research-article

A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction

Authors:

Lian LiAuthors Info & Claims

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

Pages 91 - 103

https://doi.org/10.1145/3318299.3318332

Published: 22 February 2019 Publication History

Abstract

To further increase prediction accuracy, improve power management and reduce waste, this paper proposes a hybrid electric load forecasting model based on wavelet analysis (WA) and least squares support vector regression (LSSVR) with particle swarm optimization (PSO) algorithm. Where wavelet analysis is used to transform the original electric data sequence into multi-resolution subsets during the preprocessing stage and then the decomposed subsets are inserted into LSSVR to realize prediction, finally the ultimate prediction results are obtained via the wavelet reconstruction with all the independent prediction results. However, the key to influence forecasting accuracy is the parameters used in the LSSVR, in this paper PSO is used to optimize the kernel parameter δ and the regularization parameter γ of LSSVR and choose the appropriate parameters for the hybrid forecasting model. The effectiveness of the proposed hybrid model has been proved in electric load prediction; the prediction results show that the proposed hybrid model outperforms the Elman networks model, the radial basis function (RBF) neural network model and LSSVR optimized only with PSO. The hybrid model achieves satisfying results, the mean absolute percentage error (MAPE) with 0.907% and the coefficient of determination (R 2) with 0.9936, it offers a higher forecasting precision.

References

[1]

N. Amjady. 2007. Short-term bus load forecasting of power systems by a new hybrid method. Power Systems, IEEE Transactions on, 22 (2007) 333--341.

[2]

P. LI, M. LI, D.-c. LIU. 2006. Power Load Forecasting Based on Improved Regression {J}. Power System Technology, 1 (2006) 023.

[3]

H. Li, L. Cui, S. Guo. 2014. A Hybrid Short-Term Power Load Forecasting Model Based on the Singular Spectrum Analysis and Autoregressive Model. Advances in Electrical Engineering, 2014 (2014).

[4]

K.-B. Song, Y.-S. Baek, D.H. Hong, G. Jang. 2005. Short-term load forecasting for the holidays using fuzzy linear regression method. Power Systems, IEEE Transactions on, 20 (2005) 96--101.

[5]

S. Faqi. 2005. The Preliminary Analysis of Change of Elasticity Coefficient of Energy Consumption and the Relevant Causes of China {J}. Statistical Research, 5 (2005) 002.

[6]

M. T. Hagan, S. M. Behr. 1987. The time series approach to short term load forecasting. Power Systems, IEEE Transactions on, 2 (1987) 785--791.

[7]

M. Espinoza, C. Joye, R. Belmans, B. De Moor. 2005. Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series. Power Systems, IEEE Transactions on, 20 (2005) 1622--1630.

[8]

D. Ömer Faruk. 2010. A hybrid neural network and ARIMA model for water quality time series prediction. Engineering applications of artificial intelligence, 23 (2010) 586--594.

Digital Library

[9]

C.-C. Hsu, C.-Y. Chen. 2003. Regional load forecasting in Taiwan--applications of artificial neural networks. Energy Conversion and Management, 44 (2003) 1941--1949.

[10]

Z. Yun, Z. Quan, S. Caixin, L. Shaolan, L. Yuming, S. Yang. 2008. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. Power Systems, IEEE Transactions on, 23 (2008) 853--858.

[11]

X.-G. Peng, S.-F. Hu, D.-Y. Lu. 2011. Review on grid short-term load forecasting methods based on RBF neural network. Power System Protection and Control, 39 (2011) 144--148.

[12]

Z. Li, Y. Zhou, C. Cheng, Y. Li, K. Lai. 2014. Short term photovoltaic power generation forecasting using RBF neural network. Control and Decision Conference (2014 CCDC), The 26th Chinese, IEEE, 2014, pp. 2758--2763.

[13]

D. Niu, Y. Wang, D.D. Wu. 2010. Power load forecasting using support vector machine and ant colony optimization. Expert Syst. Appl., 37 (2010) 2531--2539.

Digital Library

[14]

L. Xiaohui, D. Qian. 2014. Application of LS-SVM in the Short-term Power Load Forecasting Based on QPSO, (2014).

[15]

} R. R. Wei, Z. Z. Wei, R. Rong, Y. Wang, J. D. Jiang, H. Yu. 2013. Short Term Load Forecasting Based on PCA and LS-SVM. Advanced Materials Research, 756 (2013) 4193--4197.

