Nothing Special   »   [go: up one dir, main page]

create a website
Probabilistic forecasting of German electricity imbalance prices. (2022). Narajewski, Michal.
In: Papers.
RePEc:arx:papers:2205.11439.

Full description at Econpapers || Download paper

Cited: 3

Citations received by this document

Cites: 56

References cited by this document

Cocites: 42

Documents which have cited the same bibliography

Coauthors: 0

Authors who have wrote about the same topic

Citations

Citations received by this document

  1. Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices. (2023). Uniejewski, Bartosz.
    In: Papers.
    RePEc:arx:papers:2302.00411.

    Full description at Econpapers || Download paper

  2. Price Forecasting in Energy Market. (2022). Plastun, Alex ; Kozmenko, Serhiy ; Bilan, Yuriy.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:24:p:9625-:d:1007804.

    Full description at Econpapers || Download paper

References

References cited by this document

  1. A. Gianfreda and D. Bunn. A stochastic latent moment model for electricity price formation. Operations Research, 66(5):1189–1203, 2018.

  2. A. Kramer and R. Kiesel. Exogenous factors for order arrivals on the intraday electricity market. Energy Economics, 97:105186, 2021.

  3. A. Lucas, K. Pegios, E. Kotsakis, and D. Clarke. Price forecasting for the balancing energy market using machine-learning regression. Energies, 13(20):5420, 2020.

  4. B. Efron. Bootstrap Methods: Another Look at the Jackknife. The Annals of Statistics, pages 1–26, 1979.
    Paper not yet in RePEc: Add citation now
  5. B. Uniejewski and R. Weron. Regularized quantile regression averaging for probabilistic electricity price forecasting. Energy Economics, 95:105121, 2021.

  6. B. Uniejewski, G. Marcjasz, and R. Weron. Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO. International Journal of Forecasting, 35(4):1533–1547, 2019.

  7. B. Uniejewski, R. Weron, and F. Ziel. Variance stabilizing transformations for electricity spot price forecasting. IEEE Transactions on Power Systems, 33(2):2219–2229, 2017.

  8. C. Kath. Modeling intraday markets under the new advances of the cross-border intraday project (XBID): Evidence from the German intraday market. Energies, 12 (22):4339, 2019.

  9. C. Koch and L. Hirth. Short-term electricity trading for system balancing: An empirical analysis of the role of intraday trading in balancing Germany’s electricity system. Renewable and Sustainable Energy Reviews, 113:109275, 2019.

  10. D. M. Stasinopoulos and R. A. Rigby. Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23:1–46, 2008.
    Paper not yet in RePEc: Add citation now
  11. D. W. Bunn and S. O. Kermer. Statistical arbitrage and information flow in an electricity balancing market. The Energy Journal, 42(5), 2021.

  12. D. W. Bunn, A. Gianfreda, and S. Kermer. A trading-based evaluation of density forecasts in a real-time electricity market. Energies, 11(10):2658, 2018.

  13. F. Diebold and R. Mariano. Comparing Predictive Accuracy. Journal of Business & Economic Statistics, 13(3):253–63, 1995.

  14. F. Ocker and K.-M. Ehrhart. The “German Paradox” in the balancing power markets. Renewable and Sustainable Energy Reviews, 67:892–898, 2017.
    Paper not yet in RePEc: Add citation now
  15. F. Ziel and R. Weron. Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks. Energy Economics, 70: 396–420, 2018.

  16. F. Ziel, C. Croonenbroeck, and D. Ambach. Forecasting wind power–modeling periodic and non-linear effects under conditional heteroscedasticity. Applied Energy, 177:285– 297, 2016.

  17. F. Ziel, P. Muniain, and M. Stasinopoulos. Extra lasso-type additive terms for gamlss. 2021.
    Paper not yet in RePEc: Add citation now
  18. F. Ziel. Forecasting electricity spot prices using lasso: On capturing the autoregressive intraday structure. IEEE Transactions on Power Systems, 31(6):4977–4987, 2016.

  19. F. Ziel. M5 competition uncertainty: Overdispersion, distributional forecasting, GAMLSS, and beyond. International Journal of Forecasting, 2021.
    Paper not yet in RePEc: Add citation now
  20. G. Klæboe, A. L. Eriksrud, and S.-E. Fleten. Benchmarking time series based forecasting models for electricity balancing market prices. Energy Systems, 6(1):43–61, 2015.
    Paper not yet in RePEc: Add citation now
  21. G. Marcjasz, B. Uniejewski, and R. Weron. Beating the naı̈ve—Combining LASSO with naı̈ve intraday electricity price forecasts. Energies, 13(7):1667, 2020.
    Paper not yet in RePEc: Add citation now
  22. G. Marcjasz, M. Narajewski, R. Weron, and F. Ziel. Distributional neural networks for electricity price forecasting. working paper, 2022.
    Paper not yet in RePEc: Add citation now
  23. G. Marcjasz, T. Serafin, and R. Weron. Selection of calibration windows for day-ahead electricity price forecasting. Energies, 11(9):2364, 2018.

