Abstract
In this paper, a short-term load forecasting model based on PCA dimensionality reduction technology and grey theory is proposed. After the correlation analysis between meteorological factors and load indicators, the data is carried out by combining PCA dimensionality reduction technology and grey theoretical load forecasting model. In this paper, the validity of the load data verification model in a western region is selected. The analysis of the example shows that compared with the general gray prediction model GM (1, 1), the accuracy of the model prediction result is much higher, which proves the model. Effectiveness and practicality.
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The project was supported by the Open Research Fund of Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing Institute of Technology (No. XTCX201807).
2019 Jiangsu Province Graduate Practice Innovation Plan (SJCX19_0519).
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Bian, HH., Wang, Q., Tian, L. (2020). Research on Short-Term Load Forecasting Based on PCA-GM. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_15
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DOI: https://doi.org/10.1007/978-3-030-51103-6_15
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