Short-Term Load Forecasting Based on Tensor Low-Rank Completion Algorithm in Extreme Weather

Jiahuan FENG; Xuechen SHI; Yun ZHANG; Tao HU; Yu FENG; Chenwei HONG; Yi HONG; Yuetao WU

doi:10.16513/j.2096-2185.DE.2409406

TSINGHUA JOURNALS HOME

Source Journal for Chinese Scientific and Technical Papers and Citations

RCCSE Chinese Core Academic Journals

Classification Catalogue of High Quality Scientific Journals in the field of Energy and Power

Classification Catalogue of High Quality Scientific Journals in Coal Field

PDF(9746 KB)

Distributed Energy ›› 2024, Vol. 9 ›› Issue (4) : 51-59. DOI: 10.16513/j.2096-2185.DE.2409406

Basic Research

Short-Term Load Forecasting Based on Tensor Low-Rank Completion Algorithm in Extreme Weather

Author information +

History +

Abstract

Efficient and accurate short-term power load forecasting is very important to improve the economic operation of the new power system. In view of the characteristics of less load forecasting data and strong randomness in extreme weather scenarios, a short-term load forecasting model based on the tensor low-rank completion algorithm is proposed, and extreme high temperature scenarios are selected for the research. First, the definition of extreme weather is given and data screening is performed based on the improved heat index and temperature. Then, a tensor-based load data completion model is proposed to complete the missing data. The input features are selected by Pearson correlation analysis, and the short-term load forecasting model based on long and short time memory (LSTM) network and rough set theory (RST) is constructed. Finally, the actual load data in Suzhou is used for verification, and the simulation results show that the proposed short-term forecasting method has high accuracy.

Key words

extreme weather / high temperature scenario / heat index / short-term load forecasting / tensor low-rank completion / long short term memory (LSTM) network / rough set theory (RST)

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Jiahuan FENG , Xuechen SHI , Yun ZHANG , et al . Short-Term Load Forecasting Based on Tensor Low-Rank Completion Algorithm in Extreme Weather[J]. Distributed Energy Resources. 2024, 9(4): 51-59 https://doi.org/10.16513/j.2096-2185.DE.2409406

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis

[1]	中国电力企业联合会. 中电联发布《2023—2024年度全国电力供需形势分析预测报告》[EB/OL]. (2024-01-30)[2024-02-22]. https://www.cec.org.cn/detail/index.html?3-330280 Cited in this article [1]

[2]	孟昭睿. 电力系统短期负荷的组合预测方法研究[D]. 武汉：武汉大学，2022. MENG Zhaorui. Research on combined forecasting methods of short-term load in power system[D]. Wuhan: Wuhan University, 2022. Cited in this article [1]

[3]	张航，杨靖，李昊霖. 基于改进回归模型的电网降温负荷预测[J]. 控制工程，2023, 30(3): 513-519. ZHANG Hang, YANG Jing, LI Haolin. Power grid cooling load forecast based on improved regression model[J]. Control Engineering of China, 2023, 30(3): 513-519. Cited in this article [2]

[4]

孙玉芹，王亚文，朱威，等. 基于考虑气温影响的门限自回归移动平均模型居民日用电负荷预测[J]. 电力建设，2022, 43(9): 117-124.

SUN

Yuqin

, WANG

Yawen

, ZHU

Wei

, et al. Residential daily power load forecasting based on threshold ARMA model considering the influence of temperature[J]. Electric Power Construction, 2022, 43(9): 117-124.

Cited in this article [2]

[5]	徐良德，郭挺，雷才嘉，等. 基于支持向量机的网格化电网负荷预测算法设计[J]. 电子设计工程，2024, 32(3): 12-16. XU Liangde, GUO Ting, LEI Caijia, et al. Design of grid power network load forecasting algorithm based on spport vector machine[J]. Electronic Design Engineering, 2024, 32(3): 12-16. Cited in this article [1]

[6]	张功勋，姚方，曹赟. 基于CNN-SVR城市日负荷预测机制[J]. 电气自动化，2022, 44(5): 38-40. ZHANG Gongxun, YAO Fang, CAO Yun. City daily load forecasting mechanism based on CNN-SVR[J]. Electrical Automation, 2022, 44(5): 38-40. Cited in this article [1]

