Study on load forecasting for air cooling thermal power units based on intelligent algorithm

doi:10.3969/j.issn.1674-1951.2021.03.009

Abstract

Abstract:

In order to detect the performance of an air-cooling power unit timely and accurately, a big data analysis method based on intelligent algorithm is introduced. Load forecasting models taking BP neural network and Random Forest algorithm are developed for a 600 MW air-cooling thermal power unit based on pretreatment and steady state screening of its historical data. Through analyzing the prediction results and the sensitivity of the models, the Random Forest prediction model is proven to be of high precision, strong generalization ability and short training period. To optimize the Random Forest model, input characteristic parameters are filtered by Pearson's correlation coefficient and the model is set up according to working conditions under different loads. The optimized model can make more accurate prediction.

Key words: School of Energy, Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China

CLC Number:

TK262

PENG Weike, NIE Chunming, CHEN Heng, XU Gang. Study on load forecasting for air cooling thermal power units based on intelligent algorithm[J]. Huadian Technology, 2021, 43(3): 57-64.

Figures/Tables 17

Fig.1

Tab.1

Tab.2

Basic parameters of the reference cold end system

项目	单位	参数
风机直径	m	9.754
风机数量	台	56
风机转速	r/min	17.0 $∼$ 74.8
风机平均风量	m³/s	135 $∼$ 595
风机负荷率区间	%	25 $∼$ 110
冷却单元长度	m	11.800
冷却单元宽度	m	11.890

Tab.2

Tab.3

Operation parameters' reasonable intervals of the reference unit

项目	单位	参数范围
机组功率	MW	240 $∼$ 650
背压	kPa	5.62 $∼$ 50.00
主蒸汽压力	MPa	11.000 $∼$ 24.926
主蒸汽温度	℃	545 $∼$ 572
风机转速	r/min	500 $∼$ 4 088
凝结水温度	℃	34 $∼$ 72
风机功耗	kW	>100

Tab.3

Tab.4

Fig.2

Fig.3

Tab.5

Specific parameters of the BP neural network model

项目	参数
训练方法	反向传播算法
隐层层数	1
激活函数	隐层/输出层: tansig/purelin
训练函数	trainlm
性能函数	均方误差
训练数据划分	训练集/验证集: 80%/20%
最大迭代次数	2 000
隐层神经元数目	5 $∼$ 70,步长5

Tab.5

Fig.4

Fig.5

Fig.6

Tab.6

Fig.7

Fig.8

Tab.7

Fig.9

Tab.8

Random Forest prediction results made by partition modeling and overall modeling

项目	功率范围/MW	δ_MAE/MW	δ_MAPE/%	δ_RMSE/MW	R²	数据量
高负荷区子模型	>450	2.160 8	0.448 0	3.349 8	0.973 5	791
中高负荷区子模型	380 $∼$ 450	2.218 0	0.545 5	3.265 0	0.941 1	3 734
中负荷区子模型	320 $∼$ 380	2.001 2	0.573 4	2.978 2	0.939 9	8 200
低负荷区子模型	>320	1.126 5	0.370 8	1.695 5	0.925 1	2 835
分负荷区建模平均	250 $∼$ 650	1.902 0	0.523 4	2.832 2	0.939 2	15 560
整体建模	250 $∼$ 650	2.259 0	0.622 7	3.371 8	0.990 1	15 560

Tab.8

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项目		单位	参数
主蒸汽	压力	MPa	24.20
	温度	℃	566.0
	流量	kg/s	501.56
再热蒸汽	压力	MPa	3.93
	温度	℃	566.0
	流量	kg/s	410.11
汽轮机排汽	压力	kPa	15.00
	温度	℃	54.0
	流量	kg/s	301.63
	排汽干度	%	93.22
给水温度		℃	282.2
机组功率		MW	600.00
热耗率		kJ/(kW·h)	7 965

判定指标	判定阈值	滑窗长度
机组功率	0.02	10
风机群功耗	0.06	10
主蒸汽温度	0.02	10
凝结水温度	0.02	10

模型评价指标	BP神经网络	随机森林	差异
δ_MAE/MW	2.581 3	1.903 7	-0.677 6
δ_MAPE /%	0.716 5	0.525 6	-0.190 9
δ_RMSE/MW	3.575 6	2.795 6	-0.780 0
R²	0.993 0	0.989 9	-0.003 1

模型评价指标	全因素模型	部分因素模型	差异
δ_MAE /MW	1.903 7	2.259 0	-0.355 3
δ_MAPE/%	0.525 6	0.622 7	-0.097 1
δ_RMSE/MW	2.795 6	3.371 8	-0.576 2
R²	0.989 9	0.990 1	-0.000 2