Abstract
Traditional data mining algorithms have problems such as poor applicability, high false positive rate or high false positive rate, resulting in low security and stability of the power system. For this reason, the regional photovoltaic power generation data mining algorithm is studied. Classification of data sources facilitates correlation calculations, and matrix relationships are used to calculate data associations. Combined with the data relevance, the association rules are output, and the output results inherit the clustering processing and time series distribution of the implicit data, thereby realizing the extraction of hidden data and completing the regional photovoltaic power generation data mining. The experimental results show that the regional PV power generation data mining algorithm has high stability and can effectively solve the system security problem.
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References
Wang, H., Li, F., Zhang, L., et al.: Research and application of big data mining technology in photovoltaic power prediction. Hebei Electr. Power Technol. 12(2), 123–143 (2018)
Ge, J.: Research on large data mining algorithm for periodic performance of warships under load environment. Ship Sci. Technol. 23(22), 121–123 (2017)
Xiaobo, Y.: Research on Data Mining Algorithms Based on Projection Pattern Support Set [J]. Computer Applications and Software 34(7), 273–276 (2017)
Lv, Y., Huang, L.: Research and implementation of data mining algorithms based on Hadoop framework in Hongbo. Large data environment. Electron. Design Eng. 25(7), 241–244 (2017)
Zheng, Z., Wu, W., Li, H.: Data mining algorithms based on CLUSTER optimization. J. Harbin Commercial Univ. (Nat. Sci. Ed.) 33(6), 329–330 (2017)
Shibing, B.: Research on data mining algorithms based on neural network and particle swarm optimization. Laser J. 38(3), 188–192 (2017)
Cheng, Z., Li, S., Han, L., et al.: Research on the prediction method of photovoltaic array power generation based on data mining. J. Solar Energy 38(3), 726–733 (2017)
Yang, J., Lin, X., Yao, Q.: Study on wind power generation system based on hydraulic constant rense speed control. J. Xi’an Polytech. Univ. 2018(05), 574–580 (2018)
Yongwen, Y., Fanyue, Q., Zaiqin, T., et al.: Data mining and analysis method for operation log of photovoltaic power plant. Sci. Technol. Econ. Guide 34(2), 114–145 (2017)
Luo, R., Wang, Z., Wu, T., et al.: A pollution diagnosis method for photovoltaic modules based on large data mining of solar power regulation. CN107065829A [P] 33(2), 134–135 (2017)
Ding, M., Li, C., Li, H., et al.: Fault detection method for photovoltaic inverters based on massive data mining. Electr. Autom. 33(3), 154–165 (2018)
Gong, S., Pan, T., Wu, D., et al.: Research on the method of filling the missing photovoltaic data in microgrid based on MCMCMC. Renew. Energy 33(3), 453–465 (2018)
Wang, H., Li, F., Zhang, L., et al.: Research and application of big data mining technology in photovoltaic power prediction. Hebei Electr. Power Technol. 12(2), 345–367 (2018)
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Lei, Z., Yang, Yb. (2019). Research on Data Mining Algorithm for Regional Photovoltaic Generation. In: Gui, G., Yun, L. (eds) Advanced Hybrid Information Processing. ADHIP 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 301. Springer, Cham. https://doi.org/10.1007/978-3-030-36402-1_46
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DOI: https://doi.org/10.1007/978-3-030-36402-1_46
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