  24. I. Oksuz and U. Ugurlu. Neural network based model comparison for intraday electricity price forecasting. Energies, 12(23):4557, 2019.

  25. J. Berrisch and F. Ziel. CRPS learning. Journal of Econometrics, 2021.
    Paper not yet in RePEc: Add citation now
  26. J. Bottieau, L. Hubert, Z. De Grève, F. Vallée, and J.-F. Toubeau. Very-short-term probabilistic forecasting for a risk-aware participation in the single price imbalance settlement. IEEE Transactions on Power Systems, 35(2):1218–1230, 2019.
    Paper not yet in RePEc: Add citation now
  27. J. Browell. Risk constrained trading strategies for stochastic generation with a singleprice balancing market. Energies, 11(6):1345, 2018.

  28. J. Dumas, I. Boukas, M. M. de Villena, S. Mathieu, and B. Cornélusse. Probabilistic Forecasting of Imbalance Prices in the Belgian Context. In 2019 16th International Conference on the European Energy Market (EEM), pages 1–7. IEEE, 2019.
    Paper not yet in RePEc: Add citation now
  29. J. Lago, F. De Ridder, P. Vrancx, and B. De Schutter. Forecasting day-ahead electricity prices in Europe: the importance of considering market integration. Applied energy, 211:890–903, 2018.

  30. J. Lago, G. Marcjasz, B. De Schutter, and R. Weron. Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark. Applied Energy, 293:116983, 2021.

  31. J. Nowotarski and R. Weron. Computing electricity spot price prediction intervals using quantile regression and forecast averaging. Computational Statistics, 30(3):791– 803, 2015.

  32. J. Nowotarski and R. Weron. Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, 81:1548–1568, 2018.
    Paper not yet in RePEc: Add citation now
  33. J. Viehmann. State of the German Short-Term Power Market. Zeitschrift für Energiewirtschaft, 41(2):87–103, Jun 2017.
    Paper not yet in RePEc: Add citation now
  34. J.-F. Toubeau, J. Bottieau, Y. Wang, and F. Vallee. Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems. IEEE Transactions on Sustainable Energy, 2021.
    Paper not yet in RePEc: Add citation now
  35. K. Maciejowska, J. Nowotarski, and R. Weron. Probabilistic forecasting of electricity spot prices using Factor Quantile Regression Averaging. International Journal of Forecasting, 32(3):957–965, 2016.

  36. K. Maciejowska. Assessing the impact of renewable energy sources on the electricity price level and variability–A quantile regression approach. Energy Economics, 85: 104532, 2020.

  37. K. Poplavskaya, J. Lago, and L. De Vries. Effect of market design on strategic bidding behavior: Model-based analysis of European electricity balancing markets. Applied Energy, 270:115130, 2020.

  38. M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015. URL https://www. tensorflow.org/. Software available from tensorflow.org.
    Paper not yet in RePEc: Add citation now
  39. M. Kremer, R. Kiesel, and F. Paraschiv. An econometric model for intraday electricity trading. Philosophical Transactions of the Royal Society A, 379(2202):20190624, 2021.
    Paper not yet in RePEc: Add citation now
  40. M. Kremer, R. Kiesel, and F. Paraschiv. Intraday electricity pricing of night contracts. Energies, 13(17):4501, 2020.

  41. M. Narajewski and F. Ziel. Econometric modelling and forecasting of intraday electricity prices. Journal of Commodity Markets, 19:100107, 2020.

  42. M. Narajewski and F. Ziel. Ensemble forecasting for intraday electricity prices: Simulating trajectories. Applied Energy, 279:115801, 2020.

  43. M. Narajewski and F. Ziel. Estimation and simulation of the transaction arrival process in intraday electricity markets. Energies, 12(23):4518, 2019.

