IDEAS home Printed from https://ideas.repec.org/f/pse644.html
   My authors  Follow this author

Tomasz Serafin

Personal Details

First Name:Tomasz
Middle Name:
Last Name:Serafin
Suffix:
RePEc Short-ID:pse644
[This author has chosen not to make the email address public]
Terminal Degree:2020 Instytut Matematyki i Informatyki; Politechnika Wrocławska (from RePEc Genealogy)

Affiliation

Katedra Badań Operacyjnych i Inteligencji Biznesowej
Politechnika Wrocławska

Wrocław, Poland
http://kbo.pwr.edu.pl/
RePEc:edi:kbpwrpl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  2. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  3. Zbigniew Palmowski & Tomasz Serafin, 2020. "Note on simulation pricing of $\pi$-options," Papers 2007.02076, arXiv.org, revised Aug 2020.
  4. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
  5. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.
  6. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
  7. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.

Articles

  1. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
  2. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
  3. Zbigniew Palmowski & Tomasz Serafin, 2020. "A Note on Simulation Pricing of π -Options," Risks, MDPI, vol. 8(3), pages 1-19, August.
  4. Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019. "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 12(13), pages 1-12, July.
  5. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

    Cited by:

    1. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    2. Julia Nasiadka & Weronika Nitka & Rafa{l} Weron, 2022. "Calibration window selection based on change-point detection for forecasting electricity prices," Papers 2204.00872, arXiv.org.
    3. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    4. Jun Dong & Xihao Dou & Aruhan Bao & Yaoyu Zhang & Dongran Liu, 2022. "Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China," Sustainability, MDPI, vol. 14(13), pages 1-24, June.

  2. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

    Cited by:

    1. Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
    2. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    3. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    4. Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
    5. Paul Ghelasi & Florian Ziel, 2024. "From day-ahead to mid and long-term horizons with econometric electricity price forecasting models," Papers 2406.00326, arXiv.org, revised Aug 2024.
    6. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    7. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    8. Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
    9. Rainer Baule & Michael Naumann, 2021. "Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market," Energies, MDPI, vol. 14(22), pages 1-24, November.
    10. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    11. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.
    12. Jonathan Berrisch & Florian Ziel, 2021. "CRPS Learning," Papers 2102.00968, arXiv.org, revised Nov 2021.
    13. Katarzyna Maciejowska & Weronika Nitka, 2024. "Multiple split approach -- multidimensional probabilistic forecasting of electricity markets," Papers 2407.07795, arXiv.org.
    14. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    15. Berrisch, Jonathan & Ziel, Florian, 2023. "CRPS learning," Journal of Econometrics, Elsevier, vol. 237(2).
    16. Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.

  3. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

    Cited by:

    1. Krishna, Attoti Bharath & Abhyankar, Abhijit R., 2023. "Time-coupled day-ahead wind power scenario generation: A combined regular vine copula and variance reduction method," Energy, Elsevier, vol. 265(C).
    2. Cramer, Eike & Witthaut, Dirk & Mitsos, Alexander & Dahmen, Manuel, 2023. "Multivariate probabilistic forecasting of intraday electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 346(C).
    3. Simon Hirsch & Florian Ziel, 2023. "Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects," Papers 2306.13419, arXiv.org.
    4. Rainer Baule & Michael Naumann, 2022. "Flexible Short-Term Electricity Certificates—An Analysis of Trading Strategies on the Continuous Intraday Market," Energies, MDPI, vol. 15(17), pages 1-28, August.

  4. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.

    Cited by:

    1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
    2. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
    3. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    4. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    5. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    6. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    7. Jozef Barunik & Lubos Hanus, 2023. "Learning Probability Distributions of Day-Ahead Electricity Prices," Papers 2310.02867, arXiv.org, revised Oct 2023.
    8. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    9. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    10. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    11. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    12. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.
    13. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    14. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    15. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    16. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.
    17. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    18. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    19. Sheybanivaziri, Samaneh & Le Dréau, Jérôme & Kazmi, Hussain, 2024. "Forecasting price spikes in day-ahead electricity markets: techniques, challenges, and the road ahead," Discussion Papers 2024/1, Norwegian School of Economics, Department of Business and Management Science.
    20. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    21. Grzegorz Marcjasz, 2020. "Forecasting Electricity Prices Using Deep Neural Networks: A Robust Hyper-Parameter Selection Scheme," Energies, MDPI, vol. 13(18), pages 1-18, September.
    22. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    23. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Enhancing load, wind and solar generation forecasts in day-ahead forecasting of spot and intraday electricity prices," HSC Research Reports HSC/19/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    24. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
    25. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
    26. Gökgöz, Fazıl & Yücel, Öykü, 2024. "Merit-order of dispatchable and variable renewable energy sources in Turkey's day-ahead electricity market," Utilities Policy, Elsevier, vol. 88(C).

  5. Grzegorz Marcjasz & Tomasz Serafin & Rafal Weron, 2018. "Selection of calibration windows for day-ahead electricity price forecasting," HSC Research Reports HSC/18/06, Hugo Steinhaus Center, Wroclaw University of Science and Technology.

    Cited by:

    1. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    2. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
    3. Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
    4. Bartosz Uniejewski & Katarzyna Maciejowska, 2022. "LASSO Principal Component Averaging -- a fully automated approach for point forecast pooling," Papers 2207.04794, arXiv.org.
    5. Jonathan Berrisch & Florian Ziel, 2023. "Multivariate Probabilistic CRPS Learning with an Application to Day-Ahead Electricity Prices," Papers 2303.10019, arXiv.org, revised Feb 2024.
    6. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
    7. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    8. Uniejewski, Bartosz & Weron, Rafał, 2021. "Regularized quantile regression averaging for probabilistic electricity price forecasting," Energy Economics, Elsevier, vol. 95(C).
    9. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    10. Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
    11. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    12. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA forecast averaging - predicting day-ahead and intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/02, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    13. Tomasz Serafin & Bartosz Uniejewski & Rafal Weron, 2019. "Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting," WORking papers in Management Science (WORMS) WORMS/19/08, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, revised 06 Jul 2019.
    14. Emma Viviani & Luca Di Persio & Matthias Ehrhardt, 2021. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case," Energies, MDPI, vol. 14(2), pages 1-33, January.
    15. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    16. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemyslaw Zaleski & Rafal Weron, 2019. "Balancing RES generation: Profitability of an energy trader," HSC Research Reports HSC/19/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    17. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    18. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    19. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    20. Philip Beran & Arne Vogler, 2021. "Multi-Day-Ahead Electricity Price Forecasting: A Comparison of fundamental, econometric and hybrid Models," EWL Working Papers 2102, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Oct 2021.
    21. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    22. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    23. Rodrigo A. de Marcos & Antonio Bello & Javier Reneses, 2019. "Short-Term Electricity Price Forecasting with a Composite Fundamental-Econometric Hybrid Methodology," Energies, MDPI, vol. 12(6), pages 1-15, March.
    24. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    25. Carlo Fezzi & Luca Mosetti, 2018. "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers 2018/10, Department of Economics and Management.
    26. Kath, Christopher & Ziel, Florian, 2021. "Conformal prediction interval estimation and applications to day-ahead and intraday power markets," International Journal of Forecasting, Elsevier, vol. 37(2), pages 777-799.
    27. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
    28. Miguel Pinhão & Miguel Fonseca & Ricardo Covas, 2022. "Electricity Spot Price Forecast by Modelling Supply and Demand Curve," Mathematics, MDPI, vol. 10(12), pages 1-20, June.
    29. Grzegorz Marcjasz, 2020. "Forecasting Electricity Prices Using Deep Neural Networks: A Robust Hyper-Parameter Selection Scheme," Energies, MDPI, vol. 13(18), pages 1-18, September.
    30. Thomas Mobius & Mira Watermeyer & Oliver Grothe & Felix Musgens, 2023. "Enhancing Energy System Models Using Better Load Forecasts," Papers 2302.11017, arXiv.org.
    31. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
    32. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Enhancing load, wind and solar generation forecasts in day-ahead forecasting of spot and intraday electricity prices," HSC Research Reports HSC/19/08, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    33. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.
    34. Rodrigo A. de Marcos & Derek W. Bunn & Antonio Bello & Javier Reneses, 2020. "Short-Term Electricity Price Forecasting with Recurrent Regimes and Structural Breaks," Energies, MDPI, vol. 13(20), pages 1-14, October.
    35. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).

Articles

  1. Christopher Kath & Weronika Nitka & Tomasz Serafin & Tomasz Weron & Przemysław Zaleski & Rafał Weron, 2020. "Balancing Generation from Renewable Energy Sources: Profitability of an Energy Trader," Energies, MDPI, vol. 13(1), pages 1-15, January.

    Cited by:

    1. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    2. Grzegorz Zimon & Dominik Zimon, 2020. "The Impact of Purchasing Group on the Profitability of Companies Operating in the Renewable Energy Sector—The Case of Poland," Energies, MDPI, vol. 13(24), pages 1-15, December.
    3. Skrzypczak, Dawid & Trzaska, Krzysztof & Mikula, Katarzyna & Gil, Filip & Izydorczyk, Grzegorz & Mironiuk, Małgorzata & Polomska, Xymena & Moustakas, Konstantinos & Witek-Krowiak, Anna & Chojnacka, Ka, 2023. "Conversion of anaerobic digestates from biogas plants: Laboratory fertilizer formulation, scale-up and demonstration of applicative properties on plants," Renewable Energy, Elsevier, vol. 203(C), pages 506-517.
    4. Lu Zhu & Lanli Hu & Serhat Yüksel & Hasan Dinçer & Hüsne Karakuş & Gözde Gülseven Ubay, 2020. "Analysis of Strategic Directions in Sustainable Hydrogen Investment Decisions," Sustainability, MDPI, vol. 12(11), pages 1-19, June.
    5. Grzegorz Zimon & Hossein Tarighi & Mahdi Salehi & Adam Sadowski, 2022. "Assessment of Financial Security of SMEs Operating in the Renewable Energy Industry during COVID-19 Pandemic," Energies, MDPI, vol. 15(24), pages 1-18, December.
    6. Adam Sulich & Letycja Sołoducho-Pelc, 2021. "Renewable Energy Producers’ Strategies in the Visegrád Group Countries," Energies, MDPI, vol. 14(11), pages 1-21, May.
    7. Grzegorz Lew & Beata Sadowska & Katarzyna Chudy-Laskowska & Grzegorz Zimon & Magdalena Wójcik-Jurkiewicz, 2021. "Influence of Photovoltaic Development on Decarbonization of Power Generation—Example of Poland," Energies, MDPI, vol. 14(22), pages 1-20, November.
    8. Tomasz Serafin & Grzegorz Marcjasz & Rafal Weron, 2020. "Trading on short-term path forecasts of intraday electricity prices," WORking papers in Management Science (WORMS) WORMS/20/17, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
    9. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    10. Weronika Nitka & Tomasz Serafin & Dimitrios Sotiros, 2021. "Forecasting Electricity Prices: Autoregressive Hybrid Nearest Neighbors (ARHNN) method," WORking papers in Management Science (WORMS) WORMS/21/06, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.

  2. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    See citations under working paper version above.
  3. Tomasz Serafin & Bartosz Uniejewski & Rafał Weron, 2019. "Averaging Predictive Distributions Across Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 12(13), pages 1-12, July.
    See citations under working paper version above.
  4. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    See citations under working paper version above.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ENE: Energy Economics (6) 2018-09-10 2019-12-16 2020-03-02 2020-05-18 2021-01-25 2021-04-19. Author is listed
  2. NEP-FOR: Forecasting (5) 2018-09-10 2020-03-02 2020-05-18 2021-01-25 2021-04-19. Author is listed
  3. NEP-REG: Regulation (2) 2018-09-10 2019-12-16. Author is listed
  4. NEP-BIG: Big Data (1) 2018-09-10
  5. NEP-CMP: Computational Economics (1) 2020-09-14
  6. NEP-MST: Market Microstructure (1) 2021-01-25
  7. NEP-ORE: Operations Research (1) 2019-12-16
  8. NEP-RMG: Risk Management (1) 2020-09-14

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Tomasz Serafin should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.