New baseload generation is needed in many areas of the United States, but financing new plants will be particularly challenging in restructured states where generation facilities are no longer...
Guessing Mother Nature's Next Move
What can be done to improve weather prediction and load forecasts?
of data. To isolate the component of the total error associated with the ANV MOS forecast, the total load error was partitioned into forecast-related error-or the error stemming from the accuracy of the previous day system-wide hourly pseudo-temperature forecast-and the model-related error, which contains errors associated with the fundamental capabilities of the ANNSTLF model itself and included the selection of stations used to develop systemwide forecasts, and stations weight determination (see Figure 3) .
The conclusion show:
- Forecasting error accounts, on average, for less than 40 percent of the total load forecast error. The majority of the error appears to lie within the models' load-assessment capabilities;
- The forecast component of the total load error and the total load error exhibit minimal correlation (r2 = 0.1900). This means that under-forecasting the temperature profile does not necessarily imply under-forecasting the load, and vice-versa; and
- The model component of the total load error and the total load error possessed a high degree of correlation (r 2 = 0.8564) (see Figure 4) .
Figure 5 provides a probability distribution histogram for the fraction of absolute forecast error amongst the total absolute error. The figure shows that AVN MOS absolute load forecast error in this analysis is approximately 40 percent of the total absolute load error.
Two model improvement runs then were conducted. The first model improvement run tested the improvement of the load model given a 1-degree improvement in temperature forecast. Because the ISO-NE load model uses an index combining dry bulb temperature and relative humidity in the summer and dry bulb temperature and wind speed, the improvement run was actually conducted using a one-increment improvement in the index. It also should be noted that on hours when the original index was equal to the observed, no improvement was possible. Thus, the total improvement over the course of the run averaged 0.7 rather than 1.0 incremental index units. Since temperature is the major contributor to the index, this approximation should not negate the temperature improvement objective.
The second improvement run was conducted substituting Bedford for Boston in an attempt to indicate the potential model improvement of a more representative station.
Mean Absolute Errors (MAE) were calculated for each of the model runs and compared to the baseline and perfect model-run MAE. These results are shown in Table 2.
The Bedford replacement of Boston showed virtually no improvement. The 6.41 percent improvement resulting from the 0.7-incremental index improvement was somewhat less than initially anticipated.
To evaluate the improvement that resulted in the high error cases when temperatures usually were outside the norm, an error analysis was conducted for the top 30 load-model error days in the data set. As shown in Table 2, most of the high-error days occurred during the summer, when power demand is high in the ISO-NE service region.
The results of the top 30 error days analysis is shown in Table 3.
The Bedford replacement of Boston still showed very little improvement, indicating this change in the model would not be useful. This analysis does indicate an increasing importance of accurate forecasts in