Abstract and Applied Analysis

Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model

Huiru Zhao and Sen Guo

Full-text: Open access

Abstract

Accurate energy consumption forecasting can provide reliable guidance for energy planners and policy makers, which can also recognize the economic and industrial development trends of a country. In this paper, a hybrid PSOCA-GRNN model was proposed for the annual energy consumption forecasting. The generalized regression neural network (GRNN) model was employed to forecast the annual energy consumption due to its good ability of dealing with the nonlinear problems. Meanwhile, the spread parameter of GRNN model was automatically determined by PSOCA algorithm (the combination of particle swarm optimization algorithm and cultural algorithm). Taking China’s annual energy consumption as the empirical example, the effectiveness of this proposed PSOCA-GRNN model was proved. The calculation result shows that this proposed hybrid model outperforms the single GRNN model, GRNN model optimized by PSO (PSO-GRNN), discrete grey model (DGM (1, 1)), and ordinary least squares linear regression (OLS_LR) model.

Article information

Source
Abstr. Appl. Anal., Volume 2014, Special Issue (2014), Article ID 217630, 11 pages.

Dates
First available in Project Euclid: 2 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aaa/1412279022

Digital Object Identifier
doi:10.1155/2014/217630

Zentralblatt MATH identifier
07021951

Citation

Zhao, Huiru; Guo, Sen. Annual Energy Consumption Forecasting Based on PSOCA-GRNN Model. Abstr. Appl. Anal. 2014, Special Issue (2014), Article ID 217630, 11 pages. doi:10.1155/2014/217630. https://projecteuclid.org/euclid.aaa/1412279022


