Abstract and Applied Analysis

The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia

Jianzhou Wang, Ling Xiao, and Jun Shi

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber. If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text

Abstract

Electricity price forecasting holds very important position in the electricity market. Inaccurate price forecasting may cause energy waste and management chaos in the electricity market. However, electricity price forecasting has always been regarded as one of the largest challenges in the electricity market because it shows high volatility, which makes electricity price forecasting difficult. This paper proposes the use of artificial intelligence optimization combination forecasting models based on preprocessing data, called “chaos particles optimization (CPSO) weight-determined combination models.” These models allow for the weight of the combined model to take values of [ - 1,1 ] . In the proposed models, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to identify outliers, and the outliers are replaced by a new data-produced linear interpolation function. The proposed CPSO weight-determined combination models are then used to forecast the projected future electricity price. In this case study, the electricity price data of South Australia are simulated. The results indicate that, while the weight of the combined model takes values of [ - 1,1 ] , the proposed combination model can always provide adaptive, reliable, and comparatively accurate forecast results in comparison to traditional combination models.

Article information

Source
Abstr. Appl. Anal., Volume 2014 (2014), Article ID 172306, 12 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412276992

Digital Object Identifier
doi:10.1155/2014/172306

Zentralblatt MATH identifier
07021859

Citation

Wang, Jianzhou; Xiao, Ling; Shi, Jun. The Combination Forecasting of Electricity Price Based on Price Spikes Processing: A Case Study in South Australia. Abstr. Appl. Anal. 2014 (2014), Article ID 172306, 12 pages. doi:10.1155/2014/172306. https://projecteuclid.org/euclid.aaa/1412276992


