How to divide the wind season for wind power generation

What is the correlation between seasonal mean wind speed and wind power?

The simplest method, using the seasonal mean wind speed to forecast the seasonal mean wind power, has a correlation of 0.40, as shown in Fig. 7. The second method uses the seasonal mean of the cube of the instantaneous wind speeds.

How do I choose a wind data type?

Given the two different wind data types, we can decide which data to prefer by comparing the wind power curves that are associated with either wind measurement or modeled wind speed. The more scattered these curves map wind speed to wind power, the harder it gets to forecast wind power given wind speed.

Can wind power generation forecasts be forecasted at seasonal timescales?

While forecasts of wind power generation at lead times from minutes and hours to a few days ahead have been produced with very advanced methodologies (e.g. dynamical downscaling, machine learning or statistical downscaling [ 17 ]), a number of difficulties make the provision of generation forecasts at seasonal timescales challenging.

How many data groups are there in wind power data?

We mainly differentiate between two groups of data, namely, wind power data and wind-based data. We then further split the wind power data into three subgroups and the wind-based data into two subgroups, resulting in a total of five different groups of data. Figure 1 shows the connections between the five data groups.

How do we estimate wind power generation?

In some other cases, wind speed is first used to forecast and, then, the future values of this predicted variables are employed to estimate wind power generation. The obtained forecasts and simulations are evaluated through the most used accuracy measures: MAE, RMSE, MAPE, MSE, R 2, Mean Error (ME).

Can a seasonal wind energy prediction model be used for energy system planning?

There is a growing need of skillful seasonal wind energy prediction for energy system planning and operation. Here we demonstrate model’s capability in producing skillful seasonal wind energy prediction over the U.S.