考虑春节影响的售电量预测结果修正方法

doi:10.11959/j.issn.1000-0801.2019041

Abstract

Abstract:

The effects of Spring Festival on electricity sales of different trades were considered and an adjustment method for electricity sales forecasting result was proposed.Firstly,the month-quarter ratio of historical electricity sales in each trade of every month in the first quarter and the distance between Spring Festival and the first day of every month in the first quarter was used to create a functional relationship.Then,the distance between Spring Festival and the first day of every month in the first quarter of the target year was used as input and the predicted month-quarter ratio of electricity sales of each trade of every month in the first quarter of the target year was calculated according to the functional relationship.The adjusted electricity sales forecasting result can then be computed by using the predicted month-quarter ratio and the electricity sales forecasting of each trade of every month in the first quarter of the target year before adjustment.The adjustment result shows that the proposed method can effectively lower the error of electricity sales forecasting in the first quarter.

Key words: electricity sales, Spring Festival, regression analysis, least square

CLC Number:

TM715

Jian LIU,Jiakui ZHAO,Yuze CHEN,Xuemin FANG,Yuxi LIU,Shulong WANG,Yan CHEN,Hong OUYANG. An adjustment method for electricity sales forecasting result considering the effects of spring festival[J]. Telecommunications Science, 2019, 35(3): 127-134.

Figures/Tables 8

References 15

[1]	崔和瑞, 王娣 . 基于季节 ARIMA 模型的华北电网售电量预测研究[J]. 华东电力, 2009,37(1): 70-72.
	CUI H R , WANG D . Study of electricity sales forecasting for North China Power Grid based on seasonal ARIMA model[J]. East China Electric Power, 2009,37(1): 70-72.
[2]	吴杰, 宋国堂, 卢志刚 ,等. 基于偏最小二乘回归与比重法的月售电量预测[J]. 电力系统及其自动化学报, 2008,20(3): 66-69.
	WU J , SONG G T , LU Z G ,et al. Forecasting for monthly elec-tricity consumption using partial least-square regressive and proportion model[J]. Proceedings of the CSU-EPSA, 2008,20(3): 66-69.
[3]	樊娇, 冯昊, 牛东晓 ,等. 基于小波分析和GM-ARIMA模型的月度售电量预测[J]. 华北电力大学学报(自然科学版), 2015,42(4): 101-105.
	FAN J , FENG H , NIU D X ,et al. Monthly electricity sales forecast based on wavelet analysis and GM-ARIMA model[J]. Journal of North China Electric Power University, 2015,42(4): 101-105.
[4]	颜伟, 程超, 薛斌 ,等. 结合X12乘法模型和ARIMA模型的月售电量预测方法[J]. 电力系统及其自动化学报, 2016,28(5): 74-80.
	YAN W , CHENG C , XUE B ,et al. Forecasting for monthly electricity consumption using X12 multiplication method and ARIMA model[J]. Proceedings of the CSU-EPSA, 2016,28(5): 74-80.
[5]	TAYLOR J W . Triple seasonal methods for short-term electricity demand forecasting[J]. European Journal of Operational Research, 2010,204(1): 139-152.
[6]	OHTSUKA Y , OGA T , KAKAMU K . Forecasting electricity demand in Japan:a Bayesian spatial autoregressive ARMA approach[J]. Computational Statistics ＆ Data Analysis, 2010,54(11): 2721-2735.
[7]	RINGWOOD J V , BOFELLI D , MURRAY F T . Forecasting electricity demand on short,medium and long time scales using neural networks[J]. Journal of Intelligent and Robotic Systems, 2001,31(1-3): 129-147.
[8]	HIRANO O , KANKE M , OZAWA K . Electricity demand and price analysis in California using possibility regression model[J]. Journal of Advanced Computational Intelligence and Intelligent Informatics, 2003,7(2): 147-152.
[9]	MOTAMEDI A , ZAREIPOUR H , ROSEHART W D . Electricity price and demand forecasting in smart grids[J]. IEEE Transactions on Smart Grid, 2012,3(2): 664-674.
[10]	LIU S , TIAN L . The study of long-term electricity load forecasting based on improved grey prediction model[C]// 2013 International Conference on Machine Learning and Cybernetics,July 14-17,2013,Tianjin,China. Piscataway:IEEE Press, 2013: 653-656.
[11]	KALASHNIKOV V , KALASHNYKOVA N , PEREZ F J C ,et al. Liberalization of Mexican electricity market:various modeling approaches with numerical simulation results[C]// 2008 International Conference on Intelligent Systems Design and Applications,June 24-28,2008,Kaohsiung,China. Piscataway:IEEE Press, 2008: 72-76.
[12]	ZHAO J , TANG W S , FANG X M . A novel electricity sales forecasting method based on clustering regression and time series analysis[C]// 2015 International Conference on Artificial Intelligence and Software Engineering,January 10-12,2015,Lisbon,Portugal. New York:ACM Press, 2015.
[13]	ZHAO J , TANG W S , FANG X M ,et al. A novel accuracy adjustment approach for forecasted quantity of electricity sales based on abnormal influencing factors assessment[C]// 2015 8th International Symposium on Computational Intelligence and Design (ISCID),Dec 21-22,2015,Hangzhou,China. Piscataway:IEEE Press, 2015: 516-520.
[14]	孙婧捷 . 基于组合方法的月度售电量预测研究[D]. 大连：大连理工大学, 2014.
	SUN J J . Research on monthly electricity sales forecasting based on combination method[D]. Dalian:Dalian University of Technology, 2014.
[15]	潘小辉, 刘丽萍, 李扬 . 提高月度售电量预测精度的一种新方法[J]. 电力需求侧管理, 2013(3): 11-15.
	PAN X H , LIU L P , LI Y . A new method to promote the fore-casting accuracy of monthly electricity consumption[J]. Power Demand Side Management, 2013(3): 11-15.

