Abstract
In order to improve the accuracy and efficiency of mid-term power load forecasting, a mid-term power load forecasting method of long short time memory network (LSTM), which combines weather factors, is proposed. Firstly, the influence of meteorological factors affecting the power load on the mid-time power load is analyzed. Secondly, the meteorological factors are used as the input factor of the LSTM model to predict the power load. Finally, compared with the Random forest, ARIMA, GBDT, the LSTM algorithm which is not fused with meteorological factors, through the analysis of the experimental data, the LSTM fusion of meteorological factors has a better prediction effect on the mid-term load forecasting.
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Acknowledgments
This work was supported by the Project of Industry-guidance of Fujian Province under Grant No. 2015H0009, Fujian Young Teacher Education Research Foundation under Grant No. JA14217, National Natural Science Foundation of China (No. 61304199), Fujian Science and Technology Department (No. 2014H0008), Fujian Transportation Department (No. 2015Y0008), Fujian Education Department (No. JK2014033, JA14209, JA1532), and Fujian University of Technology (No. GYZ13125, 61304199, GY-Z160064). Many thanks to the anonymous reviewers, whose insightful comments made this a better paper.
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Su, X., Jiang, Xh., Zhang, Sm., Chen, Ml. (2019). LSTM Power Mid-Term Power Load Forecasting with Meteorological Factors. In: Zhao, Y., Wu, TY., Chang, TH., Pan, JS., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2018. Smart Innovation, Systems and Technologies, vol 128. Springer, Cham. https://doi.org/10.1007/978-3-030-04585-2_12
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DOI: https://doi.org/10.1007/978-3-030-04585-2_12
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