Forecasting of IRAN Power Demand Network by hybrid of Support Vector Regression model and Fruit fly Optimization Algorithm

Document Type: Research Paper

Authors

1 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

2 Department of Industrial engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

Abstract

Accurate monthly power demand network forecasting can help to plan the energy and it can handle the correct management of the power consumption. It has been found that the monthly electricity consumption demonstrates a complex nonlinear characteristic and has an obvious seasonal tendency. One of the models that is widely used to predict the nonlinear time series is the support vector regression model (SVR) in which the selection of key parameters and the effect of seasonal changes could be considered. The important issues in this research are to determine the parameters of the support vector regression model optimally, as well as the adjustment of the nonlinear and seasonal trends of the electricity data. The method that is proposed by this study is to hybrid the support vector regression model (SVR) with Fruit fly optimization Algorithm (FOA) and the seasonal index adjustment to forecast the monthly power demand. In addition, in order to evaluate the performance of the hybrid predictive model a small sample of the monthly power demand from Iran and a large sample of Iran monthly electricity production has been used to demonstrate the predictive model performance. This study also evaluates the superiority of the SFOASVR model to the other known predictive methods. In terms of the prediction accuracy, we used the evaluation criteria such as Root Mean Square Error (RMSE) and mean absolute percentage error (MAPE) as well as Wilcoxon's nonparametric statistical test. The results show that the SFOASVR model has less error than the other forecasting models and is superior to the most other models in terms of Wilcoxon test. Therefore, SFOASVR method is an appropriate option for prediction of the power demand.

