基于时间序列和BP神经网络的电能替代潜力分析方法

doi:10.3969/j.issn.2097-0706.2022.03.006

综合智慧能源 ›› 2022, Vol. 44 ›› Issue (3): 38-43.doi: 10.3969/j.issn.2097-0706.2022.03.006

基于时间序列和BP神经网络的电能替代潜力分析方法

廖敏乐(), 黄重阳(), 戴承承(), 李化林(), 樊高松()

广西电网有限责任公司崇左供电局,广西崇左 532200

收稿日期:2021-09-17 修回日期:2021-10-12 出版日期:2022-03-25 发布日期:2022-03-28
作者简介:廖敏乐（1986）,男,工程师,从事电力电子及需求侧管理方面的研究, li_ml.czg@gx.csg.cn。
黄重阳（1980）,男,工程师,从事增量配电网及综合能源发展方面的研究, huang_cy.czg@gx.csg.cn。
戴承承（1986）,男,工程师,从事电网规划、并网管理及综合能源发展方面的研究, dai_cheng_cheng@126.com。
李化林（1977）,男,工程师,从事增量配电网需求侧管理、电网规划及综合能源规划方面的研究, li_hl.czg@gx.csg.cn。
樊高松（1985）,男,高级工程师,从事市场营销管理、增量配电网及综合能源发展方面的研究, fan_gs.czg@gx.csg.cn。
基金资助:
南方电网有限公司科技项目(GXKJXM20180201)

Analysis method of electric energy substitution potential based on time series and BP neural network

LIAO Minle(), HUANG Chongyang(), DAI Chengcheng(), LI Hualin(), FAN Gaosong()

Chongzuo Power Supply Bureau of Guangxi Power Grid Company Limited,Chongzuo 532200,China

Received:2021-09-17 Revised:2021-10-12 Online:2022-03-25 Published:2022-03-28

摘要/Abstract

摘要：

提出了基于时间序列和误差逆传播（Back Propagation,BP）神经网络的组合预测方法,利用基于三次指数平滑法的时间序列模型对电能替代量预测进行预测,对预测结果利用BP神经网络进行修正。利用国家统计局的能源使用情况作为数据进行预测和对比分析。算例结果表明,使用基于时间序列和BP神经网络的组合预测相比于单方法预测可以显著提高电能替代量的预测精度,可用于电能替代潜力分析。

关键词: 电能替代潜力, 时间序列, BP神经网络, 组合预测

Abstract:

A prediction method based on time series and BP neural network is proposed.The time series model based on cubic exponential smoothing method is used to predict the electric energy substitution,and the prediction results are corrected by BP neural network.The prediction and comparative analysis on energy consumption are made according to the data from National Bureau of Statistics.The results of a case study show that the combination forecast method based on time series and BP neural network can make a more accurate prediction on electric energy substitution than a single prediction method,which provides certain guidance for the analysis on electric energy substitution potential.

Key words: electric energy substitution potential, time series forecast, BP neural network, combination forecast

中图分类号:

TK01

廖敏乐, 黄重阳, 戴承承, 李化林, 樊高松. 基于时间序列和BP神经网络的电能替代潜力分析方法[J]. 综合智慧能源, 2022, 44(3): 38-43.

LIAO Minle, HUANG Chongyang, DAI Chengcheng, LI Hualin, FAN Gaosong. Analysis method of electric energy substitution potential based on time series and BP neural network[J]. Integrated Intelligent Energy, 2022, 44(3): 38-43.

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年份	标准煤使用量/万t	电能使用量/（TW·h）	电能使用量占能源使用量比例/%	电能替代量/（TW·h）
2000	146 964	1 347.238	11.266 40	84.902
2001	155 547	1 472.346	11.633 22	46.427
2002	169 577	1 646.545	11.933 24	41.397
2003	197 083	1 903.160	11.868 01	-10.461
2004	230 281	2 197.137	11.726 03	-26.604
2005	261 369	2 494.032	11.727 35	0.281
2006	286 467	2 858.797	12.264 80	125.275
2007	311 442	3 271.181	12.908 60	163.146
2008	320 611	3 454.135	13.240 76	86.649
2009	336 126	3 703.214	13.540 31	81.927
2010	360 648	4 193.449	14.290 25	220.068
2011	387 043	4 700.088	14.924 46	199.730
2012	402 138	4 976.264	15.208 28	92.869
2013	416 913	5 420.341	15.978 39	261.244
2014	425 806	5 638.369	16.273 97	102.409
2015	429 905	5 801.997	16.586 58	109.351
2016	435 819	6 129.709	17.285 64	247.897
2017	448 529	6 482.097	17.761 39	173.624
2018	464 000	7 033.075	18.628 55	327.393
2019	486 000	7 649.563	19.344 27	283.023

年份	真实值/（TW·h）	预测值/（TW·h）	相对误差/%
2013	261.243	292.339	11.90
2014	102.408	73.703	28.03
2015	109.350	60.589	44.59
2016	247.896	287.563	16.00
2017	173.622	193.463	11.42
2018	327.394	399.448	22.01
2019	283.023	329.835	16.54

基于时间序列和BP神经网络的电能替代潜力分析方法

Analysis method of electric energy substitution potential based on time series and BP neural network

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