基于气象因子的EEMD-BP方法在电网用电量预测中的应用

张震; 肖莺; 任永建; 陈正洪; 张震; 肖莺; 任永建; 陈正洪

doi:10.16516/j.ceec.2024.1.13

[1]

程浩忠. 电力系统规划 [M]. 北京: 中国电力出版社, 2008.

CHENG H Z. Power system planning [M]. Beijing: China Electric Power Press, 2008.

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陈本阳. 大数据技术在短期负荷预测的应用 [J]. 自动化技术与应用, 2018, 37(6): 21-25. DOI: 10.3969/j.issn.1003-7241.2018.06.005.

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LI K, ZHAO B B, CAO Z F, et al. Short-term electricity consumption prediction management model based on regression analysis and dummy variables [J]. Electrotechnical application, 2017, 36(2): 59-65.

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WAN K, LIU R Y. Application of interval time-series vector autoregressive model in short-term load forecasting [J]. Power system technology, 2012, 36(11): 77-81. DOI: 10.13335/j.1000-3673.pst.2012.11.044.

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XU Y H, WANG C Y. Analysis of the influencing factors of load characteristics of Shaanxi power grid based on gray correlation degree [J]. Electrotechnical application, 2017, 36(14): 16-20.

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SHEN H T, LI F, SHI L, et al. Short-term power load forecasting based on reduction of meteorological data dimensionality and hybrid deep learning [J]. Electric power construction, 2024, 45(1): 13-21. DOI: 10.12204/j.issn.1000-7229.2024.01.002.

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FENG Y, SONG Y B, JIN S, et al. Improved deep learning model for forecasting short-term load based on random forest algorithm and rough set theory [J]. Power generation technology, 2023, 44(6): 889-895. DOI: 10.12096/j.2096-4528.pgt.23013.

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WU R Z, BAO Z R, SONG X Y, et al. Research on short-term load forecasting method of power grid based on deep learning [J]. Modern electric power, 2018, 35(2): 43-48. DOI: 10.3969/j.issn.1007-2322.2018.02.007.

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ZHU X H, WANG Z Q, LI Y, et al. A hybrid short-term load forecasting method combined with LS-SVM and BP neural network based on EEMD [J]. Mathematics in practice and theory, 2012, 42(8): 151-158. DOI: 10.3969/j.issn.1000-0984.2012.08.023.

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[1]	程浩忠. 电力系统规划 [M]. 北京: 中国电力出版社, 2008. CHENG H Z. Power system planning [M]. Beijing: China Electric Power Press, 2008.
[2]	陈本阳. 大数据技术在短期负荷预测的应用 [J]. 自动化技术与应用, 2018, 37(6): 21-25. DOI: 10.3969/j.issn.1003-7241.2018.06.005. CHEN B Y. Application of big data technology in short term load forecasting [J]. Techniques of automation and applications, 2018, 37(6): 21-25. DOI: 10.3969/j.issn.1003-7241.2018.06.005.
[3]	李凯, 赵滨滨, 曹占峰, 等. 基于回归分析和虚拟变量的短期用电量预测管理模型 [J]. 电气应用, 2017, 36(2): 59-65. LI K, ZHAO B B, CAO Z F, et al. Short-term electricity consumption prediction management model based on regression analysis and dummy variables [J]. Electrotechnical application, 2017, 36(2): 59-65.
[4]	万昆, 柳瑞禹. 区间时间序列向量自回归模型在短期电力负荷预测中的应用 [J]. 电网技术, 2012, 36(11): 77-81. DOI: 10.13335/j.1000-3673.pst.2012.11.044. WAN K, LIU R Y. Application of interval time-series vector autoregressive model in short-term load forecasting [J]. Power system technology, 2012, 36(11): 77-81. DOI: 10.13335/j.1000-3673.pst.2012.11.044.
[5]	徐衍会, 王晨语. 基于灰色关联度的陕西电网负荷特性影响因素分析 [J]. 电气应用, 2017, 36(14): 16-20. XU Y H, WANG C Y. Analysis of the influencing factors of load characteristics of Shaanxi power grid based on gray correlation degree [J]. Electrotechnical application, 2017, 36(14): 16-20.
[6]	耿艳, 韩学山, 韩力. 基于最小二乘支持向量机的短期负荷预测 [J]. 电网技术, 2008, 32(18): 72-76. DOI: 10.13335/j.1000-3673.pst.2008.18.011. GENG Y, HAN X S, HAN L. Short-term load forecasting based on least squares support vector machines [J]. Power system technology, 2008, 32(18): 72-76. DOI: 10.13335/j.1000-3673.pst.2008.18.011.
[7]	陈超, 黄国勇, 邵宗凯, 等. 基于日特征量相似日的PSO-SVM短期负荷预测 [J]. 中国电力, 2013, 46(7): 91-94. DOI: 10.3969/j.issn.1004-9649.2013.07.019. CHEN C, HUANG G Y, SHAO Z K, et al. Short-term load forecasting for similar days based on PSO-SVM and daily feature vector [J]. Electric power, 2013, 46(7): 91-94. DOI: 10.3969/j.issn.1004-9649.2013.07.019.
[8]	申洪涛, 李飞, 史轮, 等. 基于气象数据降维与混合深度学习的短期电力负荷预测 [J]. 电力建设, 2024, 45(1): 13-21. DOI: 10.12204/j.issn.1000-7229.2024.01.002. SHEN H T, LI F, SHI L, et al. Short-term power load forecasting based on reduction of meteorological data dimensionality and hybrid deep learning [J]. Electric power construction, 2024, 45(1): 13-21. DOI: 10.12204/j.issn.1000-7229.2024.01.002.
[9]	封钰, 宋佑斌, 金晟, 等. 基于随机森林算法和粗糙集理论的改进型深度学习短期负荷预测模型 [J]. 发电技术, 2023, 44(6): 889-895. DOI: 10.12096/j.2096-4528.pgt.23013. FENG Y, SONG Y B, JIN S, et al. Improved deep learning model for forecasting short-term load based on random forest algorithm and rough set theory [J]. Power generation technology, 2023, 44(6): 889-895. DOI: 10.12096/j.2096-4528.pgt.23013.
[10]	吴润泽, 包正睿, 宋雪莹, 等. 基于深度学习的电网短期负荷预测方法研究 [J]. 现代电力, 2018, 35(2): 43-48. DOI: 10.3969/j.issn.1007-2322.2018.02.007. WU R Z, BAO Z R, SONG X Y, et al. Research on short-term load forecasting method of power grid based on deep learning [J]. Modern electric power, 2018, 35(2): 43-48. DOI: 10.3969/j.issn.1007-2322.2018.02.007.
[11]	朱祥和, 王子琦, 李严, 等. 基于EEMD的LS-SVM和BP神经网络混合短期负荷预测 [J]. 数学的实践与认识, 2012, 42(8): 151-158. DOI: 10.3969/j.issn.1000-0984.2012.08.023. ZHU X H, WANG Z Q, LI Y, et al. A hybrid short-term load forecasting method combined with LS-SVM and BP neural network based on EEMD [J]. Mathematics in practice and theory, 2012, 42(8): 151-158. DOI: 10.3969/j.issn.1000-0984.2012.08.023.
[12]	HUANG N E, SHEN Z, LONG S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [J]. Proceedings of the royal society A:mathematical, physical and engineering sciences, 1998, 454(1971): 903-995. DOI: 10.1098/rspa.1998.0193.
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