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Found 24 papers, 2 papers with code
Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects
We validate our approach in a simulation study for the German intraday electricity market and find that modelling the dependence structure improves the forecasting performance.
Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices
A fast C++ implementation of the proposed algorithm is provided in the open-source R-Package profoc on CRAN.
Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution
We validate our modelling by simulating price paths and compare the probabilistic forecasting performance of our model to benchmark models in a forecasting study for the German market.
Modeling Volatility and Dependence of European Carbon and Energy Prices
We study the prices of European Emission Allowances (EUA), whereby we analyze their uncertainty and dependencies on related energy prices (natural gas, coal, and oil).
Distributional neural networks for electricity price forecasting
We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks.
High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks
This holds regarding the competition month and the supplementary evaluation study, which covers an additional eleven months.
M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond
The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods.
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting
It contains four steps: i) data cleaning and preprocessing, ii) a holiday adjustment procedure, iii) training of individual forecasting models, iv) forecast combination by smoothed Bernstein Online Aggregation (BOA).
Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs
This paper addresses the question of how much to bid to maximize the profit when trading in two electricity markets: the hourly Day-Ahead Auction and the quarter-hourly Intraday Auction.
tsrobprep - an R package for robust preprocessing of time series data
Data cleaning is a crucial part of every data analysis exercise.
CRPS Learning
However, the quality of different forecasts may vary not only over time but also within the distribution.
Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories
Taking the German continuous hourly intraday market as an example, this paper derives an appropriate model for electricity trading.
The energy distance for ensemble and scenario reduction
We discuss the choice of energy distance in detail, especially in comparison to the popular Wasserstein distance which is dominating the scenario reduction literature.
Probabilistic Multi-Step-Ahead Short-Term Water Demand Forecasting with Lasso
Water demand is a highly important variable for operational control and decision making.
Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories
A generalized additive model is fitted to the price differences with the assumption that they follow a zero-inflated distribution, precisely a mixture of the Dirac and the Student's t-distributions.
Multivariate Forecasting Evaluation: On Sensitive and Strictly Proper Scoring Rules
The results also show that a proposed copula score provides very strong distinction between models with correct and incorrect dependency structure.
Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets
We discuss a concept denoted as Conformal Prediction (CP) in this paper.
Quantile Regression for Qualifying Match of GEFCom2017 Probabilistic Load Forecasting
The hourly load data is log transformed and split into a long-term trend component and a remainder term.
Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
We conduct an extensive empirical study on short-term electricity price forecasting (EPF) to address the long-standing question if the optimal model structure for EPF is univariate or multivariate.
Forecasting wind power - Modeling periodic and non-linear effects under conditional heteroscedasticity
In this article we present an approach that enables joint wind speed and wind power forecasts for a wind park.
Lasso estimation for GEFCom2014 probabilistic electric load forecasting
We present a methodology for probabilistic load forecasting that is based on lasso (least absolute shrinkage and selection operator) estimation.
Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes
Shrinkage algorithms are of great importance in almost every area of statistics due to the increasing impact of big data.
Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets
In our paper we analyze the relationship between the day-ahead electricity price of the Energy Exchange Austria (EXAA) and other day-ahead electricity prices in Europe.
Efficient Modeling and Forecasting of the Electricity Spot Price
The increasing importance of renewable energy, especially solar and wind power, has led to new forces in the formation of electricity prices.
