基于PCC-LSTM模型的短期负荷预测方法

刘倩倩; 刘钰山; 温烨婷; 何杰; 李晓; 毕大强

doi:10.13700/j.bh.1001-5965.2021.0145

基于PCC-LSTM模型的短期负荷预测方法

doi: 10.13700/j.bh.1001-5965.2021.0145

1.
北京航空航天大学自动化科学与电气工程学院, 北京 100191
2.
清华大学电机系, 北京 100084

基金项目:

中央高校基本科研业务费专项资金 KG16076201

电力系统及大型发电设备安全控制和仿真国家重点实验室开放课题 SKLD20M05

详细信息

通讯作者:
刘钰山, E-mail: yushan_liu@buaa.edu.cn

中图分类号: V247; TP183
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- 文章访问数: 528
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- PDF下载量: 55
- 被引次数: 0
出版历程
- 收稿日期: 2021-03-25
- 录用日期: 2021-07-03
- 网络出版日期: 2021-07-14
- 整期出版日期: 2022-12-20

Short-term load forecasting method based on PCC-LSTM model

1.
School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China
2.
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Funds:

The Fundamental Research Funds for the Central Universities KG16076201

Project Funded by the State Key Laboratory of Safety Control and Simulation for Power Systems and Large Power Generation Equipment SKLD20M05

More Information

Corresponding author: LIU Yushan, E-mail: yushan_liu@buaa.edu.cn

摘要

摘要:
短期负荷预测是电网合理调度和平稳运行的基础。为提高短期负荷预测精度，提出了一种基于Pearson相关系数(PCC)和长短期记忆(LSTM)神经网络的短期负荷预测方法。该方法运用Pearson相关性分析对原始多维输入变量组成的时间序列进行相关性分析，选取与电力负荷数据相关性较大的影响因素作为输入量，实现原始数据的降维和选优；再通过LSTM神经网络结合Adam优化算法，对与电力负荷相关性较大的影响因素和负荷实际输出序列之间的非线性关系建立网络模型。以嘉捷BOX和重庆丽苑维景国际大酒店的负荷数据作为实际算例，并与Prophet、LSTNet、门控循环(GRU)神经网络模型方法进行对比。结果表明：所提PCC-LSTM模型预测精度均在91%以上，最高可达95.44%，有效提高了负荷预测的精度。
- Pearson相关系数 /
- 长短期记忆神经网络 /
- 负荷预测 /
- Adam算法 /
- 时间序列
Abstract:
Short-term load forecasting is the basis for reasonable dispatch and smooth operation of the power grid. To improve the accuracy of short-term load forecasting, a method based on Pearson correlation coefficient (PCC) and long and short term memory (LSTM) neural network is proposed. This method uses Pearson correlation analysis to analyze the correlation of the time series composed of original multi-dimensional input variables, and selects the influencing factors with greater correlation with the power load data as the input to achieve the dimensionality reduction of the original data. Then, through the combination of LSTM neural network and Adam optimization algorithm, a network model is established for the nonlinear relationship between the influencing factors that have a greater correlation with the power load and the actual output sequence of the load. Taking the load data of Jiajie BOX and Chongqing International Grand Metropark Liyuan Hotel as calculation examples, the proposed model is compared with Prophet, LSTNet, and gated recurrent unit (GRU) models. Results show that the prediction accuracy of the proposed model is above 91%, with the highest up to 95.44%, thus effectively improving the accuracy of load forecasting.
- Pearson correlation coefficient /
- long and short term memory neural network /
- load forecasting /
- Adam algorithm /
- time series

HTML全文

图 1 LSTM神经网络结构

Figure 1. Structure diagram of LSTM neural network

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图 2 PCC-LSTM模型

Figure 2. PCC-LSTM model

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图 3 嘉捷BOX PCC-LSTM模型2020年12月1日预测结果

Figure 3. Predicted curve of PCC-LSTM model of Jiajie BOX on December 1, 2020

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图 4 嘉捷BOX PCC-LSTM模型2020年12月2日预测结果

Figure 4. Predicted curve of PCC-LSTM model of Jiajie BOX on December 2, 2020

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图 5 嘉捷BOX PCC-LSTM模型2020年12月3日预测结果

Figure 5. Predicted curve of PCC-LSTM model of Jiajie BOX on December 3, 2020

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图 6 嘉捷BOX PCC-LSTM模型2020年12月4日预测结果

