Topic Editors

Prof. Dr. Antonio Cano-Ortega
Electrical Engineering Department, University of Jaen, Campus Las Lagunillas, s/n, 23071, Jaen, Spain
Prof. Dr. Francisco Sánchez-Sutil
Electrical Engineering Department, University of Jaen, Campus Las Lagunillas s/n, 23071 Jaen, Spain
Dr. Aurora Gil-de-Castro
Department of Electronic and Computer Engineering, University of Cordoba, Campus de Rabanales, Edificio Leonardo Da Vinci, E-14071 Córdoba, Spain

IoT for Energy Management Systems and Smart Cities

Abstract submission deadline
20 December 2022
Manuscript submission deadline
20 March 2023
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2686

Topic Information

Dear Colleagues,

Smart cities represent a great advance in terms of sustainability, energy efficiency, and being able to respond to the needs of enterprises, institutions, and inhabitants.

In this sense, smart grids contribute to the development of smart cities in the field of electrical energy, including concepts such as renewable energies, distributed generation, energy efficiency, and smart homes and automation.

In order to be able to implement all the functionalities of smart grids, it is necessary to have real-time information on the different installations. In this sense, IoT plays a fundamental role in developing smart grids.

Cloud computing, which integrates the data obtained with smart electrical meters, smart electrical power analyzers, and other intelligent metering devices, contributes to the availability of the measured data in real time and provides intelligence to existing electrical networks.

Wireless communication networks, especially LPWAN, allow the construction of devices with low energy consumption and high operating autonomy, which can be installed in different locations even with difficult access.

The massive implantation of the electric vehicle implies the construction of charging stations. These stations must use renewable energy sources that contribute to saving fossil fuels, reducing CO2, and increasing the sustainability of electric mobility.

Hybrid storage systems, together with renewable energies, constitute new development systems, in which it is necessary to measure electrical variables and control the operation of the system.

Prof. Dr. Antonio Cano-Ortega
Prof. Dr. Francisco Sánchez-Sutil
Dr. Aurora Gil-de-Castro
Topic Editors

Keywords

  • cloud computing
  • smart electric meters
  • smart power analyzers
  • smart grids for smart cities
  • smart home and automation
  • monitoring and control renewable energy: photovoltaic solar energy, wind energy, hydroelectric energy, biomass energy, and other renewable energy resources
  • public lighting system
  • distributed generation
  • hybrid electric energy storage systems (batteries, supercapacitors, fuel cells, etc.)
  • electric vehicle charging stations using renewable energy
  • wireless technologies: Wi-Fi, LoRa, ZigBee, Bluetooth, NB-IoT, etc.

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies
3.252 5.0 2008 16.2 Days 2200 CHF Submit
Sensors
sensors
3.847 6.4 2001 16.2 Days 2400 CHF Submit
Electronics
electronics
2.690 3.7 2012 16.6 Days 2000 CHF Submit
Smart Cities
smartcities
- 5.5 2018 14.9 Days 1200 CHF Submit
IoT
IoT
- - 2020 14 Days 1000 CHF Submit

Published Papers (5 papers)

