Decision making with dynamic probabilistic forecasts

TL;DR

This paper develops stochastic models for dynamic probabilistic forecasts and demonstrates their application in optimizing decision making in renewable energy trading, particularly wind energy, based on evolving weather predictions.

Contribution

It introduces new stochastic models for evolving probabilistic forecasts and shows how to calibrate them from ensemble weather data for improved decision strategies.

Findings

Models effectively calibrated from ensemble forecasts

Optimized decision strategies improve wind energy trading outcomes

Framework adaptable to various weather-dependent decision processes

Abstract

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.