From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting

Modeling price risks is crucial for economic decision making in energy markets. Besides the risk of a single price, the dependence structure of multiple prices is often relevant. We therefore propose a generic and easy-to-implement method for creating multivariate probabilistic forecasts based on univariate point forecasts of day-ahead electricity prices. While each univariate point forecast refers to one of the day's 24 hours, the multivariate forecast distribution models dependencies across hours. The proposed method is based on simple copula techniques and an optional time series component. We illustrate the method for five benchmark data sets recently provided by Lago et al. (2020). Furthermore, we demonstrate an example for constructing realistic prediction intervals for the weighted sum of consecutive electricity prices, as, e.g., needed for pricing individual load profiles

A multivariate extension of the Lorenz curve based on copulas and a related multivariate Gini coefficient

We propose an extension of the univariate Lorenz curve and of the Gini coefficient to the multivariate case, i.e., to simultaneously measure inequality in more than one variable. Our extensions are based on copulas and measure inequality stemming from inequality in every single variable as well as inequality stemming from the dependence structure of the variables. We derive simple nonparametric estimators for both instruments and apply them exemplary to data of individual income and wealth for various countries

A multivariate extension of the Lorenz curve based on copulas and a related multivariate Gini coefficient

We propose an extension of the univariate Lorenz curve and of the Gini coefficient to the multivariate case, i.e., to simultaneously measure inequality in more than one variable. Our extensions are based on copulas and measure inequality stemming from inequality in each single variable as well as inequality stemming from the dependence structure of the variables. We derive simple nonparametric estimators for both instruments and exemplary apply them to data of individual income and wealth for various countries

A multivariate extension of the Lorenz curve based on copulas and a related multivariate Gini coefficient

We propose an extension of the univariate Lorenz curve and of the Gini coefficient to the multivariate case, i.e., to simultaneously measure inequality in more than one variable. Our extensions are based on copulas and measure inequality stemming from inequality in every single variable as well as inequality stemming from the dependence structure of the variables. We derive simple nonparametric estimators for both instruments and apply them exemplary to data of individual income and wealth for various countries.Comment: 17 pages,5 figure

Analyzing Europe’s Biggest Offshore Wind Farms: A Data Set with 40 Years of Hourly Wind Speeds and Electricity Production

We provide an open, available, and ready-to-use data set covering 40 years of hourly wind speeds and synthetic hourly production signals for the 29 biggest offshore wind farms in Europe. It enables researchers and industry experts to include realistic offshore time series into their analyses. In particular, we provide data from 1980 to 2019 for wind farms already in operation and those that will be in operation by 2024. We document in detail how the data set was generated from publicly available sources and provide manually collected details on the wind farms, such as the turbine power curves. Correspondingly, the users can easily keep the data set up to date and add further wind farm locations as needed. We give a descriptive analysis of the data and its correlation structure and find a relatively high volatility and intermittency for single locations, with balancing effects across wind farms

Data Set for the paper "High-Resolution Working Layouts and Time Series for Renewable Energy Generation in Europe: A Data-Driven Approach for Accurate Fore- and Nowcasting"

The data in this repository consists of 7 files. This includes a readme file [readme.txt], three zip-files including onshore wind, offshore wind and photovoltaic layouts for each year and network nodes per country [offshore_wind_working_layouts.zip], [onshore_wind_working_layouts.zip], [PV_working_layouts.zip], and three zip-files including onshore wind, offshore wind and photovoltaic generation time series for each country and year [offshore_wind_generation.zip], [onshore_wind_generation.zip], [PV_generation.zip]. Each file can be downloaded separately or collectively by clicking the "Download all"-Button. </p