Automated variable selection and shrinkage for day-ahead electricity price forecasting

Energies

Volumn 9, Issue 8, 2016, Pages

  Uniejewski, Bartosz ^a   Nowotarski, Jakub ^a   Weron, Rafal ^a  

^a WROCLAW UNIVERSITY OF TECHNOLOGY   (Poland)

Author keywords

Autoregression; Day ahead market; Elastic net; Electricity price forecasting; Lasso; Ridge regression; Stepwise regression; Variable selection

Indexed keywords

COMMERCE; COSTS; FORECASTING; REGRESSION ANALYSIS; SHRINKAGE; TRAVELING SALESMAN PROBLEM;

AUTOREGRESSION; DAY AHEAD MARKET; ELASTIC NET; ELECTRICITY PRICE FORECASTING; LASSO; RIDGE REGRESSION; STEPWISE REGRESSION; VARIABLE SELECTION;

POWER MARKETS;

EID: 84982975808     PISSN: None     EISSN: 19961073     Source Type: Journal    
DOI: 10.3390/en9080621     Document Type: Article

References (46)

1. Weron, R. Electricity price forecasting: A review of the state-of-the-art with a look into the future. Int. J. Forecast. 2014, 30, 1030-1081.
2. Zareipour, H., Canizares, C.A., Bhattacharya, K. Economic impact of electricity market price forecasting errors: A demand-side analysis. IEEE Trans. Power Syst. 2010, 25, 254-262.
3. Hong, T. Crystal Ball Lessons in Predictive Analytics. EnergyBiz Mag. 2015, 35-37.
4. Amjady, N., Keynia, F. Day-ahead price forecasting of electricity markets by mutual information technique and cascaded neuro-evolutionary algorithm. IEEE Trans. Power Syst. 2009, 24, 306-318.
5. Gianfreda, A., Grossi, L. Forecasting Italian electricity zonal prices with exogenous variables. Energy Econ. 2012, 34, 2228-2239.
6. Maciejowska, K. Fundamental and speculative shocks, what drives electricity prices? In Proceedings of the 11th International Conference on the European Energy Market (EEM14), Kraków, Poland, 28-30 May 2014; doi:10.1109/EEM.2014.6861289.
7. Ludwig, N., Feuerriegel, S., Neumann, D. Putting Big Data analytics to work: Feature selection for forecasting electricity prices using the LASSO and random forests. J. Decis. Syst. 2015, 24, 19-36.
8. Monteiro, C., Fernandez-Jimenez, L.A., Ramirez-Rosado, I.J. Explanatory information analysis for day-ahead price forecasting in the Iberian electricity market. Energies 2015, 8, 10464-10486.
9. Ziel, F., Steinert, R., Husmann, S. Efficient modeling and forecasting of electricity spot prices. Energy Econ. 2015, 47, 89-111.
10. Dudek, G. Multilayer perceptron for GEFCom2014 probabilistic electricity price forecasting. Int. J. Forecast. 2016, 32, 1057-1060.
11. Keles, D., Scelle, J., Paraschiv, F., Fichtner,W. Extended forecast methods for day-ahead electricity spot prices applying artificial neural networks. Appl. Energy 2016, 162, 218-230.
12. Karakatsani, N., Bunn, D. Forecasting electricity prices: The impact of fundamentals and time-varying coefficients. Int. J. Forecast. 2008, 24, 764-785.
13. Misiorek, A. Short-term forecasting of electricity prices: Do we need a different model for each hour? Medium Econom. Toepass. 2008, 16, 8-13.
14. Amjady, N., Keynia, F. Electricity market price spike analysis by a hybrid data model and feature selection technique. Electr. Power Syst. Res. 2010, 80, 318-327.
15. Voronin, S., Partanen, J. Price forecasting in the day-ahead energy market by an iterative method with separate normal price and price spike frameworks. Energies 2013, 6, 5897-5920.
16. Barnes, A.K., Balda, J.C. Sizing and economic assessment of energy storage with real-time pricing and ancillary services. In Proceedings of the 2013 4th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Rogers, AR, USA, 8-11 July 2013; doi:10.1109/PEDG.2013.6785651.
17. González, C., Mira-McWilliams, J., Juárez, I. Important variable assessment and electricity price forecasting based on regression tree models: Classification and regression trees, bagging and random forests. IET Gener. Transm. Distrib. 2015, 9, 1120-1128.
18. Ziel, F. Forecasting Electricity Spot Prices Using LASSO: On Capturing the Autoregressive Intraday Structure. IEEE Trans. Power Syst. 2016, doi:10.1109/TPWRS.2016.2521545.
