Abstract
Accurate short-term load forecasting (STLF) is crucial for reliable operation of a power system. Back propagation neural network (BPNN) is widely used in the forecasting field because of its powerful approximation capability. However, due to a variety of unstable factors, electrical time series often exhibit highly noisy and nonlinear characteristics. Usually, a large deviation will be produced when employing single BPNN to capture the complex data pattern. To solve this problem, this paper proposes a new hybrid forecasting approach that combines ensemble empirical mode decomposition (EEMD), chaotic self-adaptive flower pollination algorithm (CSFPA) and BPNN. EEMD is employed to decompose the original load series with the purpose of reducing the forecasting complexity. Developed CSFPA uses logistic equation to produce the chaotic initial population. In addition, aiming at providing a better optimization capability, CSFPA calculates the self-adaptive switch probability at each iteration. The best initial weights and biases of BPNN are provided by the optimization result of CSFPA. The performance of the proposed method is validated by two real-world load data sets from different electricity markets. The numerical results demonstrate that the proposed method outperforms three advanced methods; it is an effective and promising method for STLF.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Abbreviations
- STLF:
-
Short-term load forecasting
- ANN:
-
Artificial neural network
- BPNN:
-
Back propagation neural network
- SVR:
-
Support vector regression
- SOM:
-
Self-organized map
- RBF:
-
Radial basis function
- ARIMA:
-
Autoregressive integrated moving average
- SARIMA:
-
Seasonal autoregressive integrated moving average
- EMD:
-
Empirical mode decomposition
- EEMD:
-
Ensemble empirical mode decomposition
- IMF:
-
Intrinsic mode function
- WT:
-
Wavelet transform
- PSO:
-
Particle swarm optimization
- SSO:
-
Shark smell optimization
- FPA:
-
Flower pollination algorithm
- HBMO:
-
Honey bee mating optimization
- MA:
-
Memetic algorithm
- MAE:
-
Mean absolute error
- RMSE:
-
Root-mean-square error
- MAPE:
-
Mean absolute percentage error
References
2012 India blackouts (2012). Wikipedia. https://en.wikipedia.org/wiki/2012_India_blackouts. Accessed 1 Aug 2017
Four Nigerian states in total darkness as national grid collapses (2016). P.M. News. https://www.pmnewsnigeria.com/2016/06/20/four-nigerian-states-in-total-darkness-as-national-grid-collapses/. Accessed 1 Aug 2017
Deihimi A, Orang O, Showkati H (2013) Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57:382–401. doi:10.1016/j.energy.2013.06.007
Kavousi-Fard A, Samet H, Marzbani F (2014) A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst Appl 41(13):6047–6056. doi:10.1016/j.eswa.2014.03.053
Taylor JW (2012) Short-term load forecasting with exponentially weighted methods. IEEE Trans Power Syst 27(1):458–464. doi:10.1109/TPWRS.2011.2161780
Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421. doi:10.1016/j.energy.2009.06.034
Vu DH, Muttaqi KM, Agalgaonkar AP (2015) A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables. Appl Energy 140:385–394. doi:10.1016/j.apenergy.2014.12.011
Al-Hamadi HM, Soliman SA (2004) Short-term electric load forecasting based on Kalman filtering algorithm with moving window weather and load model. Electr Power Syst Res 68(1):47–59. doi:10.1016/S0378-7796(03)00150-0
Guan C, Luh PB, Michel LD, Chi Z (2013) Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation. IEEE Trans Power Syst 28(4):3806–3817. doi:10.1109/TPWRS.2013.2264488
Shyh-Jier H, Kuang-Rong S (2003) Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans Power Syst 18(2):673–679. doi:10.1109/TPWRS.2003.811010
Boroojeni KG, Amini MH, Bahrami S, Iyengar SS, Sarwat AI, Karabasoglu O (2017) A novel multi-time-scale modeling for electric power demand forecasting: from short-term to medium-term horizon. Electr Power Syst Res 142:58–73. doi:10.1016/j.epsr.2016.08.031
Kelo S, Dudul S (2012) A wavelet Elman neural network for short-term electrical load prediction under the influence of temperature. Int J Electr Power Energy Syst 43(1):1063–1071. doi:10.1016/j.ijepes.2012.06.009
