基于多层分解——累加原理的城市综合体月度用电量预测方法

doi:10.12204/j.issn.1000-7229.2021.02.004

电力建设 ›› 2021, Vol. 42 ›› Issue (2): 27-34.doi: 10.12204/j.issn.1000-7229.2021.02.004

• 促进高比例新能源消纳的光伏发电功率与负荷预测 ·栏目主持王飞教授 • 上一篇下一篇

基于多层分解——累加原理的城市综合体月度用电量预测方法

杨德州¹, 魏勇¹, 李万伟¹, 彭婧¹, 李敏¹, 李正辉², 甄钊²^,³, 王飞²

1.国网甘肃省电力公司经济技术研究院,兰州市 730050
2.华北电力大学电力工程系, 河北省保定市 071003
3.清华大学电机系,北京市 100084

收稿日期:2020-08-26 出版日期:2021-02-01 发布日期:2021-02-09
作者简介:杨德州(1968),男,工学硕士,高级工程师,研究方向为大电网规划及负荷预测;|魏勇(1975),男,工学学士,高级工程师,研究方向为输配电安全与智能用电;|李万伟(1985),男,工学硕士,高级工程师,研究方向为大电网规划及负荷预测;|彭婧(1990),女,工学硕士,工程师,研究方向为大电网规划及负荷预测;|李敏(1984),女,工学学士,高级工程师,研究方向为输配电安全与智能用电;|李正辉(1996),男,硕士研究生,研究方向为电力系统负荷预测和电量预测|甄钊(1989),男,工学博士,讲师,研究方向为新能源功率预测技术;|王飞(1973),男,工学博士,教授,博士生导师,研究方向为综合能源系统与电力系统预测。
基金资助:
国网甘肃省电力公司科技项目(SGGSJY00PSJS1900123)

Multi-layer Decomposition-Accumulation Principle-Based Monthly Electricity Consumption Forecasting Method for Urban Complex

YANG Dezhou¹, WEI Yong¹, LI Wanwei¹, PENG Jing¹, LI Min¹, LI Zhenghui², ZHEN Zhao²^,³, WANG Fei²

1. State Grid Gansu Economic Research Institute, Lanzhou 730050, China
2. Department of Electrical Engineering, North China Electric Power University, Baoding 071003, Hebei Province, China
3. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Received:2020-08-26 Online:2021-02-01 Published:2021-02-09
Supported by:
State Grid Gansu Electric Power Company Research Program(SGGSJY00PSJS1900123)

摘要/Abstract

摘要：

由于存在严重的模型过拟合问题,传统的城市综合体月度用电量单步预测方法往往不能提供准确的预测结果。提出一种基于多层分解-累加原理的城市综合体月度用电量预测方法。该方法首先将城市综合体内部负荷根据其负荷特性细分为3类;然后,针对每一类型的负荷搜集其历史小时用电量数据,并根据数据的星期标签再次分解,以提高多步预测模型的预测精度;接着,使用改进的经验模态分解(improved empirical mode decomposition, IEMD),将用电量序列中不同尺度的波动和趋势特性分离开来,并利用极端梯度提升(extreme gradient boosting, XGBoost)算法对分解后的各分量分别建立对应的多步预测模型;最后将预测结果逐层累加得到月度用电量预测结果。研究结果表明,文章提出的方法能够有效地捕捉城市综合体用电量变化规律,其预测误差精度比传统方法提升了18.2%~34.9%。

关键词: 城市综合体, 多步预测, 改进的经验模态分解, 极端梯度提升算法

Abstract:

Traditional one-step monthly electricity consumption forecasting methods for urban complex cannot provide accurate forecasting results due to the serious challenge of model overfitting. In this paper, a monthly electricity forecasting method based on multi-layer decomposition-accumulation principle is proposed. The loads of urban complex are firstly subdivided into three categories according to their characteristics. Subsequently, for each category of load, the historical hourly electricity consumption data is collected and decomposed according to its week label so as to improve the forecasting performance of the multi-step forecasting model. Then, improved empirical mode decomposition (IEMD) algorithm is used to separate the fluctuations and trends of different scales in the electricity consumption series, and extreme gradient boosting algorithm (XGBoost) is utilized to establish multi-step forecasting model for each component. Finally, all of the forecasting results are accumulated to obtain monthly electricity consumption forecasting results. Results show that the proposed method can effectively capture the changes in the electricity consumption series. Its forecasting accuracy is improved by 18.2%-34.9% compared with the traditional method.

Key words: urban complex, multi-step forecast, improved empirical mode decomposition, extreme gradient boosting algorithm

中图分类号:

TM715

杨德州, 魏勇, 李万伟, 彭婧, 李敏, 李正辉, 甄钊, 王飞. 基于多层分解——累加原理的城市综合体月度用电量预测方法[J]. 电力建设, 2021, 42(2): 27-34.

YANG Dezhou, WEI Yong, LI Wanwei, PENG Jing, LI Min, LI Zhenghui, ZHEN Zhao, WANG Fei. Multi-layer Decomposition-Accumulation Principle-Based Monthly Electricity Consumption Forecasting Method for Urban Complex[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(2): 27-34.

图/表 7

图1 三类建筑负荷冬季典型日负荷曲线

Fig.1 Typical daily load curves of three types of building loads in winter

图2 历史小时用电量序列分解示意图

Fig.2 Schematic diagram of historical hourly electricity consumption series decomposition

图3 改进的EMD算法分解结果

Fig.3 Decomposition results from the improved EMD algorithm

图4 基于多层分解-累加原理的预测模型

Fig.4 Forecasting model based on multi-layer decomposition-accumulation principle

表1 XGBoost算法参数

Table 1 Parameters of XGBoost algorithm

表2 预测结果评价指标

Table 2 Forecasting performance metrics

表3 城市综合体月度用电量预测结果

Table 3 Forecasting results of monthly electricity consumption of urban complex

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基于多层分解——累加原理的城市综合体月度用电量预测方法

Multi-layer Decomposition-Accumulation Principle-Based Monthly Electricity Consumption Forecasting Method for Urban Complex

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