I am using Python to do weather forecasting.
Here is the original data.
The input has 180 features and the meanings are:
Suppose we are doing forecast for hour k. We will use the historic weather data (past 3 hours) and the weather forecast (hour k).
For the wind generator, the 180 input features (4x11x4+4) are:
Station 1 Hour k-3: [temperature, humidity, wind speed, wind direction] ... Hour k: [temperature, humidity, wind speed, wind direction]
Station 11 Hour k-3: [temperature, humidity, wind speed, wind direction] ... Hour k: [temperature, humidity, wind speed, wind direction]
Time related features [sin(hour), cos(hour), sin(day_of_year), cos(day_of_year)]
4114 +4 = 180
And one output is the power.
The hint is, This data is not clean, in that there is some portion of bad data in the target/output data (not the input data). However, it is hard to know which data is bad. You may consider how to clean the data first (more about sample selection, not feature selection). After data cleaning, you may then consider feature selection to reduce the input dimension. There a lot of feature selection methods available in literature.
I wonder how to clean the data and then do feature selection. Thanks!