Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features
2023-07-21
【论文推荐】国网宁夏电力 孙帆等：基于多元特征动态相似日与长短时记忆网络的综合能源系统负荷预测方法
摘要
为充分挖掘综合能源系统(integrated energy system，IES)丰富的特征规律，进一步提高短期负荷预测精度，提出一种基于多元特征动态相似日与长短时记忆网络(long short-term memory，LSTM)的IES负荷预测方法。首先进行特征扩充，构造粗细时间粒度交叉、远近时间周期交叉、涵盖负荷与气象信息的综合负荷日序列。其次采用高斯混合模型（gaussian mixture model， GMM）对综合负荷日进行场景划分，进而利用灰色关联分析与待预测日的粗时间粒度特征进行场景匹配，选择场景中与待预测日关联度最高的5个典型日加权构造“动态相似日”。最后以邻近日与“动态相似日”关键特征作为输入，采用LSTM进行细时间粒度的多元负荷预测。对比静态特征作为输入场景及基于非扩充单一特征相似日的选择方法，验证了本文所提预测方法的有效性。
Load-forecasting method for IES based on LSTM and dynamic similar days with multi-features
基于多元特征动态相似日与长短时记忆网络的综合能源系统负荷预测方法
Fan Sun1, Yaojia Huo1, Lei Fu2, Huilan Liu3, Xi Wang1, Yiming Ma1
1.State Grid Ningxia Electric Power Technical Research Institute, Yinchuan 750002, P.R.China
2.College of Electronic Information Engineering, Hebei University, Baoding 071002, P.R.China
3.State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, P.R.China
Abstract
Keywords
Integrated energy system; Load forecast; Long short-term memory; Dynamic similar days; Gaussian mixture model
Fig. 1 Heat map of Pearson correlation coefficient between comprehensive load days
Fig. 2 Selection method for dynamic similar days
Fig. 3 The prediction process
Fig. 4 IES load-forecasting model based on LSTM and dynamic similar days with multi-features
Fig. 5 Eight typical classification scenes
Fig. 6 Accuracy comparison for a continuous 20 days
Fig. 7 Classification results for randomly selected continuous 120 days
Fig. 8 Forecasting load for five consecutive days
Fig. 9 Average change curve for six key features in Scenes 3 and 4
本文引文信息
Jiangping Liu；Zong Wang, Hui Hu, Shaoxiang Xu, Jiabin Wang, Ying Liu (2023) Research on the optimization strategy of customers' electricity consumption based on big data, Global Energy Interconnection, 6(3): 273-284
刘江平、王宗、胡惠、徐劭翔、王家斌、刘颖 (2023) 基于电力大数据的用户用电优化策略研究. 全球能源互联网（英文）, 6(3): 273-284
Biographies
Fan Sun
Fan Sun, an assistant engineer, received master degree in electrical engineering from North China Electric Power University in 2021 and has been working in State Grid Ningxia Electric Power Technical Research Institute since 2021.His current research interest includes the load forecasting, coordinated operation of grid-power.
Yaojia Huo
Yaojia Huo, an assistant engineer, received master degree in electrical engineering from China University of Mining & Technology in 2020 and has been working in State Grid Ningxia Electric Power Technical Research Institute since 2020.His current research interest includes electric power environmental protection technology and electric power technology innovation.
Lei Fu
Lei Fu, a lecturer, received doctor degree from Yanshan University in 2019 and then has been working as a teacher in Hebei University.Her current research interest includes the analysis and control of time-delay singular systems,fuzzy systems, and synchronization control of complex networks.
Huilan Liu
Huilan Liu, a senior experimentalist, is currently studying for doctor degree in North China Electric Power University.Her current research interest includes equipment fault diagnosis and distributed energy storage technology research.
Xi Wang
Xi Wang, a senior engineer, graduated from Shanghai Electric Power University and has worked in State Grid Ningxia Electric Power Technical Research Institute for more than 20 years.His current research interest includes automation technology.
Yiming Ma
Yiming Ma, a senior engineer, received master degree in electrical engineering from North China Electric Power University in 2014 and has been working in State Grid Ningxia Electric Power Technical Research Institute since 2014.His current research interest includes automation technology.
编辑：王彦博
审核：王 伟
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