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文章摘要
基于深度神经网络与权值共享的工业园区负荷预测
A deep multitask learning approach for load forecasting and its task aggregation analysis
Received:September 11, 2019
Revised:September 11, 2019
DOI：
10.19753/j.issn.1001-1390.2021.01.020
中文关键词
:
工业园区负荷预测
深度学习
权值共享
任务聚合
英文关键词
:
industrial-park
load forecasting, deep
learning, weight
sharing, tasks
clustering
基金项目
:
国家自然科学基金项目（ 59577060），国家高技术研究发展计划（863计划）特大型城市电网防御大停电的应急调度与恢复关键技术研究
Author Name
Affiliation
E-mail
Wang Gang
State Grid Tianjin Electric Power Company
,
Tianjin
359690477@qq.com
Yang Xiaojing
State Grid Tianjin Electric Power Company
,
Tianjin
yangxj@qq.com
Zhang Zhijun
State Grid Tianjin Chengnan Power Supply Branch
,
Tianjin
zhangzhijun123@163.com
Liu Lixin
Beijing Tsingsoft Technology Co
,
Ltd
,
Beijing
,
China
1156039469@qq.com
Yu Meili
Beijing Tsingsoft Technology Co
,
Ltd
,
Beijing
,
China
2813964@qq.com
Abinet Tesfaye Eseye
*
North China Electric Power University
,
State Key Laboratory of Alternative Electrical Power System With Renewable Energy Sources
1423305270@qq.com
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中文摘要
:
电力体制市场化的有序推进对工业园区负荷预测提出了新的要求。本文提出了基于深度学习与权值共享机理的负荷预测方法。在预测模型中,将深度置信网络设置为训练中的有监督学习方法,权值共享模式分析了多个目标之间的相关性,并使用各个目标的负荷变化率对相关度最高的任务聚合。算例中使用广东某高新区数据对算法有效性进行了验证,结果显示本文算法有效提高了工业园区负荷预测的精度,有着较高应用价值。
英文摘要
:
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