Deep Neural Network Based Hierarchical Control of Residential Microgrid Using LSTM

被引：0

作者：

Kumar, Anu G. ^{[1
]}

Sindhu, M. R. ^{[1
]}

Kumar, Sachin S. ^{[2
]}

机构：

[1] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Dept Elect & Elect Engn, Coimbatore, Tamil Nadu, India

[2] Amrita Vishwa Vidyapeetham, Amrita Sch Engn, Ctr Computat Engn & Networking, Coimbatore, Tamil Nadu, India

来源：

PROCEEDINGS OF THE 2019 IEEE REGION 10 CONFERENCE (TENCON 2019): TECHNOLOGY, KNOWLEDGE, AND SOCIETY | 2019年

关键词：

Deep Learning; LSTM; time series; prediction; microgrid; hierarchical control;

D O I：

10.1109/tencon.2019.8929525

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Microgrid is a hot topic for the present research as their role is significant in framing reliable and efficient power system. Major sources in a typical microgrid are renewable resources like solar, wind etc. Their intermittency and uncertainty in the load demands makes the control and smooth operation of microgrid challenging. This paper presents a Long Short Term Memory (LSTM) network for hierarchical control of a residential microgrid. This multi input multi output LSTM regression architecture is used to predict the optimal real time control action for the microgrid. The performance of the LSTM model is evaluated using root mean square error (RMSE), SMAPE, MRE, MAE, RMSE and loss function. Its performance is compared with other prominent techniques also.

引用

页码：2129 / 2134

页数：6

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