What is the inverse distance weighting method and how is it used to fill in missing weather data in the solar radiation prediction model?

Inverse distance weighting is an interpolation method used to estimate a missing meteorological value at a target station by combining observed values from nearby stations, with each observation weighted by the inverse of its distance from the target. In the solar radiation prediction model, it is applied to incomplete weather time series data to fill gaps, restoring a complete dataset that can be used to train and test the AI prediction model. The inverse coefficient n in the distance weighting is commonly set to 2.

After an initial screening step removed abnormal readings, the weather data still contained missing time series points, ranging from single missing values to hundreds of missing points per day. To fill these gaps, the researchers used weather data from stations located close to the target observer station and applied inverse distance weighting. In this method, the missing value at the target station is interpolated from the observed weather values at nearby stations, with each station weighted according to the inverse of its distance from the target. The weighting parameter n, called the inverse coefficient, is often set to 2. This restored a complete set of weather information that could then be supplied to the AI modules for training, validation, and testing of the solar radiation forecasting model.