I am seeking some help and or perspectives in solving a problem. I have a dataset (accessible here) with the following columns: DATE: this is the date in dd/mm/yyyy format HH: this is the "half-hour" window of the day. In a day there are 48 HH windows starting from 12 AM - 12:30 AM, 12:30 AM - 01:00 AM, and so on. WIND_SPEED_MS: this is the forecasted wind speed in m/s GENERATION_MW: this is the actual power generated by a windfarm in MW SEASONAL_NORMAL_WIND_SPEED_KNOTS: this is the long-term, historically averaged, windspeed for a given time of day and day of year, in knots (1 nautical mile per hour) The goal: To correlate wind power generation (MWh) to the forecasted wind speed (m/s), so that for a future wind speed forecast, I may predict the expected power output reliably. Optional side questions: What explains the power generation at very high wind speeds (when in reality most turbines should all be stopped for safety reasons beyond 30-35 m/ s)? What explains really high levels of wind generation, far above the "ceiling" that can be observed in the scatter plot? Why is the seasonal normal wind speed in knots, when converted to m/s, so low. It doesn't seem right. Your help would be greatly appreciated! My approach: average generation data (MW) in wind speed bins of 0.5 m/s width. This gives me a smoother curve that I can interpolate using piecewise polynomial. Then I can look up what will be the power output for a given (future) forecast wind speed. Any comments on this approach would also be appreciated. Continue reading...
