Structural Economic Model Based on Electricity  Consumption and Extreme Learning Machine

doi:10.3969/j.issn.1000-7229.2018.05.014

Abstract

Abstract: ABSTRACT： Considering the significant correlation between industrial development and electricity consumption, this paper adopts the well-known extreme learning machine （ELM） to investigate the relationship between electricity consumption and economic development. The paper also examines how to forecast industrial economic growth based on electricity consumption data. Case study results show that: （1） ELM demonstrates higher accuracies in most cases than the linear regression method；（2） statistically, almost all sectors in the secondary industry are highly dependent on the electricity consumption, and their growth rates are nonlinearly and positively correlated with their electricity consumption levels.

Key words: , KEYWORDS： electricity consumption, secondary industry, extreme learning machine （ELM）, economic growth, forecasting ,

CLC Number:

QIU Huadong, CHEN Yaojun, LIU Wenxuan, ZHAO Junhua, YAN Yong, WEN Fushuan. Structural Economic Model Based on Electricity Consumption and Extreme Learning Machine [J]. Electric Power Construction, 2018, 39(5): 115-.

References

［1］STUDENMUND A H, HENRY J C. Using econometrics: A practical guide［M］. Addison-Wesley Educational Publishers, 1992: 2, 163, 215.

［2］HUANG G, ZHU Q, CHEE K S. Extreme learning machine: a new learning scheme of feedforward neural networks ［C］//Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference, 2004, 2: 985-990.

［3］HUANG G, LEI C, CHEE K S. Universal approximation using incremental constructive feedforward networks with random hidden nodes［J］. IEEE Trans. Neural Networks, 2006, 17（4）: 879-892.

［4］HUANG G, LEI C. Convex incremental extreme learning machine ［J］. Neurocomputing, 2007, 70（16-18）: 3056-3062.

［5］HUANG G, LEI C. Enhanced random search based incremental extreme learning machine［J］. Neurocomputing, 2008, 71（16-18）: 3460-3468.

［6］FENG G,HUANG G, LIN Q, et al. Error minimized extreme learning machine with growth of hidden nodes and incremental learning［J］. IEEE Transactions on Neural Networks, 2009, 20（8）: 1352-1357.

［7］NIZAR A H, DONG Z, WANG Y. Power utility nontechnical loss analysis withextreme learning machine method［J］. IEEE Transactions on Power Systems, 2008, 23（3）: 946-955.

［8］XU Y,DONG Z, MENG K, et al. Real-time transient stability assessment model using extreme learning machine［J］. IET generation, transmission & distribution, 2011, 5（3）: 314-322.

［9］LI X, LIANG Y. Comparative study on trend decomposition of electricity demand and GDP［J］. Modern Electric Power, 2005, 22（5）: 84-86.

［10］CAI H, HAN Z, MA W. Multiple regression analysis on electric power consumption in China［J］. Statistics and Decision, 2008, 1（14）: 101-103.

［11］HAN Z, WU H. Electric consumption forecasting of Jingjintang grid based on multiple linear regression［J］. North China Electric Power, 2011, 1（4）: 22-24.

［12］LIU C, GAO T. Characteristics of electricity industry cycle fluctuation and influential factors of electricity demand based on business analysis and the error correction model［J］. Resources Science, 2011, 33（1）: 169-177.

［13］HUANG G, ZHU Q, CHEE K S. Extreme learning machine: theory and applications［J］. Neurocomputing, 2006, 70（1-3）: 489-501.

［14］SUN Z,CHOI T, AU K, et al. Sales forecasting using extreme learning machine with applications in fashion retailing［J］. Decision Support Systems, 2008, 46（1）: 411-419.

［15］VAN H M,MICHE Y, LINDH-KNUUTILA T, et al. Adaptive ensemble models of extreme learning machines for time series prediction［C］//International Conference on Artificial Neural Networks. Berlin: Springer, 2009: 305-314.

［16］CHEN X,DONG Z, MENG K, et al. Electricity price forecasting with extreme learning machine and bootstrapping ［J］. IEEE Transactions on Power Systems, 2012, 27（4）: 2055-2062.

［17］XU Y,DAI Y, DONG Z, et al. Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems［J］. Neural Computing and Applications, 2013, 22（3-4）: 501-508.

［18］LI X,XIE H, WANG R, et al. Empirical analysis: stock market prediction via extreme learning machine［J］. Neural Computing and Applications, 2016, 27（1）: 67-78.

［19］BOX, GEORGE E P, DAVID A P. Distribution of residual autocorrelations in autoregressive-integrated moving average time series models［J］. Journal of the American Statistical Association, 1970, 65（332）: 1509-1526.

［20］ENGLE R F. Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation ［J］. Econometrica: Journal of the Econometric Society, 1982, 50（4）: 987-1007.

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Structural Economic Model Based on Electricity Consumption and Extreme Learning Machine

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