Journal Club: 04-04-2018

Article Title: Using weather ensemble prediction in electricity forecasting, Taylor and Buizza (2013).

The authors compare different methods of including weather into an electricity demand forecasting model. They compare the traditional method (inputting the mean of an ensemble of weather forecasts), to that of applying each weather forecast from the ensemble to the demand model, and then taking the mean of the scenarios. They show that the second method (x) performs better than the traditional method (o) for lead times of 1-10days (see Figure 3 below).

The difference in accuracy of the 2 methods increases with lead time. It is concluded that the reason for the improved performance is due to the fact that the demand is a non-linear function of weather. Further, they show that excluding weather variables from the electricity demand forecast model reduces the accuracy substantially.

The demand model is a regression model based on the UK National Grid. Weather related components depend on effective temperature, cooling power of the wind and effective illumination (replaced by cloud cover here). The model is weighted to reflect different population densities across 5 English cities.

The authors also present different methods of estimating the demand forecast error and the demand prediction intervals. From this they conclude that including weather ensemble information can improve these estimates compared to traditional volatility forecasting methods.

Applications to our research:

  • Implement this demand model for Ireland.
  • Use this model to identify atmospheric spatial patterns associated with certain demand profiles.
  • Apply Shortwave Radiation data to model instead of cloud cover.
  • Potentially use the model to link demand profiles to climate patterns (eg. NAO).
  • First check if there are improved versions of this model.