As the U.S. electric power industry unbundles, the industry and its regulators grapple with two big questions concerning the degree to which distribution services should be unbundled. First, what...
Guessing Mother Nature's Next Move
What can be done to improve weather prediction and load forecasts?
total error. A subsequent test of the highest 30 days of error revealed that when load error was high, forecast weather was responsible for a substantially larger percentage of the load-model error. Future research must determine the reason for the other 60 percent of the load-model error that remains unexplained.
The index forecast improvement provided a marginal improvement of 6.41 percent for the entire model run. This improvement more than doubled to 15.76 percent when the analysis was conducted on the 30 largest error days. These conclusions indicate the potential value of improving weather forecast on the load model forecast.
Table 5 shows the incidence of average and extreme forecasts errors in the summer period from May through August of 2002. Of the 325 total hours during the peak periods of 1 p.m. to 5 p.m., close to one-third of the forecast error exceeded 3 percent. Some forecast errors were as high as 15 percent during critical peak days.
It is very difficult to estimate ISO-NE's cost or a true social cost for this error. However, during some of the severe error days, and one that even included an outage of a major base-load plants that resulted in expensive imports from Quebec, costs for this power approached $1,000/MWh.
At Cal-ISO, the accuracy of 13 commercial and public weather forecasting services was evaluated during the heavy load period from July through September of 2003. The accuracy was evaluated in terms of impact on electricity load forecasting; for example, the impact of a 1-degree error at 75 degrees is far less than a one degree error at 95 degrees. Therefore, average absolute megawatt error (at the time of highest daily temperature) was utilized as the measurement of forecast accuracy. Impacts of the load forecast model were removed, so that only weather forecast errors were considered.
The results of Table 6 showed a wide variation in error in both commercial services (730 MW to 3,397 MW average absolute error) and public services (616 MW to 922 MW average absolute error). In addition to the comparison below, several consensus forecasts of the best performers were evaluated, in which forecasts were combined by averaging. However, the consensus forecast accuracy was worse than the individual forecasts.
Several conclusions can be drawn from this analysis: 1) Due to the wide variation in forecast accuracy, forecasting services should be evaluated before selection for electricity-load forecasting; 2) Weather forecast accuracy should be continuously monitored and services improved or changed if necessary; and 3) Consensus forecasts apparently do not improve forecast accuracy.
Moreover, the commercial forecasters do not necessarily provide higher accuracy than public forecasts; however, they do provide many value-added services, such as hourly forecasts (instead of every 6 hours), data formatting for direct computer input, and hourly forecast resolution instead of public 3-hour resolution.
By analyzing the forecast errors on a daily basis, the Cal-ISO also found that most of the commercial and public forecasts consistently under-forecast major temperature warm-up events by a few degrees on the daily maximum. These few degrees are critical to electricity load forecasting.