Abstract
Operation cost is an important link in the operation of power enterprises. In the process of intelligent prediction of power transformation operation cost, there is a problem of low accuracy. Therefore, an intelligent prediction method of power transformation operation cost based on multi-dimensional mixed information is designed. Evaluate the fixed cost of power grid, determine the budget amount in different budget periods, extract the life cycle of power grid substation equipment, establish the cost estimation relationship, use multi-dimensional mixed information to build the cost control model, refine the project category, and optimize the intelligent prediction mode of operation cost according to the different nature of each link cost. Test results: the average prediction accuracy of the intelligent prediction method of power grid substation operation cost in this paper and the other two intelligent prediction methods of power grid substation operation cost are 79.357%, 71.066% and 69.313% respectively, indicating that after using multi-dimensional mixed information, the application effect of the designed intelligent prediction method of power grid substation operation cost is more prominent.
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References
Wang, Y.l., Wang, S., Zheng, Y., et al.: Calculation and allocation of operation and maintenance cost of power grid project based on elastic net 20 165–172 (2020)
Wang, M.J., Liu, Y.B., Gao, H.J., et al.: A two-stage stochastic model predictive control strategy for active distribution network considering operation cost risk. Adv. Power Syst. Hydroelectr. Eng. 11, 8–18 (2020)
Zhang, Z.X., Wen, C.B., Cai, P.C.: Distributed droop control of islanded microgrid based on incremental cost consistency 4, 517–523 (2020)
Li, T.¸Xu, Y., Chen, J., et al.: Optimal configuration of energy storage for microgrid considering life cycle cost-benefit 3, 46–51, 58 (2020)
Zhang, Y., Chen, Q.X., Xia, Q., et al.: Active distribution network cost allocation method based on distribution factor method. Electr. Power 4, 13–21 (2020)
Wang, J.F., Kong, L.S., Fan, X.M., et al.: Optimal planning for soft open point integrated with ESS to improve the economy of active distribution network. Electr. Power Constr. 10, 63–70 (2020)
Fang, Y., Chen, J., Tian, X.Z.: Capacity economical optimization of non-grid-connected wind/hydrogen hybrid micro power grid. Comput. Simul. 2, 110–114 (2020)
Wang, S., Liu, X.Y., Liu, S., et al.: Human short-long term cognitive memory mechanism for visual monitoring in IoT-assisted smart cities. IEEE Internet of Things J. 9, 7128–7139 (2021)
Liu, S., He, T.H., Dai, J.H.: A survey of CRF algorithm based knowledge extraction of elementary mathematics in Chinese. Mob. Netw. Appl. 26, 1891–1903 (2021)
Liu, S., Wang, S., Liu, X.Y., et al.: Fuzzy detection aided real-time and robust visual tracking under complex environments. IEEE Trans. Fuzzy Syst. 1, 90–102 (2021)
Qian, J., Wang, P.P., Cheng, G., et al.: Joint application of multi-object beetle antennae search algorithm and BAS-BP fuel cost forecast network on optimal active power dispatch problems. Knowl.-Based Syst. 226, 107149.1–107149.21 (2021)
Li, W.J., Liu, Y.G., Liang, H.J., et al.: A new distributed energy management strategy for smart grid with stochastic wind power. IEEE Trans. Ind. Electron. 2, 1311–1321 (2021)
Huang, H., Jia, R., Shi, X.Y., et al.: Feature selection and hyper parameters optimization for short-term wind power forecast. Appl. Intell.: Int. J. Artif. Intell. Neural Netw. Complex Probl.-Solving Technol. 10, 6752–6770 (2021)
Jakoplic, A., Frankovic, D., Kirincic, V., et al.: Benefits of short-term photovoltaic power production forecasting to the power system. Optim. Eng. 1, 9–27 (2021)
Zhang, Y., Li, Y.T., Zhang, G.Y.: Short-term wind power forecasting approach based on Seq2Seq model using NWP data. Energy 213, 118371.1–118371.14 (2020)
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© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Wang, Y., Zhu, X., Ke, Y., Yu, J., Li, Y. (2023). Research on Intelligent Prediction of Power Transformation Operation Cost Based on Multi-dimensional Mixed Information. In: Fu, W., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 469. Springer, Cham. https://doi.org/10.1007/978-3-031-28867-8_5
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DOI: https://doi.org/10.1007/978-3-031-28867-8_5
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