1. Wind Power Forecasting
University of Wisconsin – Milwaukee
Department of Electrical Engineering
Wind Power Forecasting
Yuber Samir Sánchez Rosas
BistLab

2. The main goal of this approach is to predict hourly values of wind speed for horizons of up to 12h.
Raw Data

3. Boxplots of the distribution of the wind power as a function of time of day.

4. Methology 1. Preprocess Data 2. Training 3. Iteratively training
4. Model and forecasting
Methology

5. Data

6. KNN Classifier

7. Wind Power Forecasting
A model was implemented to forecasting the wind power up to 12h ahead. This model consist of three parts: Preprocess data, Training and Forecasting.

8. Bibliography
Xydas, E., Qadrdan, M., Marmaras, C., Cipcigan, L., Jenkins, N., & Ameli, H. (2017). Probabilistic wind power forecasting and its application in the scheduling of gas-fired generators. Applied energy, 192,
Zambom, A. Z., & Dias, R. (2012). A review of kernel density estimation with applications to econometrics. arXiv preprint arXiv: