244 citations · 2k across the 53 of their papers we have counts for
53 papers
Networked Spatial Effects in European Electricity Price Forecasting
Sultan Mahmud Chomon, Florian Ziel
As European bidding zones are highly interconnected by physical transmission lines, spatial influences propagate across neighboring nodes through a network. It is reflected in the…
Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration
Btissame El Mahtout, Florian Ziel
We propose a novel Mixture-of-Experts framework, referred to as Expert Loss Integration MoE (EliMoE), for time series forecasting. EliMoE addresses the optimization problem arising…
Energy-Arena: A Dynamic Benchmark for Operational Energy Forecasting
Max Kleinebrahm, Jonathan Berrisch, Philipp Eiser +11
Energy forecasting research faces a persistent comparability gap that makes it difficult to measure consistent progress over time. Reported accuracy gains are often not directly co…
Probabilistic Forecasting for Day-ahead Electricity Prices, Battery Trading Strategies and the Economic Evaluation of Predictive Accuracy
Simon Hirsch, Florian Ziel
Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, t…
Recurrent Neural Networks with Linear Structures for Electricity Price Forecasting
Souhir Ben Amor, Florian Ziel
We present a novel recurrent neural network architecture specifically designed for day-ahead electricity price forecasting, aimed at improving short-term decision-making and operat…
Electricity Price Forecasting: Bridging Linear Models, Neural Networks and Online Learning
Btissame El Mahtout, Florian Ziel
Precise day-ahead forecasts for electricity prices are crucial to ensure efficient portfolio management, support strategic decision-making for power plant operations, and enable ef…
The role of probabilistic load and renewable prediction in enhancing day-ahead electricity price forecasts
Bartosz Uniejewski, Florian Ziel
The increasing penetration of renewable energy sources (RES) has amplified volatility in electricity markets, while demand variability continues to challenge system stability. Trad…
A data-driven merit order: Learning a fundamental electricity price model
Paul Ghelasi, Florian Ziel
Electricity price forecasting approaches generally fall into two categories: data-driven models, which learn from historical patterns, or fundamental models, which simulate market…
Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting
Monika Zimmermann, Florian Ziel
Accurate forecasts of the impact of spatial weather and pan-European socio-economic and political risks on hourly electricity demand for the mid-term horizon are crucial for strate…
Online Distributional Regression
Simon Hirsch, Jonathan Berrisch, Florian Ziel
Large-scale streaming data are common in modern machine learning applications and have led to the development of online learning algorithms. Many fields, such as supply chain manag…
From day-ahead to mid and long-term horizons with econometric electricity price forecasting models
Paul Ghelasi, Florian Ziel
The recent energy crisis starting in 2021 led to record-high gas, coal, carbon and power prices, with electricity reaching up to 40 times the pre-crisis average. This had dramatic…
Assessing the impact of weather-induced uncertainties in large-scale electricity systems
Jan Peper, David Kröger, Jonathan Kipp +2
The future energy system will largely depend on volatile renewable energy sources and temperature-dependent loads, which makes the weather a central influencing factor. This articl…
Efficient mid-term forecasting of hourly electricity load using generalized additive models
Monika Zimmermann, Florian Ziel
Accurate mid-term (weeks to one year) hourly electricity load forecasts are essential for strategic decision-making in power plant operation, ensuring supply security and grid stab…
Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects
Simon Hirsch, Florian Ziel
Intraday electricity markets play an increasingly important role in balancing the intermittent generation of renewable energy resources, which creates a need for accurate probabili…
Hierarchical forecasting for aggregated curves with an application to day-ahead electricity price auctions
Paul Ghelasi, Florian Ziel
Aggregated curves are common structures in economics and finance, and the most prominent examples are supply and demand curves. In this study, we exploit the fact that all aggregat…
Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices
Jonathan Berrisch, Florian Ziel
This paper presents a new method for combining (or aggregating or ensembling) multivariate probabilistic forecasts, considering dependencies between quantiles and marginals through…
Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution
Simon Hirsch, Florian Ziel
During the last years, European intraday power markets have gained importance for balancing forecast errors due to the rising volumes of intermittent renewable generation. However,…
Modeling Volatility and Dependence of European Carbon and Energy Prices
Jonathan Berrisch, Sven Pappert, Florian Ziel +1
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). We propose a…
Distributional neural networks for electricity price forecasting
Grzegorz Marcjasz, Michał Narajewski, Rafał Weron +1
We present a novel approach to probabilistic electricity price forecasting which utilizes distributional neural networks. The model structure is based on a deep neural network that…
High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks
Jonathan Berrisch, Michał Narajewski, Florian Ziel
This paper covers predicting high-resolution electricity peak demand features given lower-resolution data. This is a relevant setup as it answers whether limited higher-resolution…
Smoothed Bernstein Online Aggregation for Day-Ahead Electricity Demand Forecasting
Florian Ziel
We present a winning method of the IEEE DataPort Competition on Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm. The day-ahead load forecasting approach is based on o…
M5 Competition Uncertainty: Overdispersion, distributional forecasting, GAMLSS and beyond
Florian Ziel
The M5 competition uncertainty track aims for probabilistic forecasting of sales of thousands of Walmart retail goods. We show that the M5 competition data faces strong overdispers…
Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs
Michał Narajewski, Florian Ziel
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 Au…
tsrobprep - an R package for robust preprocessing of time series data
Michał Narajewski, Jens Kley-Holsteg, Florian Ziel
Data cleaning is a crucial part of every data analysis exercise. Yet, the currently available R packages do not provide fast and robust methods for cleaning and preparation of time…