on the input model data available at that lead time.

**Figure 10.**Curves representing the interest in icing conditions (0–1) for three variables from the WRF RTFDDA model data. Top panel is height of the model level, middle panel represents supercooled liquid water, (i.e., liquid rain or cloud drops below freezing), and the bottom panel shows temperature. The intersection of the three curves where they return a value of 1 will maximize the icing potential from the WRF RTFDDA model data. SLW: supercooled liquid water.

**Figure 11.**Forecasts of icing potential, combined from all turbines on a single farm, from 57 consecutive runs of the extreme weather system (y-axis). The first run shown is the 2000 UTC forecast from the extreme weather system on 24 December, 2014. Black shading indicates valid times within the event window (2000 UTC on 25 December–0400 UTC on 26 December) with an icing potential forecast of < 0.5, light blue shading indicates an icing potential forecast of > 0.5 within the event window, orange shading indicates an icing potential forecast of > 0.5 outside the event window, and gray shading indicates an icing potential forecast of < 0.5 outside the event window. Vertical lines indicate the division between C1, C2, and C3 DICast forecast system data (see Figure 9).

**Figure 12.**Wind speed weighted metric to identify periods when power production was less than expected and wind speeds were high enough to have confidence in the manufacturer’s power curve (i.e., not in the lower tail of the curve). The red line shows the farm average, while the black dashed lines show +/- the standard deviation for the farm. Top panel is for 25 December 2014, and bottom panel is for 26 December 2014. Higher values indicate a higher likelihood of icing (or curtailment).

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**MDPI and ACS Style**

Kosovic, B.; Haupt, S.E.; Adriaansen, D.; Alessandrini, S.; Wiener, G.; Delle Monache, L.; Liu, Y.; Linden, S.; Jensen, T.; Cheng, W.;
et al. A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction. *Energies* **2020**, *13*, 1372.
https://doi.org/10.3390/en13061372

**AMA Style**

Kosovic B, Haupt SE, Adriaansen D, Alessandrini S, Wiener G, Delle Monache L, Liu Y, Linden S, Jensen T, Cheng W,
et al. A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction. *Energies*. 2020; 13(6):1372.
https://doi.org/10.3390/en13061372

**Chicago/Turabian Style**

Kosovic, Branko, Sue Ellen Haupt, Daniel Adriaansen, Stefano Alessandrini, Gerry Wiener, Luca Delle Monache, Yubao Liu, Seth Linden, Tara Jensen, William Cheng,
and et al. 2020. "A Comprehensive Wind Power Forecasting System Integrating Artificial Intelligence and Numerical Weather Prediction" *Energies* 13, no. 6: 1372.
https://doi.org/10.3390/en13061372