Figure 6. Predicted curve of PCC-LSTM model of Jiajie BOX on December 4, 2020

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图 7 嘉捷BOX 2020年12月3日负荷预测结果对比

Figure 7. Comparison of load forecasting results of Jiajie BOX on December 3, 2020

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图 8 重庆丽苑维景国际大酒店PCC-LSTM模型2020年12月3日预测结果

Figure 8. Predicted curve of PCC-LSTM model of Chongqing International Grand Metropark Liyuan Hotel on December 3, 2020

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图 9 重庆丽苑维景国际大酒店PCC-LSTM模型2020年12月4日预测结果

Figure 9. Predicted curve of PCC-LSTM model of Chongqing International Grand Metropark Liyuan Hotel on December 4, 2020

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图 10 重庆丽苑维景国际大酒店PCC-LSTM模型2020年12月5日预测结果

Figure 10. Predicted curve of PCC-LSTM model of Chongqing International Grand Metropark Liyuan Hotel on December 5, 2020

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图 11 重庆丽苑维景国际大酒店PCC-LSTM模型2020年12月6日预测结果

Figure 11. Predicted curve of PCC-LSTM model of Chongqing International Grand Metropark Liyuan Hotel on December 6, 2020

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图 12 重庆丽苑维景国际大酒店2020年12月5日负荷预测结果对比

Figure 12. Comparison of load forecasting results of Chongqing International Grand Metropark Liyuan Hotel on December 5, 2020

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表 1 相关系数范围

Table 1. Range of correlation coefficients

相关程度	系数范围
无关	0~0.2
弱相关	0.2~0.5
强相关	0.5~1

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表 2 嘉捷BOX影响因素相关性分析

Table 2. Correlation analysis of influencing factors of Jiajie BOX

影响因素	工作日	节假日	天气	温度	湿度
用电量相关性	0.320^**	—	-0.027 3	-0.222^**	-0.372^**
注：“**”在0.01级别(双尾)，相关性显著；“—”由于至少有一个变量为常量，因此无法进行计算。

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表 3 重庆丽苑维景国际大酒店影响因素相关性分析

Table 3. Correlation analysis of influencing factors of Chongqing International Grand Metropark Liyuan Hotel

影响因素	工作日	节假日	天气	温度	湿度
用电量相关性	-0.022 7	—	-0.089^**	0.100^**	-0.313^**
注：“**”在0.01级别(双尾)，相关性显著；“—”由于至少有一个变量为常量，因此无法进行计算。

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表 4 嘉捷BOX PCC-LSTM模型预测结果y_MAPE和y_RMSE对比

Table 4. Comparison of y_MAPE and y_RMSE of prediction results with PCC-LSTM model of Jiajie BOX

日期	y_MAPE/%	y_RMSE
2020年12月1日	6.086	70.302
2020年12月2日	5.402	77.228
2020年12月3日	4.781	56.796
2020年12月4日	4.892	49.412

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表 5 不同方法嘉捷BOX日负荷预测结果比较

Table 5. Comparison of results of daily load forecasting by different methods of Jiajie BOX

模型/方法	y_MAPE/%	y_RMSE	计算时间/h
Prophet	26.931	249.61	0.02
LSTNet	11.497	154.051	20.16
GRU	7.583	104.751	1.56
PCC-LSTM	4.562	54.685	2

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表 6 重庆丽苑维景国际大酒店PCC-LSTM模型预测结果y_MAPE和y_RMSE对比

Table 6. Comparison of y_MAPE and y_RMSE of prediction results with PCC-LSTM model of Chongqing International Grand Metropark Liyuan Hotel

日期	y_MAPE/%	y_RMSE
2020年12月3日	8.047	28.661
2020年12月4日	7.756	25.084
2020年12月5日	4.761	18.823
2020年12月6日	6.261	24.132

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表 7 不同方法重庆丽苑维景国际大酒店日负荷预测结果比较

Table 7. Comparison of results of daily load forecasting by different methods of Chongqing International Grand Metropark Liyuan Hotel

模型/方法	y_MAPE/%	y_RMSE	计算时间/h
Prophet	13.021	49.532	0.02
LSTNet	18.689	60.934	21.1
GRU	5.069	23.03	2.53
PCC-LSTM	4.866	20.266	3.1

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