基于改进时间卷积网络的微电网超短期负荷预测

王印松; 吕率豪

doi:10.19912/j.0254-0096.tynxb.2023-0307

PDF(2446 KB)

太阳能学报 ›› 2024, Vol. 45 ›› Issue (6) : 255-263. DOI: 10.19912/j.0254-0096.tynxb.2023-0307

基于改进时间卷积网络的微电网超短期负荷预测

王印松, 吕率豪

作者信息 +

ULTRA-SHORT-TERM POWER LOAD PREDICTION OF MICRO-GRID BASED ON IMPROVED TEMPORAL CONVOLUTIONAL NETWORK

Wang Yinsong, Lyu Shuaihao

Author information +

文章历史 +

摘要

为了提高微电网中用电负荷超短期预测的准确性,对时间卷积网络进行特征增强和注意力增强改进,将时间卷积网络中的一维因果膨胀卷积替换为二维卷积,同时利用时间模式注意力机制对时间卷积网络的隐藏层加权处理,提取负荷的多维特征,挖掘序列中存在的潜藏联系。根据改进的方法建立预测模型并进行对比实验以验证方法的有效性,能够对用电负荷的不确定性进行有效的处理,拓宽特征向量的维度,有效捕捉负荷序列中与时间有关的特征,提高用电负荷的预测精度。

Abstract

In order to improve the accuracy of ultra-short-term prediction of power load in micro-grid, feature enhancement and attention enhancement improvements are carried out on temporal convolution networks. It replaces the one-dimensional causal dilated convolution in the time convolution network with two-dimensional convolution, and uses the time pattern attention mechanism to weight the hidden layer of the time convolution network, extract the multidimensional characteristics of the load, and excavate the potential connections in the sequence. According to the improved method, the prediction model is established and a comparative experiment is carried out to verify the accuracy of the proposed method. As the results, it can effectively deal with the uncertainty of the power load, broaden the dimension of the feature vector, effectively capture the time-related characteristics in the load sequence, and improve the prediction accuracy of the power load.

导出引用

王印松, 吕率豪. 基于改进时间卷积网络的微电网超短期负荷预测[J]. 太阳能学报. 2024, 45(6): 255-263 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0307

Wang Yinsong, Lyu Shuaihao. ULTRA-SHORT-TERM POWER LOAD PREDICTION OF MICRO-GRID BASED ON IMPROVED TEMPORAL CONVOLUTIONAL NETWORK[J]. Acta Energiae Solaris Sinica. 2024, 45(6): 255-263 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0307