  44. M. Narajewski and F. Ziel. Optimal bidding on hourly and quarter-hourly day-ahead electricity price auctions: trading large volumes of power with market impact and transaction costs. arXiv preprint arXiv:2104.14204, 2021.
    Paper not yet in RePEc: Add citation now
  45. M. Narajewski, J. Kley-Holsteg, and F. Ziel. tsrobprep — an R package for robust preprocessing of time series data. SoftwareX, 16:100809, 2021.
    Paper not yet in RePEc: Add citation now
  46. Method for determining the reBAP – regelleistung.net. https://www.regelleistung. net/ext/static/rebap?lang=en. Accessed: 2022-02-18.
    Paper not yet in RePEc: Add citation now
  47. N. Kumbartzky, M. Schacht, K. Schulz, and B. Werners. Optimal operation of a CHP plant participating in the German electricity balancing and day-ahead spot market. European Journal of Operational Research, 261(1):390–404, 2017.

  48. R. A. Rigby and D. M. Stasinopoulos. Generalized additive models for location, scale and shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 54 (3):507–554, 2005.

  49. R. A. van der Veen and R. A. Hakvoort. The electricity balancing market: Exploring the design challenge. Utilities Policy, 43:186–194, 2016.

  50. R. A. van der Veen, A. Abbasy, and R. A. Hakvoort. Agent-based analysis of the impact of the imbalance pricing mechanism on market behavior in electricity balancing markets. Energy Economics, 34(4):874–881, 2012.

  51. R. Tibshirani. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1):267–288, 1996.
    Paper not yet in RePEc: Add citation now
  52. R. Weron. Electricity price forecasting: A review of the state-of-the-art with a look into the future. International journal of forecasting, 30(4):1030–1081, 2014.

  53. T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama. Optuna: A next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining, pages 2623–2631, 2019.
    Paper not yet in RePEc: Add citation now
  54. T. Gneiting and A. E. Raftery. Strictly proper scoring rules, prediction, and estimation. Journal of the American statistical Association, 102(477):359–378, 2007.

  55. T. Janke and F. Steinke. Forecasting the price distribution of continuous intraday electricity trading. Energies, 12(22):4262, 2019.

  56. T. Serafin, B. Uniejewski, and R. Weron. Averaging predictive distributions across calibration windows for day-ahead electricity price forecasting. Energies, 12(13):2561, 2019.

Cocites

Documents in RePEc which have cited the same bibliography

  1. Data-driven drone pre-positioning for traffic accident rapid assessment. (2024). Zhang, Guo Wei ; Zhu, Ning ; Meng, Zhu ; Ke, Ginger Y ; Liu, Zhaocai ; Yang, Yuance.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:183:y:2024:i:c:s1366554524000425.

    Full description at Econpapers || Download paper

  2. .

    Full description at Econpapers || Download paper

  3. .

    Full description at Econpapers || Download paper

  4. Deep distributional time series models and the probabilistic forecasting of intraday electricity prices. (2023). Nott, David J ; Smith, Michael Stanley ; Klein, Nadja.
    In: Journal of Applied Econometrics.
    RePEc:wly:japmet:v:38:y:2023:i:4:p:493-511.

    Full description at Econpapers || Download paper

  5. Forecasting electricity prices with expert, linear, and nonlinear models. (2023). Ravazzolo, Francesco ; del Grosso, Filippo ; Gianfreda, Angelica ; Bille, Anna Gloria.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:39:y:2023:i:2:p:570-586.

    Full description at Econpapers || Download paper

  6. Distributional neural networks for electricity price forecasting. (2023). Weron, Rafał ; Ziel, Florian ; Narajewski, Micha ; Marcjasz, Grzegorz.
    In: Energy Economics.
    RePEc:eee:eneeco:v:125:y:2023:i:c:s0140988323003419.

    Full description at Econpapers || Download paper

  7. Combining probabilistic forecasts of COVID-19 mortality in the United States. (2023). Taylor, Kathryn S.
    In: European Journal of Operational Research.
    RePEc:eee:ejores:v:304:y:2023:i:1:p:25-41.

    Full description at Econpapers || Download paper

  8. .

    Full description at Econpapers || Download paper

  9. Probabilistic Forecasting of German Electricity Imbalance Prices. (2022). Narajewski, Micha.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:14:p:4976-:d:857870.

    Full description at Econpapers || Download paper

  10. Decision strategies in sequential power markets with renewable energy. (2022). Ketter, Wolfgang ; Huisman, Ronald ; Koolen, Derck.
    In: Energy Policy.
    RePEc:eee:enepol:v:167:y:2022:i:c:s0301421522002506.

