Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018, Article ID 5194810, 13 pages
https://doi.org/10.1155/2018/5194810
Research Article

Energy Demand Forecasting: Combining Cointegration Analysis and Artificial Intelligence Algorithm

School of Economics, Southwestern University of Finance and Economics, Chengdu 611130, China

Correspondence should be addressed to Junbing Huang; nc.ude.tib@23410702

Received 6 October 2017; Accepted 6 December 2017; Published 10 January 2018

Academic Editor: Benjamin Ivorra

Copyright © 2018 Junbing Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. BP. World Energy outlook 2017 ed. Paris, Francis; 2017.
  2. B. Q. Lin, “Structural changes, efficiency improvement and electricity demand forecasting,” Economic Research, vol. 5, pp. 57–65, 2003 (Chinese). View at Google Scholar
  3. B. Lin and X. Ouyang, “Energy demand in China: Comparison of characteristics between the US and China in rapid urbanization stage,” Energy Conversion and Management, vol. 79, pp. 128–139, 2014. View at Publisher · View at Google Scholar · View at Scopus
  4. J. W. Taylor, “Short-term electricity demand forecasting using double seasonal exponential smoothing,” Journal of the Operational Research Society, vol. 54, no. 8, pp. 799–805, 2003. View at Publisher · View at Google Scholar · View at Scopus
  5. J. W. Taylor, “An evaluation of methods for very short-term load forecasting using minute-by-minute British data,” International Journal of Forecasting, vol. 24, no. 4, pp. 645–658, 2008. View at Publisher · View at Google Scholar · View at Scopus
  6. E. Erdogdu, “Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey,” Energy Policy, vol. 35, no. 2, pp. 1129–1146, 2007. View at Publisher · View at Google Scholar · View at Scopus
  7. J. D. A. Cabral, L. F. L. Legey, and M. V. D. Freitas Cabral, “Electricity consumption forecasting in Brazil: A spatial econometrics approach,” Energy, vol. 126, pp. 124–131, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. C. Deb, F. Zhang, J. Yang, S. E. Lee, and K. W. Shah, “A review on time series forecasting techniques for building energy consumption,” Renewable & Sustainable Energy Reviews, vol. 74, pp. 902–924, 2017. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Frías-Paredes, F. Mallor, M. Gastón-Romeo, and T. León, “Assessing energy forecasting inaccuracy by simultaneously considering temporal and absolute errors,” Energy Conversion and Management, vol. 142, pp. 533–546, 2017. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Haldenbilen and H. Ceylan, “Genetic algorithm approach to estimate transport energy demand in Turkey,” Energy Policy, vol. 33, no. 1, pp. 89–98, 2005. View at Publisher · View at Google Scholar · View at Scopus
  11. O. E. Canyurt and H. K. Öztürk, “Three different applications of genetic algorithm (GA) search techniques on oil demand estimation,” Energy Conversion and Management, vol. 47, no. 18-19, pp. 3138–3148, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. Z. W. Geem and W. E. Roper, “Energy demand estimation of South Korea using artificial neural network,” Energy Policy, vol. 37, no. 10, pp. 4049–4054, 2009. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Duran Toksari, “Ant colony optimization approach to estimate energy demand of Turkey,” Energy Policy, vol. 35, no. 8, pp. 3984–3990, 2007. View at Publisher · View at Google Scholar · View at Scopus
  14. M. D. Toksarı, “Estimating the net electricity energy generation and demand using the ant colony optimization approach: case of Turkey,” Energy Policy, vol. 37, no. 3, pp. 1181–1187, 2009. View at Publisher · View at Google Scholar · View at Scopus
  15. S. Yu, K. Zhu, and X. Zhang, “Energy demand projection of China using a path-coefficient analysis and PSO-GA approach,” Energy Conversion and Management, vol. 53, no. 1, pp. 142–153, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Yu and K. Zhu, “A hybrid procedure for energy demand forecasting in China,” Energy, vol. 37, no. 1, pp. 396–404, 2012. View at Publisher · View at Google Scholar · View at Scopus
  17. L. Liu, J. Huang, and S. W. Yu, “Prediction of primary energy demand in China based on AGAEDE optimal model,” Chinese Journal of Population Resources and Environment, vol. 14, no. 1, pp. 16–29, 2016. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Hsu and C. Chen, “Applications of improved grey prediction model for power demand forecasting,” Energy Conversion and Management, vol. 44, no. 14, pp. 2241–2249, 2003. View at Publisher · View at Google Scholar · View at Scopus
  19. B. Dong, C. Cao, and S. E. Lee, “Applying support vector machines to predict building energy consumption in tropical region,” Energy and Buildings, vol. 37, no. 5, pp. 545–553, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. Y. S. Lee and L. I. Tong, “Forecasting nonlinear time series of energy consumption using a hybrid dynamic model,” Applied Energy, vol. 94, pp. 251–256, 2012. View at Publisher · View at Google Scholar · View at Scopus