Keywords

Main Subjects


  1. (2016). Detailed Statistics of Iran's Power Industry Specially Produced In 1394, Tehran, Tavanir Specialized Parent Company.
  2. Ssaadatpisheh, S. (2012). Estimation of Urban Traffic Using the SVR Model by Ant Colony Optimization Algorithm, Master's Thesis, Operational Research, Faculty of Science, Shiraz University of Technology.
  3. Site of Tavanir Specialist Company, Joint Stock Company for Production, Transfer and Distribution of Iran's Power: Http://Tavanir.Org.Ir.
  4. Kiani, M. et al. (2015). “An Efficient Genetic Algorithm for the Routing of Vehicles, Considering the Skills of Team Workers”, Journal of Industrial Engineering., Vol. 49, No. 2, PP. 257-277.
  5. Mirtalayi, M. et al. (2012). “A Trust-Based Intelligent Algorithm for Determining Customer Values in a Financial System”, Journal of Industrial Engineering, Vol. 46, No. 1, PP. 91-104.
  6. Tan, P.  et al. (2015). “Estimation of Higher Heating Value of Coal Based on Proximate Analysis Using Support Vector Regression”, Fuel Processing Technology, Vol. 138, PP. 298–304.
  7. Pan, W,T. )2012(. “A New Fruit Fly Optimization Algorithm: Taking the financial Distress Model As An Example”, Knowledge-Based Systems.,Vol. 26, PP.69–74.
  8. Li, T. et al. (2016). “An Ensemble Fruit Fly Optimization Algorithm for Solving Range Image Registration to Improve Quality Inspection of Free-Form Surface Parts”, Information Sciences, Vol. 367–368, PP. 953–974.
  9. Aladag, C. H. (2011). “A New Architecture Selection Method Based on Tabu Search for Artificial Neural Networks”, Expert Systems with Applications, Vol. 38, No. 4, PP. 3287–3293.
  10. Wang, J. et al. (2012). “The Model of Chaotic Sequences Based on Adaptive Particle Swarm Optimization Arithmetic Combined with Seasonal Term”, Applied Mathematical Modelling, Vol. 36, No. 3, PP. 1184–1196.
  11. Kıran, M. S.  et al. (2012). “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.
  12. Lee, S., and Choi, W. S. (2013). “A Multi-Industry Bankruptcy Prediction Model Using Back-Propagation Neural Network and Multivariate Discriminant Analysis”, Expert Systems with Applications, Vol. 40, No. 8, PP. 2941–2946.
  13. Hong, W. C. et al. (2013). “Cyclic Electric Load Forecasting by Seasonal SVR with Chaotic Genetic Algorithm”, Electrical Power and Energy Systems, Vol. 44, No. 1, PP. 604–614.
  14. Xiong. T, Bao. Y., and Hu. Z. (2014). “Interval Forecasting of Electricity Demand: A Novel Bivariate EMD-Based Support Vector Regression Modeling Framework”, Electrical Power and Energy Systems, Vol. 63, PP. 353–362.
  15. Meng, Q., Ma., X., and Zhou, Y. (2014). “Forecasting of Coal Seam Gas Content by Using Support Vector Regression Based on Particle Swarm Optimization”, Journal of Natural Gas Science and Engineering, Vol. 21, PP. 71-78.
  16. Jamian, J. J. et al. (2014). “Simulation Study on Optimal Placement and Sizing of Battery Switching Station Units Using Artificial Bee Colony Algorithm”, Electrical Power and Energy Systems, Vol. 55, PP. 592–601.
  17. Yan, X. and Chowdhury, N. A. (2014). “Mid-Term Electricity Market Clearing Price Forecasting: A Multiple Svmapproach”, Electrical Power and Energy Systems, Vol. 58, PP. 206–214.
  18. Cai, Q.  et al. (2015). “A New Fuzzy Time Series Forecasting Model Combined with Ant Colony Optimization and Auto-Regression”, Knowledge-Based Systems, Vol. 74, PP. 61–68.
  19. Li, K. et al.  (2015). “Building’s Electricity Consumption Prediction Using Optimized Artificial Neural Networks and Principal Component Analysis”, Energy and Buildings, Vol. 108, PP. 106–113.
  20. Zhu, L. et al. (2015). “Short-Term Natural Gas Demand Prediction Based on Support Vector Regression with False Neighbours filtered”, Energy, Vol. 80, PP. 428-436.
  21. Wolff, B. et al. (2016). “Comparing Support Vector Regression for PV Power Forecasting to a Physical Modeling Approach Using Measurement, Numerical Weather Prediction, and Cloud Motion Data”, Solar Energy, Vol. 135, PP. 197–208.
  22. Sun, W., and Xu, Y. (2016). “Financial Security Evaluation of the Electric Power Industry in China Based on a Back Propagation Neural Network Optimized by Genetic Algorithm”, Energy, Vol. 101, PP. 366-379.
  23. Dong, Z. et al. (2015). “A Novel Hybrid Approach Based on Self-Organizing Maps, Support Vector Regression and Particle Swarm Optimization to Forecast Solar Irradiance”, Energy, Vol. 82, PP. 570-577
  24. Jiang, B. T. and Zhao, F. Y. (2013). “Particle Swarm Optimization-Based Least Squares Support Vector Regression for Critical Heat Flux Prediction”, Annals of Nuclear Energy, Vol. 53, PP. 69–81.
  25. Cao, G. and Wu, L. (2016). “Support Vector Regression with Fruit fly Optimization Algorithm for Seasonal Electricity Consumption Forecasting”, Energy, Vol. 115, PP. 734-745.
  26. Wua, C., Tzeng, G., and Lin, R. (2009). “A Novel Hybrid Genetic Algorithm for Kernel Function and Parameter Optimization in Support Vector Regression”, Expert Systems with Applications, Vol. 36, PP. 4725-4735.
  27. Li, H. et al. (2013). “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.
  28. Niu, J. et al. (2015). “Fruit Fly Optimization Algorithm Based on Differential Evolution and Its Application on Gasification Process Operation Optimization”, Knowledge-Based Systems,Vol. 88, PP. 253-263.
  29. Deo, R., Hurvich, C., and Lu, Y. (2006). “Forecasting Realized Volatility Using a Long-Memory Stochastic Volatility Model: Estimation, Prediction and Seasonal Adjustment”, Journal of Econometrics, Vol. 131, No. 1 and 2, PP. 29–58.
  30. Wei, Y., and Cao, Y. (2017). “Forecasting House Prices Using Dynamic Model Averaging Approach: Evidence from China”, Economic Modelling, Vol. 61, PP. 147–155.
  31. Ghose, D. K., Panda, S. S., and Swain, P. C. (2010). “Prediction of Water Table Depth in Western Region, Orissa Using BPNN and RBFN Neural Networks”, Journal of Hydrology, Vol. 394, No. 3 and 4, PP. 296–304.
  32. Perolat, J. et al. (2015). “Generalizing the Wilcoxon Rank-Sum Test for Interval Data”, International Journal of Approximate Reasoning, Vol. 56, PP. 108–121.
  33. Myttenaere, A. et al. (2016). “Mean Absolute Percentage Error For Regression Models”, Neurocomputing, Vol. 192, PP. 38–48.