A big data framework for short-term power load forecasting using heterogenous data

Haibo ZHAO; Zhijun XIANG; Linsong XIAO

doi:10.11959/j.issn.1000-0801.2022292

您当前的位置：

首页 >

文章列表页 >

A big data framework for short-term power load forecasting using heterogenous data

Research and Development | 更新时间：2024-06-05

- A big data framework for short-term power load forecasting using heterogenous data
- Telecommunications Science Vol. 38, Issue 12, Pages: 103-111(2022)
- 作者机构：
  
  1. 威胜信息技术股份技术有限公司，湖南长沙 410205
  2. 中国电力技术装备有限公司，北京 100052
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2022292
  CLC： U416.216
- Published Online：2022-12，
  
  Published：20 December 2022
- 稿件说明：
移动端阅览
Haibo ZHAO, Zhijun XIANG, Linsong XIAO. A big data framework for short-term power load forecasting using heterogenous data[J]. Telecommunications science, 2022, 38(12): 103-111.
DOI：

Haibo ZHAO, Zhijun XIANG, Linsong XIAO. A big data framework for short-term power load forecasting using heterogenous data[J]. Telecommunications science, 2022, 38(12): 103-111. DOI： 10.11959/j.issn.1000-0801.2022292.

摘要

随着多种可再生能源电力的接入，电力系统正在向更智能、更灵活、交互性更高的系统过渡。负荷预测，特别是针对单个电力客户的短期负荷预测在未来电网规划和运行中发挥着越来越重要的作用。提出了一个基于异构数据的电力短期负荷大数据预测方案，该方案收集来自智能电表和天气预报的数据，预处理后将其加载到非关系型数据库中进行存储并做进一步的异构数据处理；设计并实现了一个长短期记忆递归神经网络模型，用于确定负荷分布并预测未来24 h的住宅小区用电量；最后利用一个住宅小区的智能电表数据集对提出的短期负荷预测框架进行了测试，并使用均方根误差和平均绝对百分比误差两个指标，对比了预测模型与两种经典算法的性能，验证了所提模型的有效性。

Abstract

The power system is in a transition towards a more intelligent

flexible and interactive system with higher penetration of renewable energy generation

load forecasting

especially short-term load forecasting for individual electric customers plays an increasingly essential role in future grid planning and operation.A big data framework for short-term power load forcasting using heterogenous was proposed

which collected the data from smart meters and weather forecast

pre-processed and loaded it into a NoSQL database that was capable to store and further processing large volumes of heterogeneous data.Then

a long short-term memory (LSTM) recurrent neural network was designed and implemented to determine the load profiles and forecast the electricity consumption for the residential community for the next 24 hours.The proposed framework was tested with a publicly available smart meter dataset of a residential community

of which LSTM’s performance was compared with two benchmark algorithms in terms of root mean square error and mean absolute percentage error

and its validity has been verified.

关键词

Keywords

references

张东霞 , 王继业 , 刘科研 , 等 . 大数据技术在配用电系统的应用 [J ] . 供用电 , 2015 , 32 ( 8 ): 6 - 11 .

ZHANG D X , WANG J Y , LIU K Y , et al . Application of big data technologies in power distribution and utilization system [J ] . Distribution ＆ Utilization , 2015 , 32 ( 8 ): 6 - 11 .

赵明欣 , 张春雷 , 宋曼诗 , 等 . 基于居民小区负荷成长周期的负荷预测方法 [J ] . 供用电 , 2016 , 33 ( 8 ): 64 - 68 .

ZHAO M X , ZHANG C L , SONG M S , et al . A load forecasting method based on load growth cycle of residential community [J ] . Distribution ＆ Utilization , 2016 , 33 ( 8 ): 64 - 68 .

JIANG H , WANG K , WANG Y H , et al . Energy big data:a survey [J ] . IEEE Access , 2016 ( 4 ): 3844 - 3861 .

DAKI H , EL HANNANI A , AQQAL A , et al . Big data management in smart grid:concepts,requirements and implementation [J ] . Journal of Big Data , 2017 ( 4 ): 13 .

HONG T , GUI M , BARAN M E , et al . Modeling and forecasting hourly electric load by multiple linear regression with interactions [C ] // Proceedings of IEEE PES General Meeting . Piscataway:IEEE Press , 2010 .

王克杰 , 张瑞 . 基于改进 BP 神经网络的短期电力负荷预测方法研究 [J ] . 电测与仪表 , 2019 , 56 ( 24 ): 70 - 75 .

WANG K J , ZHANG R . Research on short-term power load forecasting method based on improved BP neural network [J ] . Electrical Measurement ＆ Instrumentation , 2019 , 56 ( 24 ): 70 - 75 .

OPREA S V , BARA A . Machine learning algorithms for short-term load forecast in residential buildings using smart meters,sensors and big data solutions [J ] . IEEE Access , 2019 ( 7 ): 177874 - 177889 .

王德文 , 周昉昉 . 基于无监督极限学习机的用电负荷模式提取 [J ] . 电网技术 , 2018 , 42 ( 10 ): 3393 - 3400 .

WANG D W , ZHOU F F . Extraction of electricity consumption load pattern based on unsupervised extreme learning machine [J ] . Power System Technology , 2018 , 42 ( 10 ): 3393 - 3400 .

ZHOU H X , ZHANG Y J , YANG L F , et al . Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism [J ] . IEEE Access , 2019 ( 7 ): 78063 - 78074 .

MOR G , VILAPLANA J , DANOV S , et al . EMPOWERING,a smart big data framework for sustainable electricity suppliers [J ] . IEEE Access , 2018 ( 6 ): 71132 - 71142 .

MOGHADDASS R , WANG J H . A hierarchical framework for smart grid anomaly detection using large-scale smart meter data [J ] . IEEE Transactions on Smart Grid , 2018 , 9 ( 6 ): 5820 - 5830 .

ZHAO T , ZHOU Z Q , ZHANG Y , et al . Spatio-temporal analysis and forecasting of distributed PV systems diffusion:a case study of Shanghai using a data-driven approach [J ] . IEEE Access , 2017 ( 5 ): 5135 - 5148 .

Views

163

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Unsupervised intrusion detection model based on temporal convolutional network

Research on new energy station network security assessment method based on improved LSTM network

Security access permission authentication simulation of multi-source network big data based on blockchain

Research on theoretical ultimate value of 5G flow residence ratio based on big data

Substructure correlation adaptation transfer learning method based on K-means clustering

Related Author

LIAO Jinju

DING Jiawei

FENG Guanghui

LIU Shan

LI Rui

WANG Yao

Bin LI

Hui HE

Related Institution

School of Computer Science and Cyber Engineering, Guangzhou University

School of Information Engineering, Zhengzhou University of Industrial Technology

State Grid Shanxi Electric Power Company

State Grid Shanxi Electric Power Research Institute

Research Institute of China United Network Communications Co., Ltd.

⁰