1. On the Influence of Weather Forecast Errors in Short-Term Load Forecasting Models Damien Fay, John V. Ringwood IEEE POWER SYSTEMS, 2010

2. Outline  Introduction  Data sets  Weather forecast errors modeling  Fusion model  Preliminary AR linear model  Sub-models  Fusion algorithm  Results  Conclusion

3. Introduction  Short-term load forecasting(STLF) refers to forecast electricity demand on an hour basis from one to several days ahead.  STLF reduce the amount of excess electricity production by underestimate the load accurately.  In many electricity grid systems, “weather” is an important factor to estimate the load and has been proved that it will improve the prediction accuracy. However, weather forecasts often come with forecasting errors, and cause about 17% to 60% load forecasting errors.

4. Introduction  Main idea in this paper is to combine several models(called sub-models), or model fusion, as a technique for minimizing the effect of weather forecast errors in load forecasting models.  Model fusion has been widely used in general field forecasting, but this is the first use to deal with forecast errors. The sub-models in fusion algorithm may be trained by actual weather data and the effect of weather forecasting error taken into account when combining models.

5. Data sets

6. Weather forecast errors modeling  Previous approach in STLF simply model the weather forecast errors as a IID Gaussian random variable

7.  Weather in Ireland is dominated by Atlantic weather systems. When a weather front reaches Ireland, there is a shift in the level of temperature and other variables. This shift is also an important factor and must be detected.  Turning points represent the arrival of the weather front. And the turning points were found using peak detection algorithm. Weather forecast errors modeling

8.  In order to generate pseudo-weather forecast errors, the turning points are first identified. Then a multivariate Gaussian pseudo-random number generator is used to generate random errors for each weather variables.

9. Fusion Model- Preliminary AR linear model

10. Fusion Model- Sub-Models  Three sub-models were chosen with different inputs. These are chosen so that forecast errors can be attributed to particular inputs.  A fourth sub-model is included using all the available inputs to capture any nonlinear relationships between the inputs and the residual.

11. Fusion Model- Sub-Models

12. Fusion Model- Fusion Algorithm

16. Results- Cases  The results are analyzed for three cases. Sub-model parameters estimate Error covariance matrices estimate Models evaluate Case Iactual Case IIactualpseudo Case IIIactual pseudo

17. Results- cross-covariance  The cross-covariance matrix of sub-models forecast errors  Covariance of sub-models 2 to 4 increases when pseudo- weather forecast are used, indicates the degradation of the models due to weather forecast errors.

18. Results- weights

19. Results- MAPE of cases  Mean absolute percentage error(MAPE) Case I: Actual weatherCase II: pseudo weather forecast

20. Results- model performance

21. Conclusion  This paper examined the effect of weather forecast errors in load forecasting models, and found Gaussian distribution was not appropriate in this case.  The proposed method utilizes a combination of forecasts from several load forecasting models(sub-models) to minimizing the effect of weather forecast errors. And finally, the fusion model was shown successfully separate the tasks of model training and rejecting weather forecast errors.