[16]

S. ZHU, C.-w. JIANG, Z.-j. HOU. 2005. Application of a Weather Component Based Elman Neural Network to Short-Term Load Forecasting {J}. Proceedings of Electric Power System and Automation, 1 (2005) 005.

[17]

R.-q. LI, Y. SUN, Z.-y. SUN, Z.-z. TANG. 2007. Short-term Load Multi-step Forecasting Based on PSRT and Elman Neural Network {J}. Proceedings of the Chinese Society of Universities for Electric Power System and Automation, 6 (2007) 018.

[18]

S. Fan, L. Chen. 2006. Short-term load forecasting based on an adaptive hybrid method. Power Systems, IEEE Transactions on, 21 (2006) 392--401.

[19]

M. Hanmandlu, B.K. Chauhan. 2011. Load forecasting using hybrid models. Power Systems, IEEE Transactions on, 26 (2011) 20--29.

[20]

Q. Wu. 2011. Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts. Expert Syst. Appl., 38 (2011) 379--385.

Digital Library

[21]

K. Lee, Y. Cha, J. Park. 1992. Short-term load forecasting using an artificial neural network. Power Systems, IEEE Transactions on, 7 (1992) 124--132.

[22]

M. Mustafa, M. Sulaiman, H. Shareef, S.A. Khalid. 2012. Reactive power tracing in pool-based power system utilising the hybrid genetic algorithm and least squares support vector machine. IET generation, transmission & distribution, 6 (2012) 133--141.

[23]

M. Afshin, A. Sadeghian, K. Raahemifar. 2007. On efficient tuning of LS-SVM hyper-parameters in short-term load forecasting: A comparative study. Power Engineering Society General Meeting, 2007. IEEE, IEEE, 2007, pp. 1--6.

[24]

Y. Li, C. Li, M. Zheng. 2012. The Agricultural Product Wholesale Price Index Forecasting Model Based on LSSVR Optimized by PSO. Journal of Convergence Information Technology, 7 (2012).

[25]

L. Feng, H. Jinquan, Q. Xiaojie. 2009. Application of multi-outputs LSSVR by PSO to the aero-engine model. Systems Engineering and Electronics, Journal of, 20 (2009) 1153--1158.

[26]

N. Allias, M.N.M.M. Noor, M.N. Ismail, K. de Silva, A Hybrid Gini PSO-SVM Feature Selection: An Empirical Study of Population Sizes on Different Classifier, (2014).

Digital Library

[27]

D. Min, Z. Jiuwen, M. Yide. 2015. Image denoising via bivariate shrinkage function based on a new structure of dual contourlet transform. Signal Process., 109 (2015) 25--37.

Digital Library

[28]

C. Torrence, G.P. Compo, A practical guide to wavelet analysis, Bulletin of the American Meteorological society, 79 (1998) 61--78.

[29]

N. Amjady, F. Keynia. 2000. Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm, Energy, 34 (2009) 46--57.

[30]

C. Guan, P. B. Luh, L. D. Michel, Y. Wang, P. B. 2013. Friedland, Very short-term load forecasting: wavelet neural networks with data pre-filtering. Power Systems, IEEE Transactions on, 28 (2013) 30--41.

[31]

S. Kouhi, F. Keynia. 2013. A new cascade NN based method to short-term load forecast in deregulated electricity market. Energy Conversion and Management, 71 (2013) 76--83.

[32]

http://en.wikipedia.org/wiki/New_South_Wales.

[33]

J.W. Zhang, Dong, M., Yuan, M., et al. 2013. A New Structure of complex contourlet transform and its application in denoising and texture retrieval. Proceedings of the IASTED International Conference on Signal and Image Processing, SIP 2012, (2013) 127--137.

[34]

I. Daubechies. 1990. The wavelet transform, time-frequency localization and signal analysis. Information Theory, IEEE Transactions on, 36 (1990) 961--1005.

Digital Library

[35]

C. Cortes, V. Vapnik. 1995. Support-vector networks. Machine learning, 20 (1995) 273--297.

Digital Library

[36]

J.A. Suykens, J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural processing letters, 9 (1999) 293--300.

