TY - JOUR
T1 - A deep learning architecture for power management in smart cities
AU - Xin, Qin
AU - Alazab, Mamoun
AU - Díaz, Vicente García
AU - Montenegro-Marin, Carlos Enrique
AU - Crespo, Rubén González
N1 - Publisher Copyright:
© 2021
Copyright:
Copyright 2022 Elsevier B.V., All rights reserved.
PY - 2022/11
Y1 - 2022/11
N2 - Sustainable energy management is an inexpensive approach for improved energy use. However, the research used does not focus on cutting-edge technology possibilities in an Internet of things (IoT). This paper includes the needs for today's distributed generation, households, and industries in proposing smart resource management deep learning model. A deep learning architecture of power management (DLA-PM) is presented in this article. It predicts future power consumption for a short period and provides effective communication between power distributors and customers. To keep power consumption and supply constant, mobile devices are linked to a universal IoT cloud server connected to the intelligent grids in the proposed design. An effective brief forecast decision-making method is followed by various preprocessing strategies to deal with electrical data. It conducts extensive tests with RMSE reduced by 0.08 for both residential and business data sources. Significant strengths include refined device-based, real-time energy administration via a shared cloud-based server data monitoring system, optimized selection of standardization technology, a new energy prediction framework, a learning process with decreased time, and lower error rates. In the proposed architecture, mobile devices link to a universal IoT cloud server communicating with the corresponding intelligent grids such that the power consumption and supply phenomena continually continue. It utilizes many preprocessing strategies to cope with the diversity of electrical data, follows an effective short prediction decision-making method, and executes it using resources. For residential and business data sources, it runs comprehensive trials with RMSE lowered by 0.08.
AB - Sustainable energy management is an inexpensive approach for improved energy use. However, the research used does not focus on cutting-edge technology possibilities in an Internet of things (IoT). This paper includes the needs for today's distributed generation, households, and industries in proposing smart resource management deep learning model. A deep learning architecture of power management (DLA-PM) is presented in this article. It predicts future power consumption for a short period and provides effective communication between power distributors and customers. To keep power consumption and supply constant, mobile devices are linked to a universal IoT cloud server connected to the intelligent grids in the proposed design. An effective brief forecast decision-making method is followed by various preprocessing strategies to deal with electrical data. It conducts extensive tests with RMSE reduced by 0.08 for both residential and business data sources. Significant strengths include refined device-based, real-time energy administration via a shared cloud-based server data monitoring system, optimized selection of standardization technology, a new energy prediction framework, a learning process with decreased time, and lower error rates. In the proposed architecture, mobile devices link to a universal IoT cloud server communicating with the corresponding intelligent grids such that the power consumption and supply phenomena continually continue. It utilizes many preprocessing strategies to cope with the diversity of electrical data, follows an effective short prediction decision-making method, and executes it using resources. For residential and business data sources, it runs comprehensive trials with RMSE lowered by 0.08.
KW - Deep learning
KW - Internet of Things
KW - Power management
KW - Wireless communication
UR - http://www.scopus.com/inward/record.url?scp=85122538468&partnerID=8YFLogxK
U2 - 10.1016/j.egyr.2021.12.053
DO - 10.1016/j.egyr.2021.12.053
M3 - Article
AN - SCOPUS:85122538468
SN - 2352-4847
VL - 8
SP - 1568
EP - 1577
JO - Energy Reports
JF - Energy Reports
ER -