Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts

Key points

• Our motivation is to construct intra-day forecasts for renewable energy generation, which is a key operational task in modern power grids.1
• Starting with a large database of complete forecast-actual pairs across many assets, we adopt a functional point of view and construct a weighted neighborhood based on similarity metrics for the observed partial curve, the DA forecast, the day, and the geographical location.1
• The selected complete nearest-neighbor curves provide a full predictive distribution, including functional focal envelopes and predictive bands based on functional depth.1
• The hyperparameters governing neighborhood size, distances, and fusion rates are tuned across forecast update times and directly target probabilistic calibration in terms of the predictive probability integral transform.1

Sources (1)

• [1]Nearest Neighbor Functional Curve Completion for Intraday Probabilistic Revisions of Renewable Generation Forecasts
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 04:41 PM

  We propose a nonparametric approach that fuses deterministic initial forecasts and a partial realization into a probabilistic forecast using the framework of functional curve completion.

  Our motivation is to construct intra-day forecasts for renewable energy generation, which is a key operational task in modern power grids.

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