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Article
Federated Deep Reinforcement Learning-Based Task Offloading and Resource Allocation for Smart Cities in a Mobile Edge Network
Sensors 2022, 22(13), 4738; https://doi.org/10.3390/s22134738 - 23 Jun 2022
Abstract
Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue [...] Read more.
Mobile edge computing (MEC) has become an indispensable part of the era of the intelligent manufacturing industry 4.0. In the smart city, computation-intensive tasks can be offloaded to the MEC server or the central cloud server for execution. However, the privacy disclosure issue may arise when the raw data is migrated to other MEC servers or the central cloud server. Since federated learning has the characteristics of protecting the privacy and improving training performance, it is introduced to solve the issue. In this article, we formulate the joint optimization problem of task offloading and resource allocation to minimize the energy consumption of all Internet of Things (IoT) devices subject to delay threshold and limited resources. A two-timescale federated deep reinforcement learning algorithm based on Deep Deterministic Policy Gradient (DDPG) framework (FL-DDPG) is proposed. Simulation results show that the proposed algorithm can greatly reduce the energy consumption of all IoT devices. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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Article
Research on Energy Saving and Environmental Protection Management Evaluation of Listed Companies in Energy Industry Based on Portfolio Weight Cloud Model
Energies 2022, 15(12), 4311; https://doi.org/10.3390/en15124311 - 13 Jun 2022
Abstract
Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry [...] Read more.
Under the background of the “carbon peaking and carbon neutrality” strategy, energy saving and environmental protection (ESEP) management has become one of the most important projects of enterprises. In order to evaluate the ESEP management level of listed companies in the energy industry comprehensively, this study puts forward the evaluation framework of “governance framework-implementation process-governance effectiveness” for ESEP management level. Based on the comprehensive collection and collating of related information reports (e.g., sustainable development reports) of listed energy companies from 2009 to 2018, the ESEP information was extracted, and the portfolio weight cloud model was used to evaluate the ESEP management status of listed energy companies in China. It is of great theoretical innovation and practical significance to promote the evolution of the economy from “green development” to “dark green development”. The results show that: (1) the number of SHEE information released by listed companies in the energy industry shows a steady increasing trend, but the release rate is low, and there are differentiation characteristics in different industries. (2) The ESEP management level of most listed companies in the energy industry is still at the low level, and only 17.19% (S = 65) of the sample companies are at the level of “IV level-acceptable” and “V level-claimable”. (3) In terms of governance framework-implementation process-governance effectiveness, B1-governance framework (Ex = 3.4451) and B2-implementation process (Ex = 2.9480) are relatively high, but B3-governance effectiveness (Ex = 2.0852) and B4-public welfare (Ex = 2.0556) are relatively low. The expectation of most ESEP evaluation indexes fluctuates between “III level-transition level” and “II Level-improvement level”. Finally, some suggestions are put forward to improve ESEP management levels. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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Article
Deep Federated Adaptation: An Adaptative Residential Load Forecasting Approach with Federated Learning
Sensors 2022, 22(9), 3264; https://doi.org/10.3390/s22093264 - 24 Apr 2022
Abstract
Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns raised by supervision departments and users limit the data for sharing. Meanwhile, the limited [...] Read more.
Residential-level short-term load forecasting (STLF) is significant for power system operation. Data-driven forecasting models, especially machine-learning-based models, are sensitive to the amount of data. However, privacy and security concerns raised by supervision departments and users limit the data for sharing. Meanwhile, the limited data from the newly built houses are not sufficient to support building a powerful model. Another problem is that the data from different houses are in a non-identical and independent distribution (non-IID), which makes the general model fail in predicting accurate load for the specific house. Even though we can build a model corresponding to each house, it costs a large computation time. We first propose a federated transfer learning approach applied in STLF, deep federated adaptation (DFA), to deal with the aforementioned problems. This approach adopts the federated learning architecture to train a global model without undermining privacy, and then the model leverage multiple kernel variant of maximum mean discrepancies (MK-MMD) to fine-tune the global model, which makes the model adapted to the specific house’s prediction task. Experimental results on the real residential datasets show that DFA has the best forecasting performance compared with other baseline models and the federated architecture of DFA has a remarkable superiority in computation time. The framework of DFA is extended with alternative transfer learning methods and all of them achieve good performances on STLF. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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Article
EggBlock: Design and Implementation of Solar Energy Generation and Trading Platform in Edge-Based IoT Systems with Blockchain
Sensors 2022, 22(6), 2410; https://doi.org/10.3390/s22062410 - 21 Mar 2022
Cited by 1
Abstract
In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can [...] Read more.
In this paper, to balance power supplement from the solar energy’s intermittent and unpredictable generation, we design a solar energy generation and trading platform (EggBlock) using Internet of Things (IoT) systems and blockchain technique. Without a centralized broker, the proposed EggBlock platform can promote energy trading between users equipped with solar panels, and balance demand and generation. By applying the second price sealed-bid auction, which is one of the suitable pricing mechanisms in the blockchain technique, it is possible to derive truthful bidding of market participants according to their utility function and induce the proceed transaction. Furthermore, for efficient generation of solar energy, EggBlock proposes a Q-learning-based dynamic panel control mechanism. Specifically, we set the instantaneous direction of the solar panel and the amount of power generation as the state and reward, respectively. The angle of the panel to be moved becomes an action at the next time step. Then, we continuously update the Q-table using transfer learning, which can cope with recent changes in the surrounding environment or weather. We implement the proposed EggBlock platform using Ethereum’s smart contract for reliable transactions. At the end of the paper, measurement-based experiments show that the proposed EggBlock achieves reliable and transparent energy trading on the blockchain and converges to the optimal direction with short iterations. Finally, the results of the study show that an average energy generation gain of 35% is obtained. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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Article
LPSRS: Low-Power Multi-Hop Synchronization Based on Reference Node Scheduling for Internet of Things
Energies 2022, 15(6), 2289; https://doi.org/10.3390/en15062289 - 21 Mar 2022
Abstract
Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in [...] Read more.
Time synchronization is one of the most fundamental problems on the internet of things (IoT). The IoT requires low power and an efficient synchronization protocol to minimize power consumption and conserve battery power. This paper introduces an efficient method for time synchronization in the IoT called low-power multi-hop synchronization (LPSRS). It employs a reference node scheduling mechanism to avoid packet collisions and minimize the communication overhead, which has a big impact on power consumption. The performance of LPSRS has been evaluated and compared to previous synchronization methods, HRTS and R-Sync, via real hardware networks and simulations. The results show that LPSRS achieves a better performance in terms of power consumption (transmitted messages). In particular, for a large network of 450 nodes, LPSRS reduced the total number of transmitted messages by 53% and 49% compared to HRTS and R-Sync, respectively. Full article
(This article belongs to the Topic IoT for Energy Management Systems and Smart Cities)
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