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Cited by 59IoT and Edge/Fog ComputingSmart Grid Energy ManagementEnergy Efficiency in Computing

The DOI matches its Crossref record; 58 of the 60 references it cites were found in a registry or an index, and 2 are not checked yet.

A Deep Learning-Driven Self-Conscious Distributed Cyber-Physical System for Renewable Energy Communities

Open access
Cicceri, Giovanni, Tricomi, Giuseppe, D’Agati, Luca, Longo, Francesco, Merlino, Giovanni, Puliafito, Antonio·2023·Sensors
DOI 10.3390/s23094549PMC PMC10181772
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Abstract

The Internet of Things (IoT) is transforming various domains, including smart energy management, by enabling the integration of complex digital and physical components in distributed cyber-physical systems (DCPSs). The design of DCPSs has so far been focused on performance-related, non-functional requirements. However, with the growing power consumption and computation expenses, sustainability is becoming an important aspect to consider. This has led to the concept of energy-aware DCPSs, which integrate conventional non-functional requirements with additional attributes for sustainability, such as energy consumption. This research activity aimed to investigate and develop energy-aware architectural models and edge/cloud computing technologies to design next-generation, AI-enabled (and, specifically, deep-learning-enhanced), self-conscious IoT-extended DCPSs. Our key contributions include energy-aware edge-to-cloud architectural models and technologies, the orchestration of a (possibly federated) edge-to-cloud infrastructure, abstractions and unified models for distributed heterogeneous virtualized resources, innovative machine learning algorithms for the dynamic reallocation and reconfiguration of energy resources, and the management of energy communities. The proposed solution was validated through case studies on optimizing renewable energy communities (RECs), or energy-aware DCPSs, which are particularly challenging due to their unique requirements and constraints; in more detail, in this work, we aim to define the optimal implementation of an energy-aware DCPS. Moreover, smart grids play a crucial role in developing energy-aware DCPSs, providing a flexible and efficient power system integrating renewable energy sources, microgrids, and other distributed energy resources. The proposed energy-aware DCPSs contribute to the development of smart grids by providing a sustainable, self-consistent, and efficient way to manage energy distribution and consumption. The performance demonstrates our approach's effectiveness for consumption and production (based on RMSE and MAE metrics). Our research supports the transition towards a more sustainable future, where communities adopting REC principles become key players in the energy landscape.

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Crossref · 10.3390/s23094549

Cites

58 of 60 references verified0 citation errors · 0 not checkable

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Figures

Figure 1 IoTronic’s architectural schema.
Figure 1 IoTronic’s architectural schema.CC BY · PubMed Central
Figure 2 Lightning-Rod’s architectural schema.
Figure 2 Lightning-Rod’s architectural schema.CC BY · PubMed Central
Figure 3 Example of a software-defined CPS acting as a self-conscious environment [50].
Figure 3 Example of a software-defined CPS acting as a self-conscious environment [50].CC BY · PubMed Central
Figure 4 REC architecture. ECEM stands for Energy Consumption Estimation Module, EPREM stands for Energy Production Estimation Module, and TEANS stands for Threshold Evaluator and Notification System.
Figure 4 REC architecture. ECEM stands for Energy Consumption Estimation Module, EPREM stands for Energy Production Estimation Module, and TEANS stands for Threshold Evaluator and Notification System.CC BY · PubMed Central
Figure 5 View of energy estimation value for consumption and production.
Figure 5 View of energy estimation value for consumption and production.CC BY · PubMed Central
Figure 6 Technologies relationship chart.
Figure 6 Technologies relationship chart.CC BY · PubMed Central
Figure 7 Gap seasonality of a real estate unit by hour, month, and week of the year.
Figure 7 Gap seasonality of a real estate unit by hour, month, and week of the year.CC BY · PubMed Central
Figure 8 AC of a computed panel line.
Figure 8 AC of a computed panel line.CC BY · PubMed Central
Figure 9 DC and efficiency training and validation loss curves of the LSTM model. (a) DC training and validation loss for the line of panels 1; (b) DC training and validation loss for the line of panels 2; (c) DC training and validation loss for the line of panels 3; (d) Efficiency training and validation loss for the line of panels 1; (e) Efficiency training and validation loss for the line of panels 2; (f) Efficiency training and validation loss for the line of panels 3.
Figure 9 DC and efficiency training and validation loss curves of the LSTM model. (a) DC training and validation loss for the line of panels 1; (b) DC training and validation loss for the line of panels 2; (c) DC training and validation loss for the line of panels 3; (d) Efficiency training and validation loss for the line of panels 1; (e) Efficiency training and validation loss for the line of panels 2; (f) Efficiency training and validation loss for the line of panels 3.CC BY · PubMed Central
Figure 10 GAP predictions after dataset resampling of a real estate unit from 17 June at 17:00.
Figure 10 GAP predictions after dataset resampling of a real estate unit from 17 June at 17:00.CC BY · PubMed Central
Figure 11 LSTM prediction graphs. (a) DC predicted by the model for the line of panels 1; (b) DC predicted by the model for the line of panels 2; (c) DC predicted by the model for the line of panels 3; (d) Efficiency predicted by the model for the line of panels 1; (e) Efficiency predicted by the model for the line of panels 2; (f) Efficiency predicted by the model for the line of panels 3.
Figure 11 LSTM prediction graphs. (a) DC predicted by the model for the line of panels 1; (b) DC predicted by the model for the line of panels 2; (c) DC predicted by the model for the line of panels 3; (d) Efficiency predicted by the model for the line of panels 1; (e) Efficiency predicted by the model for the line of panels 2; (f) Efficiency predicted by the model for the line of panels 3.CC BY · PubMed Central

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Citations

Cites

Show all 58 references

Research works printed but not linked: 2

  1. Faustine, A. (2020, January 6–12). Leveraging Machine Learning for Sustainable and Self-sufficient Energy Communities. Proceedings of the NeurIPS 2020, Online.
  2. Beaudin, M., and Zareipour, H. (2017). Energy Solutions to Combat Global Warming, Springer. Lecture Notes in Energy.

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Metadata

Authors
Cicceri, G., Tricomi, G., D’Agati, L., Longo, F., Merlino, G., & Puliafito, A.
Affiliations
University of Messina (IT)
Corresponding author
  • Tricomi, Giuseppe
  • Puliafito, Antonio
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2023-05-07
Volume
23
Issue
9
Pages
4549
ISSN
1424-8220 (ISSN-L)
DOI
10.3390/s23094549
PMC
PMC10181772
OpenAlex
W4376630019
Cited by
58
References
58
Citations by year
2026: 17 · 2025: 26 · 2024: 14 · 2023: 1
FWCI
6.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Sensors (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • IRIS UniPA (University of Palermo) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
HORIZON EUROPE Framework Programme (101079774)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
  • Physical Sciences › Computer Science › Hardware and Architecture › Energy Efficiency in Computing
Keywords
distributed cyber-physical systems, energy-aware systems, renewable energy communities, smart grid, microgrids, Internet of Things, sustainability, energy optimization, resource reconfiguration

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