    Full description at Econpapers || Download paper

  11. Improving the accuracy of wind speed statistical analysis and wind energy utilization in the Ningxia Autonomous Region, China. (2022). Ai, Xueshan ; Dong, Xuan ; Zhu, Runzhou ; Wang, Xianjia.
    In: Applied Energy.
    RePEc:eee:appene:v:320:y:2022:i:c:s0306261922006146.

    Full description at Econpapers || Download paper

  12. Solar and wind power generation forecasts using elastic net in time-varying forecast combinations. (2022). Musgens, Felix ; Kaso, Mathias ; Nikodinoska, Dragana .
    In: Applied Energy.
    RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012861.

    Full description at Econpapers || Download paper

  13. Distributional neural networks for electricity price forecasting. (2022). Ziel, Florian ; Weron, Rafal ; Narajewski, Michal ; Marcjasz, Grzegorz.
    In: Papers.
    RePEc:arx:papers:2207.02832.

    Full description at Econpapers || Download paper

  14. Probabilistic forecasting of German electricity imbalance prices. (2022). Narajewski, Michal.
    In: Papers.
    RePEc:arx:papers:2205.11439.

    Full description at Econpapers || Download paper

  15. A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources. (2022). Durante, Fabrizio ; Gianfreda, Angelica ; Rossini, Luca ; Ravazzolo, Francesco.
    In: Papers.
    RePEc:arx:papers:2201.01132.

    Full description at Econpapers || Download paper

  16. Forecasting Electricity Prices with Expert, Linear and Non-Linear Models. (2021). Ravazzolo, Francesco ; del Grosso, Filippo ; Gianfreda, Angelica ; Bille, Anna Gloria.
    In: Working Paper series.
    RePEc:rim:rimwps:21-20.

    Full description at Econpapers || Download paper

  17. Optimal Daily Trading of Battery Operations Using Arbitrage Spreads. (2021). Bunn, Derek ; Abramova, Ekaterina.
    In: Energies.
    RePEc:gam:jeners:v:14:y:2021:i:16:p:4931-:d:612882.

    Full description at Econpapers || Download paper

  18. Government penalty provision and contracting with asymmetric quality information in a bioenergy supply chain. (2021). Huang, Song ; He, NA ; Jiang, Zhong-Zhong.
    In: Transportation Research Part E: Logistics and Transportation Review.
    RePEc:eee:transe:v:154:y:2021:i:c:s136655452100243x.

    Full description at Econpapers || Download paper

  19. Pricing forward contracts in power markets with variable renewable energy sources. (2021). Stet, Cristian ; Koolen, Derck ; Huisman, Ronald.
    In: Renewable Energy.
    RePEc:eee:renene:v:180:y:2021:i:c:p:1260-1265.

    Full description at Econpapers || Download paper

  20. Modeling energy prices under energy transition: A novel stochastic-copula approach. (2021). Vidal, Joo Pedro ; Dias, Jose Carlos ; Fernandes, Mario Correia.
    In: Economic Modelling.
    RePEc:eee:ecmode:v:105:y:2021:i:c:s0264999321002601.

    Full description at Econpapers || Download paper

  21. Time-frequency connectedness between Asian electricity sectors. (2021). TAGHIZADEH-HESARY, Farhad ; Ngo, Thanh ; Naeem, Muhammad Abubakr ; Arif, Muhammad ; Hasan, Mudassar ; Taghizadehhesary, Farhad.
    In: Economic Analysis and Policy.
    RePEc:eee:ecanpo:v:69:y:2021:i:c:p:208-224.

    Full description at Econpapers || Download paper

  22. How are Day-Ahead Prices Informative for Predicting the Next Day’s Consumption of Natural Gas ?. (2020). Sevi, Benoit ; Massol, Olivier ; Thomas, Arthur.
    In: Working Papers.
    RePEc:hal:wpaper:hal-03178474.

    Full description at Econpapers || Download paper

  23. Forecasting the Intra-Day Spread Densities of Electricity Prices. (2020). Bunn, Derek ; Abramova, Ekaterina.
    In: Energies.
    RePEc:gam:jeners:v:13:y:2020:i:3:p:687-:d:316818.

    Full description at Econpapers || Download paper

  24. A strategic predictive distribution for tests of probabilistic calibration. (2020). Taylor, James W.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:36:y:2020:i:4:p:1380-1388.

    Full description at Econpapers || Download paper

  25. Comparing the forecasting performances of linear models for electricity prices with high RES penetration. (2020). Gianfreda, Angelica ; Rossini, Luca ; Ravazzolo, Francesco.
    In: International Journal of Forecasting.
    RePEc:eee:intfor:v:36:y:2020:i:3:p:974-986.