[7]	闫秀英，樊晟志. 基于RW-SSA-GRNN的短期电力负荷预测[J]. 分布式能源，2022, 7(6): 37-43. YAN Xiuying, FAN Shengzhi. Short-term power load forecasting based on RW-SSA-GRNN[J]. Distributed Energy, 2022, 7(6): 37-43. Cited in this article [1]

[8]	李润清. 基于TCN-TPA的短期负荷预测方法研究[D]. 兰州：兰州理工大学，2023. LI Runqing. Research on short-term load forecasting method based on TCN-TPA[D]. Lanzhou: Lanzhou University of Technology, 2023. Cited in this article [1]

[9]	秦浩. 基于改进时间卷积网络的短期电力负荷预测研究[D]. 南昌：南昌大学，2023. QIN Hao. Research on short-term load forecasting based on improved temporal convolutional network[D]. Nanchang: Nanchang University, 2023. Cited in this article [1]

[10]	彭泽森，刘庆珍，张溢. 基于多模型综合特征选择和LSTM-Attention的短期负荷预测[J]. 分布式能源，2022, 7(6): 11-20. PENG Zesen, LIU Qingzhen, ZHANG Yi. Short-term load forecasting based on multi-model comprehensive feature selection and LSTM-attention[J]. Distributed Energy, 2022, 7(6): 11-20. Cited in this article [2]

[11]

姚芳，汤俊豪，陈盛华，等. 基于ISSA-CNN-GRU模型的电动汽车充电负荷预测方法[J]. 电力系统保护与控制，2023, 51(16): 158-167.

YAO

Fang

, TANG

Junhao

, CHEN

Shenghua

, et al. Charging load prediction method for electric vehicles based on an ISSA-CNN-GRU model[J]. Power System Protection and Control, 2023, 51(16): 158-167.

Cited in this article [1]

[12]	LIU Nian, TANG Qingfeng, ZHANG Jianhua, et al. A hybrid forecasting model with parameter optimization for short-term load forecasting of micro-grids[J]. Applied Energy, 2014, 129(15): 336-345. Cited in this article [1]

[13]	GUAN C, LUH P B, MICHEL L D, et al. Very short-term load forecasting: wavelet neural networks with data pre-filtering[J]. IEEE Transactions on Power Systems, 2013, 28(1): 30-41. Cited in this article [1]

[14]

窦真兰，张春雁，许一洲，等. 基于多变量相空间重构和径向基函数神经网络的综合能源系统电冷热超短期负荷预测[J]. 电网技术，2024, 48(1): 121-133.

DOU

Zhenlan

, ZHANG

Chunyan

, XU

Yizhou

, et al. Ultra-short-term load forecasting of electrical, cooling and heating for integrated energy system based on multivariate phase space reconstruction and radial basis function neural network[J]. Power System Technology, 2024, 48(1): 121-133.

Cited in this article [1]

[15]	陈浩文. 考虑多能源耦合的综合能源系统多元负荷协同预测研究[D]. 北京：华北电力大学，2023. CHEN Haowen. Study on multi-energy load collaborative forecasting of integrated energy system considering multi-energy coupling[D]. Beijing: North China Electric Power University, 2023. Cited in this article [1]

[16]

封钰，宋佑斌，金晟，等. 基于随机森林算法和粗糙集理论的改进型深度学习短期负荷预测模型[J]. 发电技术，2023, 44(6): 889-895.

FENG

, SONG

Youbin

, JIN

Sheng

, et al. Improved deep learning model for forecasting short-term load based on random forest algorithm and rough set theory[J]. Power Generation Technology, 2023, 44(6): 889-895.

Cited in this article [3]

[17]

伍骏杰，张倩，陈凡，等. 计及误差修正的变分模态分解-长短期记忆神经网络短期负荷预测[J]. 科学技术与工程，2022, 22(12): 4828-4834.

Junjie

, ZHANG

Qian

, CHEN

Fan

. Short-term load forecasting with error correction and variational mode decomposition-long short-term memory[J]. Science Technology and Engineering, 2022, 22(12): 4828-4834.

