Abstract：Medium and long term load forecasting is an important prerequisite for the power sector's development planning and stable operation. According to the multiple factors of influencing the medium and long term power load forecasting accuracy, this paper uses the stepwise regression method to identify the key influencing factors from a number of factors associated load forecasting, and proposes a probability density forecasting method based on the Box-Cox transformation quantile regression combined with kernel density estimation. The probability density forecasting results of load under the different quantiles at any year in the next few years are evaluated. The proposed method is likely to realize the accurate range prediction of future annual electricity consumption. The historical load and socio-economic data of Anhui province are adopted as simulation experiment. The results show that the proposed method not only realizes the medium and long term load forecasting, but also well improves the precision of medium and long-term power load probability density forecasting by means of introducing strong relation factors, and effectively solves medium and long term power load probability density forecasting problem considering multiple factors.
何耀耀, 郑丫丫, 杨善林. 基于Box-Cox变换分位数回归与负荷关联因素辨识的中长期概率密度预测[J]. 系统工程理论与实践, 2018, 38(1): 197-207.
HE Yaoyao, ZHENG Yaya, YANG Shanlin. Medium and long term probability density forecasting based on Box-Cox transformation quantile regression and load relation factor identification. Systems Engineering - Theory & Practice, 2018, 38(1): 197-207.
[1] 康重庆, 夏清, 刘梅. 电力系统负荷预测[M].北京:中国电力出版社, 2007:241-250. Kang C Q, Xia Q, Liu M. Power system load forecasting[M].Beijing:China Electric Power Press, 2007:241-250. [2] 牛东晓, 曹树华, 卢建昌, 等. 电力负荷预测技术及其应用[M]. 北京:中国电力出版社, 2009:1-8. Niu D X, Cao S H, Lu J C, et al. Power load forecasting technology and its application[M]. Beijing:China Electric Power Press, 2009:1-8. [3] 周德强. 改进的灰色Verhulst模型在中长期负荷预测中的应用[J]. 电网技术, 2009, 18:124-127. Zhou D Q.Improved grey verhulst model in the medium and long-term load forecasting application[J]. Power Grid Technology, 2009, 18:124-127. [4] Amjady N, Keynia F.Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm[J].Energy, 2009(34):46-57. [5] 李瑾, 刘金朋, 王建军. 采用支持向量机和模拟退火算法的中长期负荷预测方法[J]. 中国电机工程学报, 2011, 31(16):63-66. Li J, Liu J P, Wang J J.Mid-long term load forecasting based on simulated annealing and SVM algorithm[J]. Proceedings of the CSEE, 2011, 31(16):63-66. [6] 龚承柱, 李兰兰, 杨娟, 等. 基于EMD-PSR-LSSVM的城市燃气管网短期负荷预测[J]. 系统工程理论与实践, 2014, 24(11):3001-3008. Gong C Z, Li L L, Yang J, et al.An integrated short-term load forecasting approach for urban gas pipeline network based on EMD, PSR and LSSVM[J]. Systems Engineering-Theory & Practice, 2014, 24(11):3001-3008. [7] 何耀耀, 许启发, 杨善林, 等. 基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J]. 中国电机工程学报, 2013, 33(1):93-98. He Y Y, Xu Q F, Yang S L, et al.A power load probability density forecasting method based on RBF neural network quantile regression[J]. Proceedings of the CSEE, 2013, 33(1):93-98. [8] 何耀耀, 闻才喜, 许启发, 等. 考虑温度因素的中期电力负荷概率密度预测方法[J]. 电网技术, 2015, 39(1):176-181. He Y Y, Wen C X, Xu Q F, et al.A method to predict probability density of medium-term power load considering temperature factor[J]. Power System Technology, 2015, 39(1):176-181. [9] 赵腾, 王林童, 张焰, 等. 采用互信息与随机森林算法的用户用电关联因素辨识及用电量预测方法[J]. 中国电机工程学报, 2016(3):604-614. Zhao T, Wang L T, Zhang Y, et al.Relation factor identification of electricity consumption behavior of users and electricity demand forecasting based on mutual information and random forests[J].Proceedings of the CSEE, 2016(3):604-614. [10] 许启发, 张金秀, 蒋翠侠. 基于支持向量分位数回归多期VaR测度[J]. 系统工程学报, 2014, 29(2):202-214. Xu Q F, Zhang J X, Jiang C X. Evaluating multiperiod VaR via support vector quantile regression[J]. Journal of Systems Engineering, 2014, 29(2):202-214. [11] 何耀耀, 刘瑞, 撖奥洋. 基于实时电价与支持向量机分位数回归的短期电力负荷概率密度预测方法[J]. 中国电机工程学报, 2017, 37(3):768-775. He Y Y, Liu R, Hao A Y.Short-term power load probability density forecasting method based on real time price and support vector quantile regression[J]. Proceedings of the CSEE, 2017, 37(3):768-775. [12] 郑晓雨, 贺仁睦, 马进. 逐步多元回归法在负荷模型扩展中的应用[J]. 中国电机工程学报, 2011(4):72-77. Zheng X Y, He R M, Ma J.Stepwise multiple regression method in the application of load model extension[J].Proceedings of the CSEE, 2011(4):72-77. [13] 陈全润, 杨翠红. "类逐步回归"变量筛选法及其在农村居民收入预测中的应用[J]. 系统工程理论与实践, 2008, 28(11):16-22. Chen Q R, Yang C H. "Stepwise regression" variable selection method and its application in rural residents income prediction[J]. Systems Engineering-Theory & Practice, 2008, 28(11):16-22. [14] 谭常春, 张诚, 周嘉章. 基于Box-Cox变换的城市火灾起数的模型研究[J]. 合肥工业大学学报(自然科学版), 2013(1):119-123. Tan C C, Zhang C, Zhou J Z. Research on the model of urban fire occurrence based on Box-Cox transformation[J].Journal of Hefei University of Technology (Natural Science Edition), 2013(1):119-123. [15] 王欢, 袁永生.Box-Cox变换用于分位数回归曲线相交问题的研究[J].数理统计与管理, 2009(6):1024-1029. Wang H, Yuan Y S.Apply Box-Cox transformation to the research on crossing problems of quantile regression[J].Journal of Mathematical Statistics and Management, 2009(6):1024-1029. [16] Koenker R W, Bassett Jr G.Regression quantiles[J].Econometrica, 1978, 46(1):33-50. [17] Taylor J W.A quantile regression neural network approach to estimating the conditional density of multiperiod returns[J]. Journal of Forecasting, 2000, 19:299-311. [18] Cannon A J.Quantile regression neural networks:Implementation in R and application to precipitation downscaling[J]. Computers & Geosciences, 2010, 37(9):1277-1284. [19] Cole T J.Fitting smoothed centile curves to reference centile data[J].Journal of the Royal Statistical Society Series A-General, 1988:385-418. [20] Cole T J, Green P J. Smoothing reference centile curves:The LMS method and penalized likelihood[J]. Statistics in Medicine, 1992, 11(10):1305-1319. [21] Yee T W.Quantile regression via vector generalized additive models[J]. Statistics in Medicine, 2004, 23:2295-2315.
2018, Vol. 38 
