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Modeling of electricity demand forecast for power system

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Abstract

The emerging complex circumstances caused by economy, technology, and government policy and the requirement of low-carbon development of power grid lead to many challenges in the power system coordination and operation. However, the real-time scheduling of electricity generation needs accurate modeling of electricity demand forecasting for a range of lead times. In order to better capture the nonlinear and non-stationary characteristics and the seasonal cycles of future electricity demand data, a new concept of the integrated model is developed and successfully applied to research the forecast of electricity demand in this paper. The proposed model combines adaptive Fourier decomposition method, a new signal preprocessing technology, for extracting useful element from the original electricity demand series through filtering the noise factors. Considering the seasonal term existing in the decomposed series, it should be eliminated through the seasonal adjustment method, in which the seasonal indexes are calculated and should multiply the forecasts back to restore the final forecast. Besides, a newly proposed moth-flame optimization algorithm is used to ensure the suitable parameters of the least square support vector machine which can generate the forecasts. Finally, the case studies of Australia demonstrated the efficacy and feasibility of the proposed integrated model. Simultaneously, it can provide a better concept of modeling for electricity demand prediction over different forecasting horizons.

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References

  1. Fan S, Chen LN (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21:392–401

    Article  Google Scholar 

  2. Sheikhan M, Mohammadi N (2013) Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data. Neural Comput Appl 23(3–4):1185–1194

    Article  Google Scholar 

  3. Ying LC, Pan MC (2008) Using adaptive network based fuzzy inference system to forecast regional electricity loads. Energy Convers Manag 49(2):205–211

    Article  MathSciNet  Google Scholar 

  4. Pappas SS, Ekonomou L, Karamousantas DC, Chatzarakis GE, Katsikas SK, Liatsis P (2008) Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models. Energy 33(9):1353–1360

    Article  Google Scholar 

  5. Fang TT, Lahdelma R (2016) Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system. Appl Energy 179:544–552

    Article  Google Scholar 

  6. Huang YJ, Wang H, Khajepour A, He H, Ji J (2017) Model predictive control power management strategies for HEVs: a review. J Power Sources 341:91–106

    Article  Google Scholar 

  7. Xie NM, Yuan CQ, Yang YJ (2015) Forecasting China’s energy demand and self-sufficiency rate by grey forecasting model and Markov model. Int J Electr Power Energy Syst 66:1–8

    Article  Google Scholar 

  8. Wang J, Zhu S, Zhang W, Lu H (2010) Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy 35(4):1671–1678

    Article  Google Scholar 

  9. Xia C, Wang J, McMenemy K (2010) Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks. Int J Electr Power Energy Syst 32(7):743–750

    Article  Google Scholar 

  10. Bercu S, Proïa F (2013) A SARIMAX coupled modelling applied to individual load curves intraday forecasting. J Appl Stat 40(6):1333–1348

    Article  MathSciNet  Google Scholar 

  11. Hsu CC, Chen CY (2003) Regional load forecasting in Taiwan—applications of artificial neural networks. Energy Convers Manag 44(12):1941–1949

    Article  Google Scholar 

  12. Qiu XH, Suganthan PN, Amaratunga GAJ (2018) Ensemble incremental learning Random Vector Functional Link network for short-term electric load forecasting. Knowl Based Syst 145:182–196

    Article  Google Scholar 

  13. Zhang H, Li J, Ji Y, Yue H (2016) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inform 13(2):616–624

    Article  Google Scholar 

  14. Xiao L, Wang JZ, Dong Y, Wu J (2015) Combined forecasting models for wind energy forecasting: a case study in China. Renew Sustain Energy Rev 44:271–288

    Article  Google Scholar 

  15. Wang JZ, Chi DZ, Wu J, Lu HY (2011) Chaotic time series method combined with particle swarm optimization and trend adjustment for electricity demand forecasting. Expert Syst Appl 38(7):8419–8429

    Article  Google Scholar 

  16. El-Telbany M, El-Karmi F (2008) Short-term forecasting of Jordanian electricity demand using particle swarm optimization. Electr Power Syst Res 78(3):425–433

    Article  Google Scholar 

  17. Azadeh A, Ghaderi SF, Tarverdian S, Saberi M (2007) Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption. Appl Math Comput 186(2):1731–1741

    MathSciNet  MATH  Google Scholar 

  18. Ghanbari A, Kazemi SMR, Mehmanpazir F, Nakhostin MM (2013) A cooperative ant colony optimization-genetic algorithm approach for construction of energy demand forecasting knowledge-based expert systems. Knowl Based Syst 39:194–206

    Article  Google Scholar 

  19. Hong WC (2010) Application of chaotic ant swarm optimization in electric load forecasting. Energy Policy 38(10):5830–5839

    Article  Google Scholar 

  20. Kiran MS, Ozceylan E, Gunduz M, Paksoy T (2012) A novel hybrid approach based on particle swarm optimization and ant colony algorithm to forecast energy demand of Turkey. Energy Convers Manag 53(1):75–83

    Article  Google Scholar 

  21. Reddy KS, Panwar LK, Panigrahi BK, Kumar R (2018) Solution to unit commitment in power system operation planning using binary coded modified moth flame optimization algorithm (BMMFOA): a flame selection based computational technique. J Comput Sci 25:298–317

    Article  MathSciNet  Google Scholar 

  22. Zhao J et al (2016) An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl Energy 162:808–826

    Article  Google Scholar 

  23. Jiang P, Li RR, Zhang KQ (2018) Two combined forecasting models based on singular spectrum analysis and intelligent optimized algorithm for short-term wind speed. Neural Comput Appl 30:1–19

    Article  Google Scholar 

  24. Pothiya S, Ngamroo I, Kongprawechnon W (2008) Application of multiple tabu search algorithm to solve dynamic economic dispatch considering generator constraints. Energy Convers Manag 49(4):506–516

