Quantifying uncertainties of neural network-based electricity price forecasts

Applied Energy

Volumn 112, Issue , 2013, Pages 120-129

  Khosravi, Abbas ^a   Nahavandi, Saeid ^a   Creighton, Doug ^a  

^a DEAKIN UNIVERSITY   (Australia)

Author keywords

Bootstrap; Delta; Electricity price; Neural networks; Prediction intervals

Indexed keywords

COSTS; ELECTRICITY; EXPERIMENTS; NEURAL NETWORKS;

AUSTRALIAN ELECTRICITIES; BOOTSTRAP; COVERAGE PROBABILITIES; DELTA; ELECTRICITY PRICES; FORECASTING ELECTRICITY; PREDICTION INTERVAL; UNCERTAINTY QUANTIFICATIONS;

FORECASTING;

ARTIFICIAL NEURAL NETWORK; BOOTSTRAPPING; DATA SET; ELECTRICITY GENERATION; ERROR ANALYSIS; EXPERIMENTAL STUDY; FORECASTING METHOD; PRICE DYNAMICS; UNCERTAINTY ANALYSIS;

EID: 84879800748     PISSN: 03062619     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apenergy.2013.05.075     Document Type: Article

References (47)

1. Tan Z., Zhang J., Wang J., Xu J. Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models. Appl Energy 2010, 87(11):3606-3610.
2. Dong Y., Wang J., Jiang H., Wu J. Short-term electricity price forecast based on the improved hybrid model. Energy Convers Manage 2011, 52(8-9):2987-2995.
3. Pedregal D.J., Trapero J.R. Electricity prices forecasting by automatic dynamic harmonic regression models. Energy Convers Manage 2007, 48(5):1710-1719. ISSN 0196-8904.
4. Nogales F., Contreras J., Conejo A., Espinola R. Forecasting next-day electricity prices by time series models. IEEE Trans Power Syst 2002, 17(2):342-348. ISSN 0885-8950.
5. Zareipour H., Canizares C., Bhattacharya K., Thomson J. Application of public-domain market information to forecast Ontario's wholesale electricity prices. IEEE Trans Power Syst 2006, 21(4):1707-1717. ISSN 0885-8950.
6. Garcia R., Contreras J., van Akkeren M., Garcia J. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst 2005, 20(2):867-874. ISSN 0885-8950.
7. 2 and electricity prices and their volatilities. Appl Energy 2013, 101:363-375.
8. Pindoriya N., Singh S., Singh S. An adaptive wavelet neural network-based energy price forecasting in electricity markets. IEEE Trans Power Syst 2008, 23(3):1423-1432. ISSN 0885-8950.
9. Lin W.M., Gow H.J., Tsai M.T. An enhanced radial basis function network for short-term electricity price forecasting. Appl Energy 2010, 87(10):3226-3234. ISSN 0306-2619.
10. Khotanzad A., Zhou E., Elragal H. A neuro-fuzzy approach to short-term load forecasting in a price-sensitive environment. IEEE Trans Power Syst 2002, 17(4):1273-1282. ISSN 0885-8950.
11. Zhao J.H., Dong Z.Y., Xu Z., Wong K.P. A statistical approach for interval forecasting of the electricity price. IEEE Trans Power Syst 2008, 23(2):267-276. ISSN 0885-8950.
12. Conejo A.J., Contreras J., Espfnola R., Plazas M.A. Forecasting electricity prices for a day-ahead pool-based electric energy market. Int J Forecasting 2005, 21(3):435-462. ISSN 0169-2070.
13. Pino R., Parreno J., Gomez A., Priore P. Forecasting next-day price of electricity in the Spanish energy market using artificial neural networks. Eng Appl Artif Intell 2008, 21(1):53-62. ISSN 0952-1976.
14. Amjady N., Hemmati M. Energy price forecasting - problems and proposals for such predictions. IEEE Power Energy Mag 2006, 4(2):20-29. ISSN 1540-7977.
15. MacKay D.J.C. The evidence framework applied to classification networks. Neural Comput 1992, 4(5):720-736.
16. Bishop C.M. Neural networks for pattern recognition 1995, Oxford University Press, Oxford.
17. Hwang J.T.G., Ding A.A. Prediction intervals for artificial neural networks. J Am Stat Assoc 1997, 92(438):748-757. ISSN 01621459.
18. Veaux R.D.d., Schumi J., Schweinsberg J., Ungar L.H. Prediction intervals for neural networks via nonlinear regression. Technometrics 1998, 40(4):273-282. ISSN 00401706.
19. Heskes T. Practical confidence and prediction intervals. Neural information processing systems 1997, vol. 9:176-182. MIT Press.
20. Nix D, Weigend A. Estimating the mean and variance of the target probability distribution. In: IEEE international conference on neural networks; 1994.
21. Khosravi A, Nahavandi S. Combined non-parametric prediction intervals for wind power generation. IEEE Trans on Sust Energy, 2013. in Press.
22. Khosravi A, Nahavandi S, Creighton D. Prediction intervals for short-term wind farm power generation forecasts. IEEE Trans on Sust Energy, 2013, in press.
23. Khosravi A., Nahavandi S., Creighton D., Atiya A.F. A lower upper bound estimation method for construction of neural network-based prediction intervals. IEEE Trans Neural Networks 2011, 22(3):337-346.
24. Khosravi A., Mazloumi E., Nahavandi S., Creighton D., van Lint J.W.C. Prediction intervals to account for uncertainties in travel time prediction. IEEE Trans Intell Transport Syst 2011, 12(2):537-547.
25. Khosravi A, Mazloumi E, Nahavandi S, Creighton D, van Lint JWC. A genetic algorithm-based method for improving quality of travel time prediction intervals. Transport Res Part C [accepted 11.04.11].
26. Yu G., Qiu H., Djurdjanovic D., Lee J. Feature signature prediction of a boring process using neural network modeling with confidence bounds. Int J Adv Manuf Technol 2006, 30(7):614-621.
27. Khosravi A., Nahavandi S., Creighton D. A prediction interval-based approach to determine optimal structures of Neural Network Metamodels. Expert Syst Appl 2010, 37:2377-2387. ISSN 0957-4174.
28. Khosravi A, Nahavandi S, Creighton D. Load Forecasting and Neural Networks: a prediction interval-based perspective. In: Panigrahi BK, et al., editors. Computational intelligence in power engineering. SCI 302; 2010. p. 131-50.
29. Khosravi A., Nahavandi S., Creighton D. Construction of optimal prediction intervals for load forecasting problem. IEEE Trans Power Syst 2010, 25:1496-1503.
30. Zio E. A study of the bootstrap method for estimating the accuracy of artificial neural networks in predicting nuclear transient processes. IEEE Trans Nucl Sci 2006, 53(3):1460-1478. ISSN 0018-9499.
31. Khosravi A. Construction and Optimisation of prediction intervals for neural network models, Ph.D. Thesis. Deakin University; 2010.
32. Wu HC, Chan SC, Tsui KM, A new recursive dynamic factor analysis for point and interval forecast of electricity price. IEEE Trans on Power Syst 2013. in press. http://dx.doi.org/10.1109/TPWRS.2012.2232314.
33. Chen X., Dong Z.Y., Meng K., Xu Y., Wong K.P., Ngan H. Electricity price forecasting with extreme learning machine and bootstrapping. IEEE Trans Power Syst 2012, 27(4):2055-2062. ISSN 0885-8950.
34. Weron R., Misiorek A. Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. Int J Forecasting 2008, 24(4):744-763. ISSN 0169-2070.
35. Niimura T., Nakashima T. Deregulated electricity market data representation by fuzzy regression models. IEEE Trans Syst Man Cyber Part C: Appl Rev 2001, 31(3):320-326. ISSN 1094-6977.
36. Yamin H.Y., Shahidehpour S.M., Li Z. Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. Int J Electrical Power Energy Syst 2004, 26(8):571-581. ISSN 0142-0615.
37. Zhang X., Rice J.A. Short-term travel time prediction. Transport Res Part C: Emerging Technol 2003, 11(3-4):187-210. ISSN 0968-090X.
38. Zhang L., Luh P. Neural network-based market clearing price prediction and confidence interval estimation with an improved extended Kalman filter method. IEEE Trans Power Syst 2005, 20(1):59-66. ISSN 0885-8950.
39. Zhou M., Yan Z., Ni Y., Li G., Nie Y. Electricity price forecasting with confidence-interval estimation through an extended ARIMA approach. IEE Proc Generation Transm Distribution 2006, 153(2):187-195. ISSN 1350-2360.
40. Baringo L, Conejo AJ. Offering strategy via robust optimization. IEEE Trans Power Syst. doi:10.1109/TPWRS.2010.2092793 ISSN 0885-8950 [13.12.10].
41. Conejo A.J., Morales J.M., Baringo L. Real-time demand response model. IEEE Trans Power Syst 2010, 1(3):236-242.
42. Efron B., Tibshirani R.J. An introduction to the bootstrap 1993, Chapman and Hall, New York.
43. Khosravi A., Nahavandi S., Creighton D. Prediction interval construction and optimization for adaptive neurofuzzy inference systems. IEEE Trans Fuzzy Syst 2011, 19(5):983-988.
44. Amjady N. Day-ahead price forecasting of electricity markets by a new fuzzy neural network. IEEE Trans Power Syst 2006, 21(2):887-896. ISSN 0885-8950.
45. Box G., Jenkins G., Reinsel G.C. Time series analysis: forecasting and control 2008, John Wiley, Hoboken, N.J. 4th ed.
46. Zhang G.P., Qi M. Neural network forecasting for seasonal and trend time series. Eur J Operational Res 2005, 160(2):501-514. ISSN 0377-2217.
47. Khosravi A., Nahavandi S., Creighton D., Atiya A. Comprehensive review of neural network-based prediction intervals and new advances. IEEE Trans Neural Networks 2011, 22(9):1341-1356. ISSN 1045-9227.