An introduction to multivariate probabilistic forecast evaluation

  title={An introduction to multivariate probabilistic forecast evaluation},
  author={Mathias Blicher Bjerreg{\aa}rd and Jan Kloppenborg M{\o}ller and Henrik Madsen},

Recent developments in multivariate wind and solar power forecasting

The intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and

A review of probabilistic forecasting and prediction with machine learning

The topic of predictive uncertainty estimation with machine learning algorithms, as well as the related metrics (consistent scoring functions and proper scoring rules) for assessing probabilistic predictions are reviewed, sparking understanding on how to develop new algorithms tailored to users’ needs.

onlineforecast: An R package for adaptive and recursive forecasting

The R package onlineforecast is presented that provides a generalized setup of data and models for online forecasting that has functionality for time-adaptive shifting of dynamical and non-linear models.

Data-driven uncertainty quantification for constrained stochastic differential equations and application to solar photovoltaic power forecast data

In this work, we extend the data-driven It\^{o} stochastic differential equation (SDE) framework for the pathwise assessment of short-term forecast errors to account for the time-dependent upper

Interpretable Deep Learning for Probabilistic MJO Prediction

The Madden‐Julian oscillation (MJO) is the dominant source of sub‐seasonal variability in the tropics. It consists of an Eastward moving region of enhanced convection coupled to changes in zonal

The COVID-19 shock and challenges for inflation modelling



Assessing probabilistic forecasts of multivariate quantities, with an application to ensemble predictions of surface winds

We discuss methods for the evaluation of probabilistic predictions of vector-valued quantities, that can take the form of a discrete forecast ensemble or a density forecast. In particular, we propose

Non‐parametric probabilistic forecasts of wind power: required properties and evaluation

Predictions of wind power production for horizons up to 48-72 h ahead comprise a highly valuable input to the methods for the daily management or trading of wind generation. Today, users of wind

Leveraging stochastic differential equations for probabilistic forecasting of wind power using a dynamic power curve

Short-term (hours to days) probabilistic forecasts of wind power generation provide useful information about the associated uncertainty of these forecasts. Standard probabilistic forecasts are

Variogram-Based Proper Scoring Rules for Probabilistic Forecasts of Multivariate Quantities*

AbstractProper scoring rules provide a theoretically principled framework for the quantitative assessment of the predictive performance of probabilistic forecasts. While a wide selection of such

Probabilistic forecasts of solar irradiance using stochastic differential equations

Probabilistic forecasts of renewable energy production provide users with valuable information about the uncertainty associated with the expected generation. Current state‐of‐the‐art forecasts for

Probabilistic Forecasts of Wind Power Generation by Stochastic Differential Equation Models

The increasing penetration of wind power has resulted in larger shares of volatile sources of supply in power systems worldwide. In order to operate such systems efficiently, methods for reliable

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

Around the world wind energy is starting to become a major energy provider in electricity markets, as well as participating in ancillary services markets to help maintain grid stability. The

Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling

It is shown that seemingly unrelated, recent advances can be interpreted, fused and consolidated within the framework of ECC, the common thread being the adoption of the empirical copula of the raw ensemble.