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.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision Makingenergy tradingSchedulingSequential Decision MakingSimilar Papers 제목 키워드 기반
A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models
Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. While different models for short-term forecasting have been developed, open questions about…
Decision MakingTime Seriese-Values for Real-Time Residential Electricity Demand Forecast Model Selection
With the growing number of forecasting techniques and the increasing significance of forecast-based operation - particularly in the rapidly evolving energy sector - selecting the most effective forecasting model has beco…
Model SelectionTruthful Elicitation of Imprecise Forecasts
The quality of probabilistic forecasts is crucial for decision-making under uncertainty. While proper scoring rules incentivize truthful reporting of precise forecasts, they fall short when forecasters face epistemic unc…
Decision MakingDecision Making Under UncertaintyA framework for probabilistic weather forecast post-processing across models and lead times using machine learning
Forecasting the weather is an increasingly data intensive exercise. Numerical Weather Prediction (NWP) models are becoming more complex, with higher resolutions, and there are increasing numbers of different models in op…
BIG-bench Machine LearningDecision Makingquantile regressionProbabilistic Symmetry for Multi-Agent Dynamics
Learning multi-agent dynamics is a core AI problem with broad applications in robotics and autonomous driving. While most existing works focus on deterministic prediction, producing probabilistic forecasts to quantify un…
Autonomous DrivingCollision AvoidanceDecision MakingMotion Planning+4