    Article  Google Scholar 

  25. Hadji MM, Vahidi B (2012) A solution to the unit commitment problem using imperialistic competition algorithm. IEEE Trans Power Syst 27(1):117–124

    Article  Google Scholar 

  26. Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21(1):68–76

    Article  Google Scholar 

  27. Piltan M, Shiri H, Ghaderi SF (2012) Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms. Energy Convers Manag 58:1–9

    Article  Google Scholar 

  28. Barman M, Choudhury NBD, Sutradhar S (2018) A regional hybrid GOA-SVM model based on similar day approach for short-term load forecasting in Assam, India. Energy 145:710–720

    Article  Google Scholar 

  29. Zhang H, Cao X et al (2016) Object-level video advertising: an optimization framework. IEEE Trans Ind Inform 13(2):520–531

    Article  Google Scholar 

  30. Yadav V, Srinivasan D (2011) A SOM-based hybrid linear-neural model for short-term load forecasting. Neurocomputing 74(17):2874–2885

    Article  Google Scholar 

  31. Soares LJ, Souza LR (2006) Forecasting electricity demand using generalized long memory. Int J Forecast 22(1):17–28

    Article  Google Scholar 

  32. Bessec M, Fouquau J (2018) Short-run electricity load forecasting with combinations of stationary wavelet transforms. Eur J Oper Res 264(1):149–164

    Article  MathSciNet  MATH  Google Scholar 

  33. Jiang P, Liu F, Song YL (2017) A hybrid forecasting model based on date-framework strategy and improved feature selection technology for short-term load forecasting. Energy 119:694–709

    Article  Google Scholar 

  34. Andersen FM, Larsen HV, Boomsma TK (2013) Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energy Convers Manag 68(3):244–252

    Article  Google Scholar 

  35. Han H, Zhong Z, Wen C, Sun H (2018) Agricultural environmental total factor productivity in China under technological heterogeneity: characteristics and determinants. Environ Sci Pollut Res 25:32096–32111

    Article  Google Scholar 

  36. Zhang GP, Qi M (2005) Neural network forecasting for seasonal and trend time series. Eur J Oper Res 160(2):501–514

    Article  MathSciNet  MATH  Google Scholar 

  37. Wang JZ, Zhu WJ, Zhang WY, Sun DH (2009) A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand. Energy Policy 37(11):4901–4909

    Article  Google Scholar 

  38. Abdoos AA (2016) A new intelligent method based on combination of VMD and ELM for short term wind power forecasting. Neurocomputing 203:111–120

    Article  Google Scholar 

  39. Wang Z, Wan F, Wong CM, Zhang LM (2016) Adaptive Fourier decomposition based ECG denoising. Comput Biol Med 77:195–205

    Article  Google Scholar 

  40. Benitez D, Gaydecki PA, Zaidi A, Fitzpatrick AP (2001) The use of the Hilbert transform in ECG signal analysis. Comput Biol Med 31(5):399–406

    Article  Google Scholar 

  41. Seyedali M (2015) Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm. Knowl Based Syst 89:228–249

    Article  Google Scholar 

  42. Allam D, Yousri DA, Eteiba MB (2016) Parameters extraction of the three diode model for the multi-crystalline solar cell/module using moth-flame optimization algorithm. Energy Convers Manag 123:535–548

    Article  Google Scholar 

  43. Hassanien AE, Gaber T, Mokhtar U, Hefny H (2017) An improved moth flame optimization algorithm based on rough sets for tomato diseases detection. Comput Electron Agric 136:86–96

    Article  Google Scholar 

  44. Lin KP, Pai PF (2016) Solar power output forecasting using evolutionary seasonal decomposition least-square support vector regression. J Clean Prod 134:456–462

    Article  Google Scholar 

  45. Ahmadi MA, Masoumi M, Askarinezhad R (2015) Evolving Smart model to predict the combustion front velocity for in situ combustion. Energy Technol 3(2):128–135

    Article  Google Scholar 

  46. Moradi MH, Abedini M, Tousi SMR, Hosseinian SM (2015) Optimal siting and sizing of renewable energy sources and charging stations simultaneously based on differential evolution algorithm. Int J Electr Power Energy Syst 73:1015–1024

    Article  Google Scholar 

  47. Li B, Li DY, Zhang ZJ, Yang SM, Wang F (2015) Slope stability analysis based on quantum-behaved particle swarm optimization and least squares support vector machine. Appl Math Model 39(17):5253–5264

    Article  MathSciNet  MATH  Google Scholar 

  48. Mahani AS, Shojaee S, Salajegheh E, Khatibinia M (2015) Hybridizing two-stage meta-heuristic optimization model with weighted least squares support vector machine for optimal shape of double-arch dams. Appl Soft Comput 27:205–218

    Article  Google Scholar 

  49. Zhu BZ, Wei YM (2013) Carbon price forecasting with a hybrid ARIMA and least squares support vector machines methodology. Omega Int J Manag Sci 41(3):517–524

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Natural Science Foundation of China [Grant Number 71573034].

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Authors and Affiliations

  1. School of Statistics, Dongbei University of Finance and Economics, Dalian, 116025, China

    Ping Jiang, Ranran Li & Xiaobo Zhang

  2. School of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, Australia

    Haiyan Lu

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  1. Ping Jiang
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  2. Ranran Li
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  3. Haiyan Lu
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  4. Xiaobo Zhang
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Correspondence to Ranran Li.

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Jiang, P., Li, R., Lu, H. et al. Modeling of electricity demand forecast for power system. Neural Comput & Applic 32, 6857–6875 (2020). https://doi.org/10.1007/s00521-019-04153-5

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