Wind power forecasting will play a more important role in electrical system planning with the greater wind penetrations of the coming decades. Wind will most likely comprise a larger percentage of the generation mix, and as a result forecasting errors may have more significant effects on balancing operations. The natural uncertainties associated with wind along with limitations in numerical weather prediction (NWP) models lead to these forecasting errors, which play a considerable role in the impacts and costs of utility-scale wind integration. The premise of this project was to examine errors between the actual and commercially forecasted power production data from a typical wind power plant in the Northwestern United States. An exhaustive statistical characterization of the forecast behavior and error trends was undertaken, which allowed the most important metrics for describing wind power forecast errors to be identified. This paper presents only the metrics considered by the authors to be most significant. While basic information about wind forecast accuracy such as the mean absolute error (MAE) is valuable, a more detailed description is useful for system operators or in wind integration studies. System planners have expressed major concern in the area of forecast performance during large wind ramping events. For such reasons, this methodology included the development of a comprehensive ramp identification algorithm to select significant ramp events from the data record, and particular attention was paid to the error analysis during these events. The algorithm allows user input to select ramps of any desired magnitude, and also performs correlation analysis between forecasted ramp events and actual ramp events that coincide within a desired timing window. From this procedure, an investigation of the magnitude and phase of forecast errors was conducted for various forecast horizons. The metrics found to be of most importance for error characterization were selected based on overall impacts, and were ranked in a rudimentary (and perhaps subjective) order of significance. These metrics included: mean absolute error, root mean square error, average magnitude of step changes, standard deviation of step changes, mean bias levels, correlation coefficient of power values, mean temporal bias of ramp events, and others. While these metrics were selected and the methodology was developed for a single dataset, the entire process can be applied generally to any wind power and forecast time series. The implications for such a process include use for generating a synthetic wind power forecast for wind integration studies that will reproduce the same error trends as those found in a real forecast.

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