Metrics

Recommended 0

No Suggested Reading articles found!

年份春节日期	距1月首日天数	距2月首日天数	距3月首日天数
2010年 2月14日	-44	-13	15
2011年 2月3日	-33	-2	26
2012年 1月23日	-22	9	38
2013年 2月10 日	-40	-9	19
2014年 1月31日	-30	1	29
2015年 2月19日	-49	-18	10

年份	月份	占季比
2010年	1月	37.22%
	2月	32.32%
	3月	30.46%
2011年	1月	36.56%
	2月	31.48%
	3月	31.96%
2012年	1月	31.62%
	2月	33.52%
	3月	34.86%
2013年	1月	37.80%
	2月	30.86%
	3月	31.34%
2014年	1月	36.65%
	2月	30.82%
	3月	32.53%

An adjustment method for electricity sales forecasting result considering the effects of spring festival

RichHTML

PDF下载

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 8

References 15

Related Articles 11

Metrics

Recommended 0

[1]	Haiyan CAO, Zhiyao MA, Yingjuan ZHI, Renqing LIU, Fangmin XU, Xin FANG. Design of full-connected hybrid precoding based on an equivalent channel in millimeter-wave massive MIMO system [J]. Telecommunications Science, 2021, 37(1): 85-93.
[2]	Pengfeng SONG,Qingwei YE,Zhihua LU,Yu ZHOU. AdaBoost algorithm based on fitted weak classifier [J]. Telecommunications Science, 2019, 35(11): 27-35.
[3]	Yuze CHEN,Lei LI,Xuemin FANG,Jian LIU,Jiakui ZHAO,Yuxi LIU,Songlin ZUO. An analysis method for electricity consumption in peak load period of summer based on regression analysis [J]. Telecommunications Science, 2019, 35(11): 125-131.
[4]	Xu DU,Jingyu FENG,Shaoqing LV,Wei SHI. PM2.5 concentration prediction model based on random forest regression analysis [J]. Telecommunications Science, 2017, 33(7): 66-75.
[5]	Zhenhong HE. Iris recognition algorithm based on feature weighting fast discrete Curvelet transform and fuzzy LS-SVM [J]. Telecommunications Science, 2016, 32(3): 92-98.
[6]	Lu YANG,Ping HE,Shan WANG. Carrier frequency synchronization technology in UFMC [J]. Telecommunications Science, 2016, 32(11): 50-55.
[7]	Jihong Zhao,Pei Lei,Weihua Wang,Hua Qu,Dan He. RLS Beamforming Algorithm Based on Wavelet Transform [J]. Telecommunications Science, 2015, 31(2): 108-112.
[8]	Danhua Jia,Runrun Wang,Peng Wang. Research on Prediction Method of the Amount of Customers in Telecom Services Tariff Preview [J]. Telecommunications Science, 2011, 27(8): 25-32.
[9]	Xin Geng,Hanying Hu,Yanan Dun. An Algorithm of Channel Estimation Based on Space-Time Correlation in MIMO OFDM System [J]. Telecommunications Science, 2011, 27(11): 84-89.
[10]	Qiang Wang,Daming Wang,Xinran Zhang,Chao Wang. Channel Estimation for EV-DO Forward Link Pilot Slots [J]. Telecommunications Science, 2010, 26(3): 41-45.
[11]	Jianxin Wang,Jianguo Yu. Research and Realization of Adaptive Digital Pre-distortion Algorithm for Wideband OFDM Power Amplifiers [J]. Telecommunications Science, 2009, 25(12): 51-56.