Digital Library

[37]

R.C. Eberhart, J. Kennedy. 1995. A new optimizer using particle swarm theory. Proceedings of the sixth international symposium on micro machine and human science, New York, NY, 1995, pp. 39--43.

[38]

H.-W. Ge, F. Qian, Y.-C. Liang, W.-l. Du, L. Wang. 2008. Identification and control of nonlinear systems by a dissimilation particle swarm optimization-based Elman neural network. Nonlinear Analysis: Real World Applications, 9 (2008) 1345--1360.

[39]

X. Tong, Z. Wang, H. Yu. 2009. A research using hybrid RBF/Elman neural networks for intrusion detection system secure model. Computer Physics Communications, 180 (2009) 1795--1801.

[40]

C.-M. Lee, C.-N. Ko. 2009. Time series prediction using RBF neural networks with a nonlinear time-varying evolution PSO algorithm. Neurocomputing, 73 (2009) 449--460.

Digital Library

Cited By

Lu YWei D(2022)Chaos Prediction of Power Systems by Using Deep LearningProceedings of the 2022 14th International Conference on Machine Learning and Computing10.1145/3529836.3529843(11-17)Online publication date: 18-Feb-2022
https://dl.acm.org/doi/10.1145/3529836.3529843
Wang XWu ZGe JZhang ZHan LWang SZhang X(2022)Grid Load Forecasting Based on Dual Attention BiGRU and DILATE Loss FunctionIEEE Access10.1109/ACCESS.2022.318233410(64569-64579)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3182334
Wang ZZhou XTian JHuang T(2021)Hierarchical parameter optimization based support vector regression for power load forecastingSustainable Cities and Society10.1016/j.scs.2021.10293771(102937)Online publication date: Aug-2021
https://doi.org/10.1016/j.scs.2021.102937

Index Terms

A Hybrid Model of Least Squares Support Vector Regression Optimized by Particle Swarm Optimization for Electricity Demand Prediction
1. Computing methodologies
  1. Machine learning

Recommendations

Particle Swarm Optimization-Least Squares Support Vector Regression with Multi-scale Wavelet Kernel
ISCID '13: Proceedings of the 2013 Sixth International Symposium on Computational Intelligence and Design - Volume 01

A novel regression model combining least squares support vector regression (LS-SVR) with multi-scale wavelet kernel and particle swarm optimization (PSO) was presented in this paper, and applied to the approximation of non-stationary dataset and those ...
Car Sales Volume Prediction Based on Particle Swarm Optimization Algorithm and Support Vector Regression
ICICTA '11: Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation - Volume 01

In this paper, the car sales prediction model is established by using Support Vector Regression(SVR) combined with Particles Swarm Optimization algorithm (PSO-SVR). In this model, PSO Algorithm is used to optimize the 3 parameter used in Support Vector ...
Support Vector Regression Based on Particle Swarm Optimization for Rainfall Forecasting
CSO '10: Proceedings of the 2010 Third International Joint Conference on Computational Science and Optimization - Volume 02

This study applies a novel neural network technique, support vector regression (SVR), to rainfall forecasting. To build an effective SVR model, SVR's parameters must be set carefully. This study proposes a novel approach, known as particle swarm ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICMLC '19: Proceedings of the 2019 11th International Conference on Machine Learning and Computing

February 2019

563 pages

ISBN:9781450366007

DOI:10.1145/3318299

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Southwest Jiaotong University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 22 February 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

ICMLC '19

ICMLC '19: 2019 11th International Conference on Machine Learning and Computing

February 22 - 24, 2019

Zhuhai, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
73
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Lu YWei D(2022)Chaos Prediction of Power Systems by Using Deep LearningProceedings of the 2022 14th International Conference on Machine Learning and Computing10.1145/3529836.3529843(11-17)Online publication date: 18-Feb-2022
https://dl.acm.org/doi/10.1145/3529836.3529843
Wang XWu ZGe JZhang ZHan LWang SZhang X(2022)Grid Load Forecasting Based on Dual Attention BiGRU and DILATE Loss FunctionIEEE Access10.1109/ACCESS.2022.318233410(64569-64579)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3182334
Wang ZZhou XTian JHuang T(2021)Hierarchical parameter optimization based support vector regression for power load forecastingSustainable Cities and Society10.1016/j.scs.2021.10293771(102937)Online publication date: Aug-2021
https://doi.org/10.1016/j.scs.2021.102937

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents