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Abstract

Techniques for near-term data filtering, smoothing and forecasting are described herein. In one example, data is received from supervisory control and data acquisition (SCADA) measurements available in an electrical grid. The data may be filtered according to a two-stage Kalman filter, which may include a ramp rate filter test and a load level filter test. The filtered data may then be smoothed according to an augmented Savitzky-Golay filter. Within the filter, a lift multiplier may correct for bias, which may have been introduced by load changes (e.g., an early morning increase in load). In one example, the lift multiplier may be calculated as a ratio between a smoothed load from a centered Savitzky-Golay moving average and a right hand side constrained Savitzky-Golay moving average. The filtered and smoothed data may be used in forming near-term forecast(s), which may be performed by autoregressive model(s).

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