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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
[ "赵海波(1978- ),男,博士,威胜信息技术股份有限公司高级工程师,主要研究方向为电力大数据分析、移动通信、电力终端设计" ]
[ "相志军(1984- ),男,中国电力技术装备有限公司高级工程师,主要研究方向为AMI系统数据采集" ]
[ "肖林松(1980- ),男,威胜信息技术股份有限公司高级工程师,主要研究方向为电力终端设备开发、电力大数据分析" ]
网络出版日期:2022-12,
纸质出版日期:2022-12-20
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赵海波, 相志军, 肖林松. 基于异构数据的电力短期负荷大数据预测方案[J]. 电信科学, 2022,38(12):103-111.
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.
赵海波, 相志军, 肖林松. 基于异构数据的电力短期负荷大数据预测方案[J]. 电信科学, 2022,38(12):103-111. DOI: 10.11959/j.issn.1000-0801.2022292.
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.
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。
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.
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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 .
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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 .
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