Cited in this article [1]

[18]

袁畅，王森，孙永辉，等. 基于混合特征双重衍生和误差修正的风电功率超短期预测[J]. 电力系统自动化，2024, 48(5): 68-76.

YUAN

Chang

, WANG

Sen

, SUN

Yonghui

, et al. Ultra-short-term forecasting of wind power based on dual perivation of hybrid features and error correction[J]. Automation of Electric Power Systems, 2024, 48(5): 68-76.

Cited in this article [1]

[19]	张夏韦，梁军，王要强，等. 电动汽车充电负荷时空分布预测研究综述[J]. 电力建设，2023, 44(12): 161-173. ZHANG Xiawei, LIANG Jun, WANG Yaoqiang, et al. Overview of research on spatiotemporal distributed prediction of electric vehicle charging[J]. Electric Power Construction, 2023, 44(12): 161-173. Cited in this article [1]

[20]	陈宋宋，王阳，周颖，等. 基于客户用电数据的多时空维度负荷预测综述[J]. 电网与清洁能源，2023, 39(12): 28-40. CHEN Songsong, WANG Yang, ZHOU Ying, et al. A review of multi-time-space load forecasting based on customer electricity consumption data[J]. Power System and Clean Energy, 2023, 39(12): 28-40. Cited in this article [1]

[21]	鞠冠章，王靖然，崔琛，等. 极端天气事件对新能源发电和电网运行影响研究[J]. 智慧电力，2022, 50(11): 77-83. JU Guanzhang, WANG Jinran, CUI Chen, et al. Impact of extreme weather events on new energy power generation and power grid operation[J]. Smart Power, 2022, 50(11): 77-83. Cited in this article [1]

[22]	戴明明，王康，李强，等. 基于天气分类和卷积神经网络的短期负荷预测方法[J]. 电力需求侧管理，2023, 25(3): 93-98. DAI Mingming, WANG Kang, LI Qiang, et al. Short-term load forecasting method based on weather classification and convolutional neural network[J]. Power Demand Side Management, 2023, 25(3): 93-98. Cited in this article [2]

[23]

徐先峰，赵依，刘状壮，等. 用于短期电力负荷预测的日负荷特性分类及特征集重构策略[J]. 电网技术，2022, 46(4): 1548-1556.

Xianfeng

, ZHAO

, LIU

Zhuangzhuang

, et al. Daily load characteristic classification and feature set reconstruction strategy for short-term power load forecasting[J]. Power System Technology, 2022, 46(4): 1548-1556.

Cited in this article [1]

[24]	MARTIN H, WOLTER K, PERLWITZ J, et al. Northeast Colorado extreme rains interpreted in a climate change context[J]. Bulletin of the American Meteorological Society, 2014, 95(9): 15-18. Cited in this article [1]

[25]	黄卓，陈辉，田华. 高温热浪指标研究[J]. 气象，2011, 37(3): 345-351. HUANG Zhuo, CHEN Hui, TIAN Hua. Research on the heat wave index[J]. Meteorological Monthly, 2011, 37(3): 345-351. Cited in this article [1]

[26]	杨挺，叶芷杉，徐嘉成，等. 基于低秩张量补全的非侵入式负荷监测缺失数据修复方法[J]. 电网技术，2024, 48(1): 394-405. YANG Ting, YE Zhishan, XU Jiacheng, et al. A non-intrusive load monitoring missing data recovery method based on low-rank tensor completion[J]. Power System Technology, 2024, 48(1): 394-405. Cited in this article [3]

[27]	ZHANG H, CHEN P, ZHENG J, et al. Missing data detection and imputation for urban ANPR system using an iterative tensor decomposition approach[J]. Transportation Research Part C Emerging Technologies, 2019, 107: 337-355. Cited in this article [2]

[28]	寿佩瑶. 低压台区缺失数据的张量补全方法研究[D]. 北京：华北电力大学，2022. SHOU Peiyao. Research on the tensor completion method of missing data in low-voltage area network[D]. Beijing: North China Electric Power University, 2022. Cited in this article [2]