Abstract
In recent years, DBN applied to load forecasting as a hot issue has aroused the concern of many scholars at home and abroad. A new method based on RBM and NARX neural network for short-term load forecasting is brought forward in this paper. In order to test the performance of this model, the historical load data of a town in the UK is used. The obtained results are compared with DBN and NARX neural network based on the same dataset. Experimental results show that the proposed method significantly improves the predication accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, Y., Luh, P.B., Rourke, S.J.: Short-term load forecasting: similar day-based wavelet neural networks. World Congr. Intell. Control Autom. 25(1), 3353–3358 (2008)
Chen, C., Zhou, J.N.: Application of regression analysis in power system load forecasting. Adv. Mater. Res. 960–961, 1516–1522 (2014)
Shankar, R., Chatterjee, K., Chatterjee, T.K.: A very short-term load forecasting using kalman filter for load frequency control with economic load dispatch. J. Eng. Sci. Technol. Rev. 5(1), 97–103 (2012)
Wei, L., Zhen-gang, Z.: Based on time sequence of ARIMA model in the application of short-term electricity load forecasting. In: International Conference on Research Challenges in Computer Science, pp. 11–14 (2009)
Vähäkyla, P., Hakonen, E., Léman, P.: Short-term forecasting of grid load using Box-Jenkins techniques. Int. J. Electr. Power Energy Syst. 2(1), 29–34 (1980)
Li, X., Chen, H., Gao, S.: Electric power system load forecast model based on State Space time-varying parameter theory. In: IEEE International Conference on Power System Technology, pp. 1–4 (2010)
Christiaanse, W.R.: Short-term load forecasting using general exponential smoothing. IEEE Trans. Power Appar. Syst. PAS-90, 900–911(2007)
Koo, B.G., Kim, M.S., Kim, K.H.: Short-term electric load forecasting using data mining technique. In: International Conference on Intelligent Systems and Control, pp. 153–157. IEEE (2013)
Lee, K.Y., Cha, Y.T., Park, J.H.: Short-term load forecasting using artificial neural networks. IEEE Trans. Power Syst. 7(1), 124–132 (2014)
Li, G., Cheng, C.T., Lin, J.Y.: Short-term load forecasting using support vector machine with SCE-UA algorithm. In: International Conference on Natural Computation, pp. 290–294. IEEE (2007)
Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527 (2006)
Sun, S., Liu, F., Liu, J.: Web classification using deep belief networks. In: IEEE International Conference on Computational Science and Engineering, pp. 768–773 (2014)
Arsa, D.M.S., Jati, G., Mantau, A.J.: Dimensionality reduction using deep belief network in big data case study: hyperspectral image classification. In: International Workshop on Big Data and Information Security, pp. 71–76. IEEE (2017)
Cheng, M.: The cross-field DBN for image recognition. In: IEEE International Conference on Progress in Informatics and Computing, pp. 83–86. IEEE (2016)
Zhang, X., Wang, R., Zhang, T.: Short-term load forecasting based on an improved deep belief network. In: International Conference on Smart Grid and Clean Energy Technologies, pp. 339–342 (2016)
Zhang, X., Wang, R., Zhang, T., Liu, Y., Zha, Y.: Effect of transfer functions in deep belief network for short-term load forecasting. In: He, C., Mo, H., Pan, L., Zhao, Y. (eds.) BIC-TA 2017. CCIS, vol. 791, pp. 511–522. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7179-9_40
Takens F.: Detecting strange attractors in turbulence. In: Dynamical Systems and Turbulence, Warwick 1980, pp. 366–381. Springer, Heidelberg (1981)
Hegger, R., Kantz, H., Schreiber, T.: Practical implementation of nonlinear time series methods: the tisean package. Chaos 9(2), 413–435 (1999)
Nonlinear Time Series Analysis (TISEAN). https://www.mpipks-dresden.mpg.de/~tisean/
Wang, R., Purshouse, R.C., Fleming, P.J.: Preference-inspired co-evolutionary algorithms for many-objective optimisation. IEEE Trans. Evol. Comput. 17, 474–494 (2013)
Wang, R., Ishibuchi, H., Zhou, Z., Liao, T., Zhang, T.: Localized weighted sum method for many-objective optimization. IEEE Trans. Evol. Comput. 22, 3–18 (2018)
Wang, R., Zhang, Q., Zhang, T.: Decomposition based algorithms using Pareto adaptive scalarizing methods. IEEE Trans. Evol. Comput. 20, 821–837 (2016)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (Nos. 61773390, 71571187) and the Distinguished Natural Science Foundation of Hunan Province (No. 2017JJ1001).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, X., Wang, R., Zhang, T., Wang, L., Liu, Y., Zha, Y. (2018). Short-Term Load Forecasting Based on RBM and NARX Neural Network. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-95957-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95956-6
Online ISBN: 978-3-319-95957-3
eBook Packages: Computer ScienceComputer Science (R0)