Export citation

References

  • J. Janczura, S. Trückb, R. Weron, and R. C. Wolff, “Identifying spikes and seasonal components in electricity spotprice data: a guide to robust modeling,” Energy Economics, vol. 38, pp. 96–110, 2013.
  • A. Cifter, “Forecasting electricity price volatility with the Markov-switching GARCH model: evidence from the Nordic electric power market,” Electric Power Systems Research, vol. 102, pp. 61–67, 2013.
  • S. K. Aggarwal, L. M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: a review and evaluation,” International Journal of Electrical Power and Energy Systems, vol. 31, no. 1, pp. 13–22, 2009.
  • E. Hickey, D. G. Loomis, and H. Mohammadi, “Forecasting hourly electricity prices using ARMAX-GARCH models: an application to MISO hubs,” Energy Economics, vol. 34, no. 1, pp. 307–315, 2012.
  • N. Amjady and F. Keynia, “A new prediction strategy for price spike forecasting of day-ahead electricity markets,” Applied Soft Computing Journal, vol. 11, no. 6, pp. 4246–4256, 2011.
  • A. I. Arciniegas and I. E. Arciniegas Rueda, “Forecasting short-term power prices in the Ontario Electricity Market (OEM) with a fuzzy logic based inference system,” Utilities Policy, vol. 16, no. 1, pp. 39–48, 2008.
  • T. M. Christensen, A. S. Hurn, and K. A. Lindsay, “Forecasting spikes in electricity prices,” International Journal of Forecasting, vol. 28, no. 2, pp. 400–411, 2012.
  • S. Bordignon, D. W. Bunn, F. Lisi, and F. Nan, “Combining day-ahead forecasts for British electricity prices,” Energy Economics, vol. 35, pp. 88–103, 2013.
  • J. M. Bates and C. W. J. Granger, “The combination of forecasts,” Operational Research Quarterly, vol. 20, no. 4, pp. 451–468, 1969.
  • R. R. Andrawis, A. F. Atiya, and H. El-Shishiny, “Combination of long term and short term forecasts, with application to tourism demand forecasting,” International Journal of Forecasting, vol. 27, no. 3, pp. 870–886, 2011.
  • M. Khashei, M. Bijari, and G. A. Raissi Ardali, “Hybridization of autoregressive integrated moving average (ARIMA) with probabilistic neural networks (PNNs),” Computers and Industrial Engineering, vol. 63, no. 1, pp. 37–45, 2012.
  • X. WeiLi, S. J. Cho, and S. T. Kim, “Combined use of BP neural network and computational integral imaging reconstruction for optical multiple-image security,” Optics Communications, vol. 315, pp. 147–158, 2014.
  • R. Hecht-Nielsen, “Kolmogorov's mapping neural network existence theorem,” in Proceedings of the IEEE 1st International Conference on Neural Networks, pp. 11–13, 1987.
  • C. Ren, N. An, J. Wang, L. Li, B. Hu, and D. Shang, “Optimal parameters selection for BP neural network based on particleswarm optimization: a case study of wind speed forecasting,” Knowledge-Based Systems, vol. 56, pp. 226–239, 2014.
  • P. Louka, G. Galanis, N. Siebert et al., “Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering,” Journal of Wind Engineering and Industrial Aerodynamics, vol. 96, no. 12, pp. 2348–2362, 2008.
  • Y. Wang, J. Wang, G. Zhao, and Y. Dong, “Application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of China,” Energy Policy, vol. 48, pp. 284–294, 2012.
  • Z. H. Guo, J. Wu, H. Y. Lu, and J. Z. Wang, “A case study on a hybrid wind speed forecasting method using BP neural network,” Knowledge-Based Systems, vol. 24, no. 7, pp. 1048–1056, 2011.
  • M. Chaabene and M. Ben Ammar, “Neuro-fuzzy dynamic model with Kalman filter to forecast irradiance and temperature for solar energy systems,” Renewable Energy, vol. 33, no. 7, pp. 1435–1443, 2008.
  • H. Chen, The Validity of the Theory and Its Application of Combination Forecast Methods, Science Press, Beijing, China, 2008.
  • S. Alam, G. Dobbie, Y. S. Koh, P. Riddlea, and S. U. Rehman, “Research on particle swarm optimization based clustering: a systematic review of literature and techniques,” Swarm and Evolutionary Computation, 2014.
  • W. Zhang, D. Ma, J. Wei, and H. Liang, “A parameter selection strategy for particle swarm optimization basedon particle positions,” Expert Systems with Applications, vol. 41, no. 7, pp. 3576–3584, 2014.
  • S. J. Nanda, G. Panda, and B. Majhi, “Improved identification of Hammerstein plants using new CPSO and IPSO algorithms,” Expert Systems with Applications, vol. 37, no. 10, pp. 6818–6831, 2010.
  • X. Yuan, J. Zhao, Y. Yang, and Y. Wang, “Hybrid parallel chaos optimization algorithm with harmony searchalgorithm,” Applied Soft Computing, vol. 17, pp. 12–22, 2014.
  • X. Y. Meng, S. Huang, and Y. Li, “Analysis of chaotic PSO and application in ship design,” Computer Engineeringand Applications, vol. 46, no. 17, pp. 224–228, 2010.
  • H. Jiang, C. K. Kwong, Z. Chen, and Y. C. Ysim, “Chaos particle swarm optimization and T-S fuzzy modeling approaches to constrained predictive control,” Expert Systems with Applications, vol. 39, no. 1, pp. 194–201, 2012.
  • J. M. Liu and Y. L. Gao, “Chaos particle swarm optimization algorithm,” Computer Application, vol. 28, no. 2, pp. 322–325, 2008.
  • Y. Gao and S. L. Xie, “Chaos particle swarm optimization algorithm,” in Computer Science and Technology, 2004.
  • J. Forrest and K. Dunn, “Cultural diversity, racialisation and the experience of racism in rural Australia: the South Australian case,” Journal of Rural Studies, vol. 30, pp. 1–9, 2013.
  • Z. Francis, C. Villagrasa, and I. Clairand, “Simulation of DNA damage clustering after proton irradiation using an adapted DBSCAN algorithm,” Computer Methods and Programs in Biomedicine, vol. 101, no. 3, pp. 265–270, 2011.
  • H. Jiang, J. Li, S. Yi, X. Wang, and X. Hu, “A new hybrid method based on partitioning-based DBSCAN and ant clustering,” Expert Systems with Applications, vol. 38, no. 8, pp. 9373–9381, 2011.
  • W. Zhao, J. Wang, and H. Lu, “Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model,” Omega, vol. 45, pp. 80–91, 2014. \endinput