19. Misiorek, A., Trück, S.;Weron, R. Point and interval forecasting of spot electricity prices: Linear vs. non-linear time series models. Stud. Nonlinear Dyn. Econom. 2006, 10, doi:10.2202/1558-3708.1362.
20. Weron, R. Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach; John Wiley & Sons: Chichester, UK, 2006.
21. Weron, R., Misiorek, A. Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. Int. J. Forecast. 2008, 24, 744-763.
22. Serinaldi, F. Distributional modeling and short-term forecasting of electricity prices by Generalized Additive Models for Location, Scale and Shape. Energy Econ. 2011, 33, 1216-1226.
23. Kristiansen, T. Forecasting Nord Pool day-ahead prices with an autoregressive model. Energy Policy 2012, 49, 328-332.
24. Nowotarski, J., Raviv, E., Trück, S., Weron, R. An empirical comparison of alternate schemes for combining electricity spot price forecasts. Energy Econ. 2014, 46, 395-412.
25. Gaillard, P., Goude, Y., Nedellec, R. Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting. Int. J. Forecast. 2016, 32, 1038-1050.
26. Maciejowska, K., Nowotarski, J.;Weron, R. Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. Int. J. Forecast. 2016, 32, 957-965.
27. Nowotarski, J., Weron, R. To combine or not to combine? Recent trends in electricity price forecasting. ARGO 2016, 9, 7-14.
28. Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., Hyndman, R.J. Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. Int. J. Forecast. 2016, 32, 896-913.
29. Maciejowska, K., Nowotarski, J. A hybrid model for GEFCom2014 probabilistic electricity price forecasting. Int. J. Forecast. 2016, 32, 1051-1056.
30. Nowotarski, J.;Weron, R. On the importance of the long-term seasonal component in day-ahead electricity price forecasting. Energy Econ. 2016, 57, 228-235.
31. Diebold, F.X., Mariano, R.S. Comparing predictive accuracy. J. Bus. Econ. Stat. 1995, 13, 253-263.
32. Garcia-Martos, C., Conejo, A. Price forecasting techniques in power systems. InWiley Encyclopedia of Electrical and Electronics Engineering; Wiley: Chichester, UK, 2013; pp. 1-23, doi:10.1002/047134608X.W8188.
33. Burger, M., Graeber, B., Schindlmayr, G. Managing Energy Risk: An Integrated View on Power and Other Energy Markets; Wiley: Chichester, UK, 2007.
34. Nogales, F.J., Contreras, J., Conejo, A.J., Espinola, R. Forecasting next-day electricity prices by time series models. IEEE Trans. Power Syst. 2002, 17, 342-348.
35. Conejo, A.J., Contreras, J., Espínola, R., Plazas, M.A. Forecasting electricity prices for a day-ahead pool-based electric energy market. Int. J. Forecast. 2005, 21, 435-462.
36. Paraschiv, F., Fleten, S.E., Schürle, M. A spot-forward model for electricity prices with regime shifts. Energy Econ. 2015, 47, 142-153.
37. Broszkiewicz-Suwaj, E., Makagon, A., Weron, R., Wylomá nska, A. On detecting and modeling periodic correlation in financial data. Physica A 2004, 336, 196-205.
38. Bosco, B., Parisio, L., Pelagatti, M. Deregulated wholesale electricity prices in Italy: An empirical analysis. Int. Adv. Econ. Res. 2007, 13, 415-432.
39. James, G., Witten, D., Hastie, T., Tibshirani, R. An Introduction to Statistical Learning with Applications in R; Springer: New York, NY, USA, 2013.
40. Bessec, M., Fouquau, J., Meritet, S. Forecasting electricity spot prices using time-series models with a double temporal segmentation. Appl. Econ. 2016, 48, 361-378.
41. Hoerl, A.E., Kennard, R.W. Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 1970, 12, 55-67.
42. Tibshirani, R. Regression shrinkage and selection via the lasso. J. Royal Stat. Soc. B 1996, 58, 267-288.
43. Ziel, F. Iteratively reweighted adaptive lasso for conditional heteroscedastic time series with applications to AR-ARCH type processes. Comput. Stat. Data Anal. 2016, 100, 773-793.
44. Hastie, T., Tibshirani, R., Wainwright, M. Statistical Learning with Sparsity: The Lasso and Generalizations; CRC Press: Philadelphia, PA, USA, 2015.
45. Zou, H., Hastie, T. Regularization and Variable Selection via the Elastic Nets. J. Royal Stat. Soc. B 2015, 67, 301-320.
46. Bordignon, S., Bunn, D.W., Lisi, F., Nan, F. Combining day-ahead forecasts for British electricity prices. Energy Econ. 2013, 35, 88-103.