R
Rafał Weron
Researcher at Wrocław University of Technology
Publications - 291
Citations - 13851
Rafał Weron is an academic researcher from Wrocław University of Technology. The author has contributed to research in topics: Electricity price forecasting & Spot contract. The author has an hindex of 58, co-authored 285 publications receiving 12058 citations. Previous affiliations of Rafał Weron include University of Wrocław.
Papers
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Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this paper, a review article aims to explain the complexity of available solutions, their strengths and weaknesses, and the opportunities and threats that the forecasting tools offer or that may be encountered.
Posted Content
Electricity price forecasting: A review of the state-of-the-art with a look into the future
TL;DR: In this article, a review article aims at explaining the complexity of available solutions, their strengths and weaknesses, and the opportunities and treats that the forecasting tools offer or that may be encountered.
Book
Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach
TL;DR: In this paper, the authors present a case study of the electricity market in the UK and Australia, showing that electricity prices in both countries are correlated with the number of customers and the amount of electricity consumed.
Journal ArticleDOI
Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models
Rafał Weron,Adam Misiorek +1 more
TL;DR: In this article, the authors compared the performance of 12 time series methods for short-term (day-ahead) spot price forecasting in auction-type electricity markets, including spike preprocessed, threshold and semiparametric autoregressions, as well as mean-reverting jump diffusions.
Journal ArticleDOI
Estimating long-range dependence: finite sample properties and confidence intervals
TL;DR: In this paper, the authors test R/S analysis, Detrended Fluctuation Analysis and periodogram regression methods on samples drawn from Gaussian white noise, and the DFA statistics turns out to be the unanimous winner.