Abstract
Short-term load forecasting can reasonably allocate power resources and keep modern power systems in a stable and reliable working state. Due to the increasing requirements of modern power systems in recent years and the close integration of computer technology and smart grids, artificial intelligence has been widely used in power load forecasting. The least-square support vector machine (LSSVM) model is used in It has good forecasting effect in short-term load forecasting. This article introduces several artificial intelligence algorithms that can be used to optimize model parameters and summarize methods to make model prediction results more accurate.
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References
Yin, Z.: Advantages and disadvantages of LSSVM and PSO-LSSVM in short-term power load forecasting. J. Qinghai Electr. Power 38(2), 53–57 (2019)
Chen, Y., Mao, Y., Chen, P.: Based on EEMD-sample entropy and Elman neural network short-term power load forecasting. J. Power Syst. Autom. 28(3), 59–64 (2019). 2016
Liu, Y., Liu, H., Chen, X., Li, H., Zhou, Q.: Electricity load forecasting based on DE-GWO-LSSVM model. J. Hubei Univ. Techonol. 34(4), 30–34 (2019)
Chen, Y., Li, P., Zhang, Z., Nie, H., Shen X.: Online prediction model of transmission line icing load based on PCA_GA_LSSVM. J. Power Syst. Protect. Control 47(10), 110–119 (2019)
Huang, Y., Zhang, L.: Research on short-term electricity price forecasting based on genetic algorithm for weight optimization of BP-LSSVM combined variable weight model. J. Coal. Eng. 51(5), 172–176 (2019)
Zhou, S., Li, J., Mao, Q., Song, M.: GOA-LSSVM short-term load forecasting based on data dimensionality reduction and feature analysis. J. Res. Explor. Lab. 38(11), 38–42 (2019)
Lei, S., Sun, C., Wang, S.: Multivariable time series of short-term power load research on linear regression forecasting method. J. China Electr. Eng. 26(2), 25–29 (2006)
Zhou, H., Wang, L., Pu, F.: Short-term power load based on PSO-WPESN load forecasting method. J. Electr. Meas. Instrument. 54(6), 113–119 (2017)
Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. J. Neural Process. Lett. 9(3), 293–300 (1999)
Guan, T., Xu, Z., Lin, L., Zhang, G., Jia, Y., Shi, Y.: Maximum incremental load recursive model based on LS-SVM considering accumulated temperature effect. In: 2018 IEEE Conference on Internet of Things, Green Computing and Communications, Cyber, Physical and Social Computing, Smart Data, Blockchain, Computer and Information Technology, Congress on Cybermatics, pp. 716–719 (2018)
XuhuiZhu, Z.: Application of GWO-LSSVM coupling model in deformation prediction. J. Beijing Surv. Mapping 33(07), 835–838 (2019)
Liang, T., Sun, T., Zhou, J., Hou, Z.: Weighted LSSVM short-term wind speed prediction based on GA optimization. J. High-tech Commun. 29(02), 142–148 (2019)
Chang, K.C., Chu, K.C., Wang, H.C., Lin, Y.C., Pan, J.S.: Energy saving technology of 5G base station based on Internet of Things collaborative control. IEEE Access 8, 32935–32946 (2020)
Chang, K.-C., Chu, K.-C., Wang, H.-C., Lin, Y.-C., Pan, J.-S.: Agent-based middleware framework using distributed CPS for improving resource utilization in smart city. Fut. Gener. Comput. Syst. 108, 445–453 (2020)
Chu, K.C., Horng, D.C., Chang, K.C.: Numerical optimization of the energy consumption for wireless sensor networks based on an improved ant colony algorithm. IEEE Access, 7, 105562–105571 (2019)
Chu, K.C., Chang, K.C., Wang, H.C., Lin, Y.C., Hsu, T.L.: Field-programmable gate array-based hardware design of optical fiber transducer integrated platform. J. Nanoelectron. Optoelectron. 15(5), 663–671 (2020)
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Lin, YC. et al. (2021). Algorithm Optimization of Short-Term Load Forecasting Model Based on Least Square Support Vector Machine. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_26
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DOI: https://doi.org/10.1007/978-3-030-76346-6_26
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