Decision making with dynamic probabilistic forecasts

We consider a sequential decision making process, such as renewable energy trading or electrical production scheduling, whose outcome depends on the future realization of a random factor, such as a meteorological variable. We assume that the decision maker disposes of a dynamically updated probabilistic forecast (predictive distribution) of the random factor. We propose several stochastic models for the evolution of the probabilistic forecast, and show how these models may be calibrated from ensemble forecasts, commonly provided by weather centers. We then show how these stochastic models can be used to determine optimal decision making strategies depending on the forecast updates. Applications to wind energy trading are given.

• Publication year

  2021

• Venue

  Unknown venue

• Publication date

  2021-06-30

• Fields of study

  Mathematics, Computer Science, Economics, Environmental Science

• Identifiers
  arXiv 2106.16047
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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