Awan SM, Aslam M, Khan ZA, Saeed H (2014) An efficient model based on artificial bee colony optimization algorithm with Neural Networks for electric load forecasting. Neural Comput Appl 25(7):1967–1978. doi:10.1007/s00521-014-1685-y
Hu R, Wen S, Zeng Z, Huang T (2017) A short-term power load forecasting model based on the generalized regression neural network with decreasing step fruit fly optimization algorithm. Neurocomputing 221:24–31. doi:10.1016/j.neucom.2016.09.027
Jain A, Jain MB, Srinivas E (2010) A novel hybrid method for short term load forecasting using fuzzy logic and particle swarm optimization. In: 2010 international conference on power system technology, 24–28 October 2010, pp 1–7. doi:10.1109/POWERCON.2010.5666080
Çevik HH, Çunkaş M (2015) Short-term load forecasting using fuzzy logic and ANFIS. Neural Comput Appl 26(6):1355–1367. doi:10.1007/s00521-014-1809-4
Niu D, Wang Y, Wu DD (2010) Power load forecasting using support vector machine and ant colony optimization. Expert Syst Appl 37(3):2531–2539. doi:10.1016/j.eswa.2009.08.019
Rahman S, Bhatnagar R (1988) An expert system based algorithm for short term load forecast. IEEE Trans Power Syst 3(2):392–399. doi:10.1109/59.192889
Kwang-Ho K, Jong-Keun P, Kab-Ju H, Sung-Hak K (1995) Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems. IEEE Trans Power Syst 10(3):1534–1539. doi:10.1109/59.466492
Fan S, Chen L (2006) Short-term load forecasting based on an adaptive hybrid method. IEEE Trans Power Syst 21(1):392–401. doi:10.1109/TPWRS.2005.860944
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. doi:10.1016/j.energy.2009.12.015
Bashir ZA, El-Hawary ME (2009) Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Trans Power Syst 24(1):20–27. doi:10.1109/TPWRS.2008.2008606
Dedinec A, Filiposka S, Dedinec A, Kocarev L (2016) Deep belief network based electricity load forecasting: an analysis of Macedonian case. Energy 115. Part 3:1688–1700. doi:10.1016/j.energy.2016.07.090
Panapakidis IP (2016) Application of hybrid computational intelligence models in short-term bus load forecasting. Expert Syst Appl 54:105–120. doi:10.1016/j.eswa.2016.01.034
Abedinia O, Amjady N (2016) Short-term load forecast of electrical power system by radial basis function neural network and new stochastic search algorithm. Int Trans Electr Energy Syst 26(7):1511–1525. doi:10.1002/etep.2160
Hu Z, Bao Y, Xiong T (2014) Comprehensive learning particle swarm optimization based memetic algorithm for model selection in short-term load forecasting using support vector regression. Appl Soft Comput 25:15–25. doi:10.1016/j.asoc.2014.09.007
Saleh S, Mohammadi S, Rostami M-A, Askari M-R (2014) A hybrid artificial-based model for accurate short term electric load prediction. J Intell Fuzzy Syst 27(6):3103–3110. doi:10.3233/IFS-141267
Yang X-S (2012) Flower pollination algorithm for global optimization. In: Durand-Lose J, Jonoska N (eds) Unconventional computation and natural computation: 11th international conference, UCNC 2012, Orléan, France, September 3–7, 2012. Proceedings. Springer, Berlin, pp 240–249. doi:10.1007/978-3-642-32894-7_27
Wu Z, Huang NE (2009) Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv Adapt Data Anal 01(01):1–41. doi:10.1142/S1793536909000047
Hecht-Nielsen R (1989) Theory of the backpropagation neural network. In: International 1989 joint conference on neural networks, 0–0 1989 1989, vol 591, pp 593–605. doi:10.1109/IJCNN.1989.118638
Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the international conference on Neural Networks, 1987. IEEE Press, New York, pp 11–13
Acknowledgements
This research was supported by the National Natural Science Foundation of China (No. 61602225) and Introduce Talents Science Projects for Northwest University for Nationalities (No. xbmuyjrcs201616).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Rights and permissions
About this article
Cite this article
Pan, L., Feng, X., Sang, F. et al. An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting. Neural Comput & Applic 31, 2679–2697 (2019). https://doi.org/10.1007/s00521-017-3222-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00521-017-3222-2