S&M Young Researcher Paper Award 2020
Recipients: Ding Jiao, Zao Ni, Jiachou Wang, and Xinxin Li [Winner's comments]
Paper: High Fill Factor Array of Piezoelectric Micromachined
Ultrasonic Transducers with Large Quality Factor

S&M Young Researcher Paper Award 2021
Award Criteria
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.

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

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




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