Young Researcher Paper Award 2021
🥇Winners

Notice of retraction
Vol. 32, No. 8(2), S&M2292

Print: ISSN 0914-4935
Online: ISSN 2435-0869
Sensors and Materials
is an international peer-reviewed open access journal to provide a forum for researchers working in multidisciplinary fields of sensing technology.
Sensors and Materials
is covered by Science Citation Index Expanded (Clarivate Analytics), Scopus (Elsevier), and other databases.

Instructions to authors
English    日本語

Instructions for manuscript preparation
English    日本語

Template
English

Publisher
 MYU K.K.
 Sensors and Materials
 1-23-3-303 Sendagi,
 Bunkyo-ku, Tokyo 113-0022, Japan
 Tel: 81-3-3827-8549
 Fax: 81-3-3827-8547

MYU Research, a scientific publisher, seeks a native English-speaking proofreader with a scientific background. B.Sc. or higher degree is desirable. In-office position; work hours negotiable. Call 03-3827-8549 for further information.


MYU Research

(proofreading and recording)


MYU K.K.
(translation service)


The Art of Writing Scientific Papers

(How to write scientific papers)
(Japanese Only)

Sensors and Materials, Volume 34, Number 4(1) (2022)
Copyright(C) MYU K.K.
pp. 1275-1285
S&M2890 Research Paper of Special Issue
https://doi.org/10.18494/SAM3468
Published: April 4, 2022

Prediction of Short-term Load of Microgrid Based on Multivariable and Multistep Long Short-term Memory [PDF]

Dashuang Li

(Received June 17, 2021; Accepted January 12, 2022)

Keywords: microgrid, load prediction, LSTM, multivariable and multistep

In a microgrid system, a phasor measurement device (PMU) is used to measure the electrical quantities of nodes, which can provide accurate data for system stability control. How to use the data measured using a PMU to improve the stability of a microgrid is an important practical problem. The mismatch between generation power and load power in a microgrid system will cause oscillation in the system. To ensure accurate and rapid load forecasting in a microgrid system and the reliable and safe operation of the microgrid, deep learning is introduced into microgrid load prediction, and a method of predicting the short-term load for a microgrid based on multivariable and multistep long short-term memory (MM-LSTM) is proposed in this paper. The method considers the effects of meteorological factors on load data and forecasts the current load situation from the load data and the temperature and humidity data of the previous period. A Keras-based model of the short-term load for microgrid prediction based on MM-LSTM is built and its parameters are optimized. Then, the load of a microgrid is predicted using the power consumption and meteorological data. The average absolute percentage error between the experimental results and the actual power consumption is 8.827%, demonstrating the effectiveness of the method.

Corresponding author: Dashuang Li


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Cite this article
Dashuang Li, Prediction of Short-term Load of Microgrid Based on Multivariable and Multistep Long Short-term Memory, Sens. Mater., Vol. 34, No. 4, 2022, p. 1275-1285.



Forthcoming Regular Issues


Forthcoming Special Issues

Special Issue on Collection, Processing, and Applications of Measured Sensor Signals
Guest editor, Hsiung-Cheng Lin (National Chin-Yi University of Technology)


Special Issue on Advanced Materials and Sensing Technologies on IoT Applications: Part 4-3
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)


Special Issue on IoT Wireless Networked Sensing for Life and Safety
Guest editor, Toshihiro Itoh (The University of Tokyo) and Jian Lu (National Institute of Advanced Industrial Science and Technology)
Call for paper


Special Issue on the International Multi-Conference on Engineering and Technology Innovation 2021 (IMETI2021)
Guest editor, Wen-Hsiang Hsieh (National Formosa University)
Conference website


Special Issue on Biosensors and Biofuel Cells for Smart Community and Smart Life
Guest editor, Seiya Tsujimura (University of Tsukuba), Isao Shitanda (Tokyo University of Science), and Hiroaki Sakamoto (University of Fukui)
Call for paper


Special Issue on Novel Sensors and Related Technologies on IoT Applications: Part 1
Guest editor, Teen-Hang Meen (National Formosa University), Wenbing Zhao (Cleveland State University), and Cheng-Fu Yang (National University of Kaohsiung)
Call for paper


Copyright(C) MYU K.K. All Rights Reserved.