基于BP神经网络的北京夏季日最大电力负荷预测方法

李琛; 郭文利; 吴进; 金晨曦

doi:10.3878/j.issn.1006-9585.2018.17126

基于BP神经网络的北京夏季日最大电力负荷预测方法

doi: 10.3878/j.issn.1006-9585.2018.17126

李琛^{1, 2,},
郭文利²,
吴进³,
金晨曦²

1.
中国气象局北京城市气象研究院, 北京 100089
2.
北京市气象服务中心, 北京 100089
3.
京津冀环境气象预报预警中心, 北京 100089

基金项目:

北京市市委、市政府重点工作及区县政府应急项目预启动专项“城市效应可分辨数值天气预报技术与应用研发” Z151100002115045

详细信息

作者简介:
李琛, 男, 1987年出生, 工程师, 主要从事天气预报与服务研究。E-mail:13635435@qq.com

中图分类号: P49
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- 文章访问数: 759
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- 被引次数: 0
出版历程
- 收稿日期: 2017-08-18
- 网络出版日期: 2017-12-04
- 刊出日期: 2019-01-20

A Method for Prediction of Daily Maximum Electric Loads in the Summer in Beijing Based on the BP Neural Network

Chen LI^{1, 2
,},
Wenli GUO²,
Jin WU³,
Chenxi JIN²

1.
Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
2.
Beijing Meteorological Service Center, Beijing 100089
3.
Environment Meteorology Forecast Center of Beijing-Tianjin-Hebei, Beijing 100089

Funds:

Applicate Reserch and Development of Distinguishable Numerical Weather Forecasting Technology with Urban Effect Z151100002115045

摘要

摘要: 利用2006~2017年北京夏季（6~8月）逐日最大电力负荷和同期气象资料，分析最大电力负荷与各种气象因子的相关性，基于BP（Back Propagation）神经网络算法，建立了两种夏季日最大电力负荷预测模型并对比。结果表明：北京夏季周末基础负荷远小于工作日，剔除时应加以区分；气象因子对气象负荷的影响具有累积效应，累积2 d时两者的相关性最强；结合实际，根据自变量的不同分别建立了两种日最大电力负荷预测模型；经实际预测检验，两种预测模型均取得了较好的预测效果，能够满足电力部门的实际需求，其中自变量中加入前一日气象负荷的模型效果更优。
- BP神经网络 /
- 日最大电力负荷 /
- 累积气象因子 /
- 预测模型
Abstract: Based on daily maximum electric loads and meteorological data in the summer (June-August) from 2006 to 2017 in Beijing, the relationship between electric load and meteorological factors is diagnosed. Using the BP (Back Propagation) neural network algorithm, two maximum electric power load prediction models are established and evaluated. The results indicate that (1) the basic electric load on weekends in Beijing in the summer is much less than that in working days, which should be distinguished when being removed; (2) the influence of meteorological factors on meteorological load has cumulative effect, and the correlation between them is the highest for two days of accumulation; (3) taking the actual situation into account, two different daily maximum electric load forecasting models are established based on different independent variables. Comparing the prediction results with actual data, both of the forecasting models show good prediction performance that can meet the actual demand of the power sector. The forecasting model with meteorological load of the previous day as an independent variable shows better prediction effect.
- BP neural network /
- Daily maximum electric load /
- Cumulative meteorological factors /
- Prediction model

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图 1 2006~2016年北京夏季气象负荷与累积气象因子的相关系数的变化

Figure 1. Variation of correlation coefficients between the meteorological electric loads and cumulative meteorological factors in the summer in Beijing from 2006 to 2016

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图 2 2006~2016年北京夏季平均星期一至星期日最大电力负荷

Figure 2. Averaged summertime daily maximum electric loads from Monday to Sunday in Beijing from 2006 to 2016

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图 3 夏季最大电力负荷预测模型方案1模拟值与实际值的对比：（a）2006~2015年；（b）2010年；（c）2016年

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图 4 2016年夏季最大电力负荷预测模型方案2和方案3模拟结果对比

Figure 4. Comparison of simulation results between scheme 2 and scheme 3 models of daily maximum electric loads in the summer of 2016

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图 5 2017年夏季最大电力负荷预测模型方案1和方案3的预测结果对比：（a）气象因子为预报值；（b）气象因子为实际值

Figure 5. Comparison of prediction results between scheme 1 and scheme 3 models of daily maximum electric loads in the summer of 2017: (a) Meteorological factors are predictive values; (b) meteorological factors are actual values

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表 1 2006~2016年北京市夏季最大电力负荷与各气象因子的相关系数

Table 1. Correlation coefficients between daily maximum electric loads and meteorological factors in the summer in Beijing from 2006 to 2016

	r₀	r
最高气温	0.362^**	0.556^**
最低气温	0.485^**	0.707^**
平均气温	0.499^**	0.741^**
相对湿度	0.041	0.057
降水量	－0.009	－0.065
日照时间	0.064	0.032
风速	－0.057^*	-0.084^*
闷热指数	0.554^**	0.822^**
注：r₀、r分别为剔除基础负荷前与剔除后最大电力负荷与气象因子的相关系数；^*表示通过置信度为99%的显著性检验，^表示通过95%检验。

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表 2 夏季最大电力负荷预测模型方案1的误差统计

Table 2. Error statistics in scheme 1 model of daily maximum electric loads in the summer

	相对误差 < 2%比例	相对误差 < 5%比例	平均相对误差	最大相对误差	与实际值相关系数	均方根误差
模型对2006~ 2015年的模拟值	50.5%	90.4%	2.3%	11.0%	0.99^**	370.6
模型对2016年的模拟值	42.4%	71.7%	3.6%	13.7%	0.96^**	705.2
^**表示通过置信度为99%的显著性检验。

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表 3 夏季最大电力负荷预测模型方案2和方案3的误差对比

Table 3. Comparison of errors between scheme 2 and scheme 3 models of daily maximum electric loads in the summer

		相对误差＜2%比例	相对误差＜5%比例	平均相对误差	最大相对误差	与实际值相关系数	均方根误差
模型对2006~2015年的模拟值	方案2	28.2%	61.9%	3.6%	23.3%	0.97^**	564
模型对2006~2015年的模拟值	方案3	33.8%	72.6%	3.7%	17.5%	0.96^**	552
模型对2016年的模拟值	方案2	24.9%	54.2%	6.9%	34.5%	0.89^**	1311
模型对2016年的模拟值	方案3	31.6%	63.7%	5.8%	21.1%	0.91^**	1034
^**表示通过置信度为99%的显著性检验。

下载: 导出CSV

表 4 2017年夏季最大电力负荷预测模型方案1和方案3的预测误差对比

Table 4. Comparison of errors between scheme 1 and scheme 3 models of daily maximum electric loads in the summer of 2017

		平均相对误差	最大相对误差	均方根误差
方案1	气象因子为预报值	3.4%	11.0%	829
方案1	气象因子为实际值	2.5%	9.6%	674
方案3	气象因子为预报值	5.6%	15.6%	1253
方案3	气象因子为实际值	4.5%	12.1%	1164

下载: 导出CSV

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