Journal of Shandong University(Engineering Science) ›› 2021, Vol. 51 ›› Issue (4): 91-98.doi: 10.6040/j.issn.1672-3961.0.2020.539

• Electrical Engineering • Previous Articles     Next Articles

Short-term load forecasting of iron and steel industry area based on combination model of SVM and LSTM

Xiaoyan QI1(),Hengjie LIU1,Qiuhua HOU1,Xiaoyu LIU1,Yanchao TAN1,Liancheng WANG2,*()   

  1. 1. Sate Grid Laiwu Power Company, Jinan 271001, Shandong, China
    2. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China
  • Received:2020-12-22 Online:2021-08-20 Published:2021-08-18
  • Contact: Liancheng WANG E-mail:qxy_2001@sina.com;lwang@hisingpower.com

Abstract:

A short-term load forecasting algorithm combining long short-term memory (LSTM) and support vector machine (SVM) was proposed to solve the low accuracy problem of short-term load forecasting due to the large-scale iron and steel enterprise power consumption impact on regional load. The research thoroughly analyzed the load characteristics of the selected region with predominant iron and steel mill load, which divided the load into the impulse load and others based on its various components.Covariance algorithm and Pearson algorithm were used to analyze the correlation and differentiation of load influence factors. Six attributes of historical load, temperature, date type, steel price, electricity price and iron ore price were selected as load forecasting. The fuzzy weight assignment was used to fuse LSTM and SVM which got the final load forecasting result. The simulation results showed that the proposed method could predict the short-term load more accurately than the single LSTM or SVM.

Key words: iron and steel industry, short-term load forecasting, LSTM, SVM, impulse load

CLC Number: 

  • TM714

Fig.1

Daily load curves of iron and steel companies"

Table 1

Correlative analysis of load and influence factors"

冲击性负荷影响因素名称
铁矿石期货价格 螺纹钢期货价格 气温 湿度 风向 负荷历史数据1 负荷历史数据2 周日期 风速 日类型
皮尔逊算法 0.356 7 0.335 6 0.301 0 0.453 7 0.167 8 0.612 0 0.534 0 0.109 1 0.100 8 0.556 1
协方差算法 0.400 0 0.350 0 0.320 0 0.510 2 0.058 9 0.699 8 0.467 8 0.110 0 0.088 1 0.410 0

Table 2

Inputs of load forecasting"

输入量 描述
铁矿石期货价格 上一周铁矿石期货均价
螺纹钢期货价格 上一周铁螺纹钢期货均价
气温 t时刻的预测温度
湿度 t时刻的预测湿度
负荷历史数据1 预测日前30 d内的同时刻负荷
负荷历史数据2 预测时刻前24 h内各时刻负荷
日类型 工作日或者节假日, 以1/0表示

Fig.2

Stability load curves"

Table 3

Correlative analysis of load and other"

稳定性负荷影响因素名称
钢价 日期类型 电价 气温
协方差算法 0.000 7 0.510 5 0.412 3 0.612 0
皮尔逊算法 0.000 2 0.489 0 0.453 4 0.587 4

Table 4

Inputs of load forecasting"

输入量 描述
日类型 预测日前7 d的工作日或节假日类型, 以1/0表示
电价 预测日前7 d的电价
气温 预测日前7 d的温度
负荷历史 预测日前7 d的历史负荷

Fig.3

LSTM Neuron structure"

Fig.4

Fuzzy function"

Fig.5

Daily load curves of April 15"

Fig.6

Proportion of steel load curve on April 15"

Table 5

Comparison of load prediction accuracy  %"

日期平均相对误差
LSTM SVM 组合算法
5月1日 1.81 2.84 1.74
5月2日 2.20 3.53 2.00
5月3日 1.98 2.06 1.50
5月4日 2.10 3.64 2.00
5月5日 2.98 2.20 1.97
5月6日 3.36 3.88 2.42
5月7日 2.06 2.87 1.69

Fig.7

Effect of learning efficiency on training times and MAPE"

Fig.8

MAPE function"

Fig.9

MAPE function contour"

Fig.10

Daily load forecasting curves"

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