  21. L. Tang, L. Yu, S. Wang, and J. Li, “A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting,” Applied Energy, vol. 93, pp. 432–443, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. T. Chae, R. Horesh, Y. Hwang, and Y. M. Lee, “Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings,” Energy and Buildings, vol. 111, pp. 184–194, 2016. View at Publisher · View at Google Scholar · View at Scopus
  23. M. E. Günay, “Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicators and climatic conditions: Case of Turkey,” Energy Policy, vol. 90, pp. 92–101, 2016. View at Publisher · View at Google Scholar · View at Scopus
  24. H. L. Chan and S. L. Lee, “Forecasting the demand for energy in China,” The Energy Journal, vol. 17, no. 1, pp. 19–30, 1996. View at Google Scholar · View at Scopus
  25. P. Chujai, N. Kerdprasop, and K. Kerdprasop, “Time series analysis of household electric consumption with ARIMA and ARMA models,” in Proceedings of the International MultiConference of Engineers and Computer Scientists 2013, IMECS 2013, pp. 295–300, Hong Kong, China, March 2013. View at Scopus
  26. M. Zhang, H. Mu, G. Li, and Y. Ning, “Forecasting the transport energy demand based on PLSR method in China,” Energy, vol. 34, no. 9, pp. 1396–1400, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. M. Meng, D. Niu, and W. Sun, “Forecasting monthly electric energy consumption using feature extraction,” Energies, vol. 4, no. 10, pp. 1495–1507, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. V. N. Vapnik, “An overview of statistical learning theory,” IEEE Transactions on Neural Networks and Learning Systems, vol. 10, no. 5, pp. 988–999, 1999. View at Publisher · View at Google Scholar · View at Scopus
  29. V. Vapnik, S. E. Golowich, and A. Smola, “Support vector method for function approximation, regression estimation, and signal processing,” in Proceedings of the 10th Annual Conference on Neural Information Processing Systems (NIPS '96), pp. 281–287, December 1996. View at Scopus
  30. Z. Wang, R. S. Srinivasan, and J. Shi, “Artificial intelligent models for improved prediction of residential space heating,” Journal of Energy Engineering, vol. 142, no. 4, Article ID 04016006, 2016. View at Publisher · View at Google Scholar · View at Scopus
  31. J. Kang and H. Zhao, “Application of improved grey model in long-term load forecasting of power engineering,” Systems Engineering Procedia, vol. 3, pp. 85–91, 2012. View at Publisher · View at Google Scholar
  32. W. Xu, R. Gu, Y. Liu, and Y. Dai, “Forecasting energy consumption using a new GM-ARMA model based on HP filter: the case of Guangdong Province of China,” Economic Modelling, vol. 45, pp. 127–135, 2015. View at Publisher · View at Google Scholar · View at Scopus
  33. Z. Wang and R. S. Srinivasan, “A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models,” Renewable & Sustainable Energy Reviews, vol. 75, pp. 796–808, 2017. View at Publisher · View at Google Scholar · View at Scopus
  34. R. F. Engle and C. W. J. Granger, “Co-integration and error correction: representation, estimation, and testing,” Econometrica, vol. 55, no. 2, pp. 251–276, 1987. View at Publisher · View at Google Scholar
  35. S. Johansen and K. Juselius, “Maximum likelihood estimation and inference on cointegration—with applications to the demand for money,” Oxford Bulletin of Economics and Statistics, vol. 52, no. 2, pp. 169–210, 1990. View at Publisher · View at Google Scholar
  36. M. H. Pesaran, Y. Shin, and R. J. Smith, “Bounds testing approaches to the analysis of level relationships,” Journal of Applied Econometrics, vol. 16, no. 3, pp. 289–326, 2001. View at Publisher · View at Google Scholar · View at Scopus
  37. P. K. Narayan, “The saving and investment nexus for China: evidence from cointegration tests,” Applied Economics, vol. 37, no. 17, pp. 1979–1990, 2005. View at Publisher · View at Google Scholar · View at Scopus
  38. T. W. Feng, L. Y. Sun, and Y. Zhang, “The relationship between energy consumption structure, economic structure and energy intensity in China,” Energy Policy, vol. 37, no. 12, pp. 5475–5483, 2009. View at Publisher · View at Google Scholar · View at Scopus
  39. Y. Li, L. Sun, T. Feng, and C. Zhu, “How to reduce energy intensity in China: A regional comparison perspective,” Energy Policy, vol. 61, pp. 513–522, 2013. View at Publisher · View at Google Scholar · View at Scopus
  40. S. Ng and P. Perron, “Lag length selection and the construction of unit root tests with good size and power,” Econometrica, vol. 69, no. 6, pp. 1519–1554, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  41. The World Bank, 2012. China 2030: Building a modern, harmonious, and creative high-income society. USA.