    Full description at Econpapers || Download paper

  26. The effect of a new power cable on energy prices volatility spillovers. (2020). Spagnolo, Nicola ; Sapio, Alessandro.
    In: Energy Policy.
    RePEc:eee:enepol:v:144:y:2020:i:c:s0301421520302354.

    Full description at Econpapers || Download paper

  27. Volatility spillovers in Australian electricity markets. (2020). Trueck, Stefan ; Truck, Stefan ; Kordzakhia, Nino ; Han, Lin.
    In: Energy Economics.
    RePEc:eee:eneeco:v:90:y:2020:i:c:s0140988320301225.

    Full description at Econpapers || Download paper

  28. Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach. (2020). Maciejowska, Katarzyna.
    In: Energy Economics.
    RePEc:eee:eneeco:v:85:y:2020:i:c:s0140988319303275.

    Full description at Econpapers || Download paper

  29. Factor models in the German electricity market: Stylized facts, seasonality, and calibration. (2020). Wagner, A ; Hinderks, W J.
    In: Energy Economics.
    RePEc:eee:eneeco:v:85:y:2020:i:c:s0140988319301033.

    Full description at Econpapers || Download paper

  30. Ensemble forecasting for intraday electricity prices: Simulating trajectories. (2020). Ziel, Florian ; Narajewski, Micha.
    In: Applied Energy.
    RePEc:eee:appene:v:279:y:2020:i:c:s0306261920312824.

    Full description at Econpapers || Download paper

  31. Large Time-Varying Volatility Models for Electricity Prices. (2020). Rossini, Luca ; Ravazzolo, Francesco ; Gianfreda, Angelica.
    In: Working Papers.
    RePEc:bny:wpaper:0088.

    Full description at Econpapers || Download paper

  32. Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices. (2020). Nott, David J ; Smith, Michael Stanley ; Klein, Nadja.
    In: Papers.
    RePEc:arx:papers:2010.01844.

    Full description at Econpapers || Download paper

  33. Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories. (2020). Ziel, Florian ; Narajewski, Michal.
    In: Papers.
    RePEc:arx:papers:2005.01365.

    Full description at Econpapers || Download paper

  34. Forecasting the Intra-Day Spread Densities of Electricity Prices. (2020). Bunn, Derek ; Abramova, Ekaterina.
    In: Papers.
    RePEc:arx:papers:2002.10566.

    Full description at Econpapers || Download paper

  35. Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits. (2019). Weron, Tomasz ; Nitka, Weronika ; Maciejowska, Katarzyna.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:4:p:631-:d:206429.

    Full description at Econpapers || Download paper

  36. Greener, more integrated, and less volatile? A quantile regression analysis of Italian wholesale electricity prices. (2019). Sapio, Alessandro.
    In: Energy Policy.
    RePEc:eee:enepol:v:126:y:2019:i:c:p:452-469.

    Full description at Econpapers || Download paper

  37. Pricing German Energiewende products: Intraday cap/floor futures. (2019). Wagner, A ; Hinderks, W J.
    In: Energy Economics.
    RePEc:eee:eneeco:v:81:y:2019:i:c:p:287-296.

    Full description at Econpapers || Download paper

  38. Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity. (2019). Bunn, Derek ; Abramova, Ekaterina.
    In: Papers.
    RePEc:arx:papers:1903.06668.

    Full description at Econpapers || Download paper

  39. A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market. (2018). Gianfreda, Angelica ; Kermer, Stefan ; Bunn, Derek W.
    In: Energies.
    RePEc:gam:jeners:v:11:y:2018:i:10:p:2658-:d:173889.

    Full description at Econpapers || Download paper

  40. Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration. (2018). Rossini, Luca ; Ravazzolo, Francesco ; Gianfreda, Angelica.
    In: Working Papers.
    RePEc:bny:wpaper:0060.

    Full description at Econpapers || Download paper

  41. Comparing the Forecasting Performances of Linear Models for Electricity Prices with High RES Penetration. (2018). Rossini, Luca ; Ravazzolo, Francesco ; Gianfreda, Angelica.
    In: Papers.
    RePEc:arx:papers:1801.01093.

    Full description at Econpapers || Download paper

Coauthors

Authors registered in RePEc who have wrote about the same topic

Report date: 2024-12-26 01:20:30 || Missing content? Let us know

CitEc is a RePEc service, providing citation data for Economics since 2001. Sponsored by INOMICS. Last updated October, 6 2023. Contact: CitEc Team.