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References

  • L. Ma, P. Liu, F. Fu, Z. Li, and W. Ni, “Integrated energy strategy for the sustainable development of China,” Energy, vol. 36, no. 2, pp. 1143–1154, 2011.
  • R. Schaeffer, A. S. Szklo, A. F. Pereira de Lucena et al., “Energy sector vulnerability to climate change: a review,” Energy, vol. 38, no. 1, pp. 1–12, 2012.
  • S. Yu, Y. Wei, and K. Wang, “China's primary energy demands in 2020: predictions from an MPSO-RBF estimation model,” Energy Conversion and Management, vol. 61, pp. 59–66, 2012.
  • V. Ş. Ediger and S. Akar, “ARIMA forecasting of primary energy demand by fuel in Turkey,” Energy Policy, vol. 35, no. 3, pp. 1701–1708, 2007.
  • U. Kumar and V. K. Jain, “Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India,” Energy, vol. 35, no. 4, pp. 1709–1716, 2010.
  • H. T. Pao, H. C. Fu, and C. L. Tseng, “Forecasting of CO$_{2}$ emis-sions, energy consumption and economic growth in China using an improved grey model,” Energy, vol. 40, no. 1, pp. 400–409, 2012.
  • Y. Huang, Y. J. Bor, and C. Peng, “The long-term forecast of Taiwan's energy supply and demand: LEAP model application,” Energy Policy, vol. 39, no. 11, pp. 6790–6803, 2011.
  • A. Ünler, “Improvement of energy demand forecasts using swarm intelligence: the case of Turkey with projections to 2025,” Energy Policy, vol. 36, no. 6, pp. 1937–1944, 2008.
  • A. H. Neto and F. A. S. Fiorelli, “Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption,” Energy and Buildings, vol. 40, no. 12, pp. 2169–2176, 2008.
  • M. S. K\iran, E. Özceylan, M. Gündüz et al., “A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey,” Energy Conversion and Management, vol. 53, no. 1, pp. 75–83, 2012.
  • H. Li, S. Guo, H. Zhao, C. Su, and B. Wang, “Annual electric load forecasting by a least squares support vector machine with a fruit fly optimization algorithm,” Energies, vol. 5, no. 11, pp. 4430–4445, 2012.
  • J. M. L. Sloughter, T. Gneiting, and A. E. Raftery, “Probabilistic wind spread forecasting using ensembles and Bayesian model averaging,” Journal of the American Statistical Association, vol. 105, no. 489, pp. 25–35, 2010.
  • C. Yeh, C. Huang, and S. Lee, “A multiple-kernel support vector regression approach for stock market price forecasting,” Expert Systems with Applications, vol. 38, no. 3, pp. 2177–2186, 2011.
  • C. Y. Chen, W. I. Lee, H. M. Kuo, and K. Chen, “The study of a forecasting sales model for fresh food,” Expert Systems with Applications, vol. 37, no. 12, pp. 7696–7702, 2010.
  • E. Hadavandi, H. Shavandi, and A. Ghanbari, “Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting,” Knowledge-Based Systems, vol. 23, no. 8, pp. 800–808, 2010.
  • D. A. Fadare, “The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria,” Applied Energy, vol. 87, no. 3, pp. 934–942, 2010.
  • L. Guo, D. Rivero, and A. Pazos, “Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks,” Journal of Neuroscience Methods, vol. 193, no. 1, pp. 156–163, 2010.
  • D. Singhal and K. S. Swarup, “Electricity price forecasting using artificial neural networks,” International Journal of Electrical Power and Energy Systems, vol. 33, no. 3, pp. 550–555, 2011.
  • D. F. Specht, “A general regression neural networkčommentComment on ref. [19?]: We deleted reference [33] in the original manuscript, which was a repetition of [19?]. Please check.,” IEEE Transactions on Neural Networks, vol. 2, no. 6, pp. 568–576, 1991.
  • R. Singh, V. Vishal, T. N. Singh, and P. G. Ranjith, “A comparative study of generalized regression neural network approach and adaptive neuro-fuzzy inference systems for prediction of unconfined compressive strength of rocks,” Neural Computing and Applications, vol. 23, no. 2, pp. 499–506, 2013.
  • M. T. Leung, A. S. Chen, and H. Daouk, “Forecasting exchange rates using general regression neural networks,” Computers and Operations Research, vol. 27, no. 11-12, pp. 1093–1110, 2000.
  • W. Y. Lee, J. M. House, and N. H. Kyong, “Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks,” Applied Energy, vol. 77, no. 2, pp. 153–170, 2004.
  • H. K. Cigizoglu and M. Alp, “Generalized regression neural network in modelling river sediment yield,” Advances in Engineering Software, vol. 37, no. 2, pp. 63–68, 2006.
  • K. Nose-Filho, A. D. P. Lotufo, and C. R. Minussi, “Short-term multinodal load forecasting using a modified general regression neural network,” IEEE Transactions on Power Delivery, vol. 26, no. 4, pp. 2862–2869, 2011.
  • E. Orhan, F. Temurtas, and A. Ç. Tanrikulu, “Tuberculosis dis-ease diagnosis using artificial neural networks,” Journal of Medical Systems, vol. 34, no. 3, pp. 299–302, 2010.
  • T. Chen and C. Yu, “Motion control with deadzone estimation and compensation using GRNN for TWUSM drive system,” Expert Systems with Applications, vol. 36, no. 8, pp. 10931–10941, 2009.
  • S. C. Chelgani and E. Jorjani, “Microwave irradiation pretreatment and peroxyacetic acid desulfurization of coal and application of GRNN simultaneous predictor,” Fuel, vol. 90, no. 11, pp. 3156–3163, 2011.
  • H. Li, S. Guo, C. Li, and J. Sun, “A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm,” Knowledge-Based Systems, vol. 37, pp. 378–387, 2013.
  • R. C. Eberhart and J. Kennedy, “A new optimizer using particle swarm theory,” in Proceedings of the 6th International Symposium on Micro Machine and Human Science, vol. 1, pp. 39–43, 1995.
  • T. A. A. Victoire and A. E. Jeyakumar, “Hybrid PSO-SQP for economic dispatch with valve-point effect,” Electric Power Systems Research, vol. 71, no. 1, pp. 51–59, 2004.
  • B. Liu, L. Wang, and Y. Jin, “An effective PSO-based memetic algorithm for flow shop scheduling,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 37, no. 1, pp. 18–27, 2007.
  • Z. A. Bashir and M. E. El-Hawary, “Applying wavelets to short-term load forecasting using PSO-based neural networks,” IEEE Transactions on Power Systems, vol. 24, no. 1, pp. 20–27, 2009.
  • Ö. Polat and T. Y\ild\ir\im, “Genetic optimization of GRNN for pattern recognition without feature extraction,” Expert Systems with Applications, vol. 34, no. 4, pp. 2444–2448, 2008.
  • M. Theodosiou, “Disaggregation & aggregation of time series components: a hybrid forecasting approach using generalized regression neural networks and the theta method,” Neurocomputing, vol. 74, no. 6, pp. 896–905, 2011.
  • H. B. Celikoglu and H. K. Cigizoglu, “Public transportation trip flow modeling with generalized regression neural networks,” Advances in Engineering Software, vol. 38, no. 2, pp. 71–79, 2007.
  • R. G. Reynolds, “An introduction to cultural algorithms,” in Proceedings of the 3rd Annual Conference on Evolutionary Programming, pp. 131–139, Singapore, 1994.
  • Z. Wu and J. Xu, “Predicting and optimization of energy con-sumption using system dynamics-fuzzy multiple objective programming in world heritage areas,” Energy, vol. 49, no. 1, pp. 19–31, 2013.
  • M. Kankal, A. Akpinar, M. Kömürcü et al., “Modeling and forecasting of Turkeys energy consumption using socio-economic and demographic variables,” Applied Energy, vol. 88, no. 5, pp. 1927–1939, 2011.
  • Y. S. Lee and L. I. Tong, “Forecasting energy consumption using a grey model improved by incorporating genetic programming,” Energy Conversion and Management, vol. 52, no. 1, pp. 147–152, 2011. \endinput