中图分类号： TM715

参考文献

[1] 郭琦, 卢远宏. 新型电力系统的建模仿真关键技术及展望[J]. 电力系统自动化, 2022, 46(10): 18-32.
GUO Q, LU Y H.Key technologies and prospects of modeling and simulation of new power system[J]. Automation of electric power systems, 2022, 46(10): 18-32.
[2] 王小杨, 罗多, 孙韵琳,等. 基于ABC-SVM和PSO-RF的光伏微电网日发电功率组合预测方法研究[J]. 太阳能学报, 2020, 41(3): 177-183.
WANG X Y, LUO D, SUN Y L, et al.Combined forecasting method of daily photovoltaic power generation in microgrid based on ABC-SVM and PSO-RF models[J]. Acta energiae solaris sinica, 2020, 41(3): 177-183.
[3] AGÜERA-PÉREZ A, PALOMARES-SALAS J C, GONZÁLEZ DE LA ROSA J J, et al. Weather forecasts for microgrid energy management: review, discussion and recommendations[J]. Applied energy, 2018, 228: 265-278.
[4] HERNANDEZ L, BALADRON C, AGUIAR J M, et al.A survey on electric power demand forecasting: future trends in smart grids,microgrids and smart buildings[J]. IEEE communications surveys & tutorials, 2014, 16(3): 1460-1495.
[5] 蔡秋娜, 潮铸, 苏炳洪, 等. 基于一种新型鲁棒损失的神经网络短期负荷预测方法[J]. 电网技术, 2020, 44(11): 4132-4139.
CAI Q N, CHAO Z, SU B H, et al.Short-term load forecasting method based on a novel robust loss neural network algorithm[J]. Power system technology, 2020, 44(11): 4132-4139.
[6] 甘景福, 晏坤, 马明晗, 等. 基于改进聚类算法的人工神经网络短期负荷预测研究[J]. 电工电能新技术, 2022, 41(9): 40-46.
GAN J F, YAN K, MA M H, et al.Research on short-term load forecasting based on modified clustering and artificial neural networks[J]. Advanced technology of electrical engineering and energy, 2022, 41(9): 40-46.
[7] ZENDEHBOUDI A, BASEER M A, SAIDUR R.Application of support vector machine models for forecasting solar and wind energy resources: a review[J]. Journal of cleaner production, 2018, 199: 272-285.
[8] 向玲, 刘佳宁, 苏浩, 等. 基于CEEMDAN二次分解和LSTM的风速多步预测研究[J]. 太阳能学报, 2022, 43(8): 334-339.
XIANG L, LIU J N, SU H,et al.Research on multi-step wind speed forecast based on CEEMDAN secondary decomposition and LSTM[J]. Acta energiae solaris sinica, 2022, 43(8): 334-339.
[9] 王继东, 宋智林, 冉冉. 基于改进支持向量机算法的光伏发电短期功率滚动预测[J]. 电力系统及其自动化学报, 2016, 28(11): 9-13.
WANG J D, SONG Z L, RAN R.Short-term photovoltaic power generation rolling forecast based on optimized SVM[J]. Proceedings of the CSU-EPSA, 2016, 28(11): 9-13.
[10] 姜贵敏, 陈志军, 李笑竹, 等. 基于EEMD-ACS-LSSVM的短期风电功率预测[J]. 太阳能学报, 2020, 41(5) : 77-84.
JIANG G M, CHEN Z J, LI X Z, et al.Short-term prediction of wind power based on EEMD-ACS-LSSVM[J]. Acta energiae solaris sinica, 2020, 41(5): 77-84.
[11] 雷炳银, 王子驰, 苏雨晴, 等. 基于EEMD-CS-LSSVM的短期负荷预测方法研究[J]. 电力系统及其自动化学报, 2021, 33(9): 117-122.
LEI B Y, WANG Z C, SU Y Q, et al.Research on short-term load forecasting method based on EEMD-CS-LSSVM[J]. Proceedings of the CSU-EPSA, 2021, 33(9): 117-122.
[12] 乔石, 王磊, 张鹏超, 等. 基于模态分解及注意力机制长短时间网络的短期负荷预测[J]. 电网技术, 2022, 46(10): 3940-3951.
QIAO S, WANG L, ZHANG P C, et al.Short-term load forecasting by long- and short-term temporal networks with attention based on modal decomposition[J]. Power system technology, 2022, 46(10): 3940-3951.
[13] SONG Z F, LIANG H B, ZHANG X F, et al. Ultra-short-term power load prediction based on deep learning and frequency decomposition[J/OL]. SSRN electronic journal,2022, 4211360 [2023-03-15]. https://ssrn.com/abstract=4211360.
[14] 赵凌云, 刘友波, 沈晓东, 等. 基于CEEMDAN和改进时间卷积网络的短期风电功率预测模型[J]. 电力系统保护与控制, 2022, 50(1): 42-50.
ZHAO L Y, LIU Y B, SHEN X D, et al.Short-term wind power prediction model based on CEEMDAN and an improved time convolutional network[J]. Power system protection and control, 2022, 50(1): 42-50.
[15] RESHEF D N, RESHEF Y A, FINUCANE H K, et al.Detecting novel associations in large data sets[J]. Science, 2011, 334(6062): 1518-1524.
[16] HUANG N E, SHEN Z, LONG S R, et al.The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the royal society of London series A: mathematical, physical and engineering sciences, 1998, 454(1971): 903-995.
[17] COLOMINAS M A, SCHLOTTHAUER G, TORRES M E.Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomedical signal processing and control, 2014, 14: 19-29.
[18] SHIH S Y, SUN F K, LEE H Y.Temporal pattern attention for multivariate time series forecasting[J]. Machine learning, 2019, 108(8): 1421-1441.