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Internet of Energy and Artificial Intelligence for Sustainable Cities

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "G1: Smart Cities and Urban Management".

Deadline for manuscript submissions: closed (10 February 2023) | Viewed by 7845

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Special Issue Editors

Prof. Dr. Faycal Bensaali

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Guest Editor

Department of Electrical Engineering, College of Engineering, Qatar University, P. O. Box 2713, Doha 122104, Qatar
Interests: energy efficiency; digital image and signal processing; machine learning; embedded systems; IoT
Special Issues, Collections and Topics in MDPI journals

Dr. Yassine Himeur

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Co-Guest Editor

Department of Electrical Engineering, Qatar University, Doha, Qatar
Interests: machine learning; artificial intelligence; energy efficiency in buildings; multimedia security

Prof. Dr. Abbes Amira

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Co-Guest Editor

College of Computing and Informatics, University of Sharjah, Sharjah, United Arab Emirates
Interests: artificial intelligence; embedded systems; high-performance computing; big data and IoT
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaojun Zhai

E-Mail Website
Co-Guest Editor

School of Computer Science and Electronic Engineering, University of Essex, 5B.542, Colchester Campus, Colchester, UK
Interests: FPGAE; mbedded systems; image processing; intelligent systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Developing sustainable and green cities principally relies on adopting the five major subjects of artificial intelligence, machine learning, Internet of energy (IoE), information and communications technology (ICT), and data visualization for energy applications.

These technologies have developed considerably and are becoming increasingly integrated into energy management systems. This has been achieved from both software and hardware perspectives to develop robust intelligent energy systems with a high self-adaption capability that are able to support decision making. These highly interconnected topics are gaining popularity since their applications exceed the energy sector to traverse other disciplines (healthcare, finance, agriculture, biology, etc.). On the other side, intelligent and distributed energy management systems could significantly improve crisis readiness, e.g., in the case of COVID-19, in which they have enhanced efficiency and transparency and have made building energy infrastructures more robust.

This Special Issue calls for papers on recent trends in intelligent Internet of energy management and its applications in sustainable and smart cities. It aims to tackle the latest technologies in developing applied intelligent energy systems to improve our society.

RELEVANCE OF THE THEME OF THE SPECIAL ISSUE

The significant technological advancements which have recently been achieved have aided the development of different kinds of intelligent Internet-based energy-management systems and raised various novel challenges. Starting from the IoE and moving towards smart cities and blockchain technology, several applications have been proposed in the literature to promote sustainability and reduce wasted energy. While intelligent Internet-based technologies have been effectively deployed to address various issues in healthcare, finance, business and economics, telecommunication, education, etc., less has been done to tackle the technical challenges in applied intelligent energy-efficiency systems, which have a direct societal impact.

To that end, the proposed Special Issue focuses on highlighting the latest progress in embedding intelligent Internet systems and related solutions into the building energy sector.

AIM AND SCOPE

This Special Issue attempts to gather recent progress on applied research to develop energy management systems and promote energy-saving behaviors using the latest intelligent data analysis technologies, IoE, and knowledge-based systems. Despite the enormous scope, significant attention is devoted to these technologies from both the hardware and software perspectives. Therefore, this helps in illustrating their innovative analysis and intelligent behaviors thanks to the use of artificial intelligence, the Internet of things (IoT), big data, machine learning, decision making, ICT, and data visualization techniques to boost high-impact, energy-efficient, real-world applications (i.e., in residential, commercial and industrial environments) for improving our society. In this light, submissions tackling (but not limited to) the following areas of intelligent Internet for energy and buildings applications are highly encouraged:

Smart metering;
Energy data analytics;
Big data energy services;
Smart energy management;
Smart grid and buildings;
Energy recommendation systems;
Energy data visualization;
Deep learning for IoE;
Explainable artificial intelligence for energy saving;
Decision-making for sustainable buildings;
Internet of things and IoE;
Energy systems for smart cities and smart environments;
Behavioral change for energy efficiency;
Blockchain for energy and buildings;
Edge artificial intelligence for energy systems;
Cloud computing for energy-efficiency applications;
Security and privacy of smart energy systems;
Edge computing for IoE;
IoE distributed intelligence.

Prof. Dr. Faycal Bensaali
Dr. Yassine Himeur
Prof. Dr. Abbes Amira
Dr. Xiaojun Zhai
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

IoE
smart grid
smart energy systems
edge computing
blockchain for energy
energy recommender systems

Published Papers (4 papers)

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Research

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14 pages, 10005 KiB

Open AccessArticle

Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System

by Aya Nabil Sayed, Faycal Bensaali, Yassine Himeur and Mahdi Houchati

Energies 2023, 16(5), 2388; https://doi.org/10.3390/en16052388 - 02 Mar 2023

Cited by 5 | Viewed by 1713

Abstract

Building automation and the advancement of sustainability and safety in internal spaces benefit significantly from occupancy sensing. While particular traditional Machine Learning (ML) methods have succeeded at identifying occupancy patterns for specific datasets, achieving substantial performance in other datasets is still challenging. This paper proposes an occupancy detection method using non-intrusive ambient data and a Deep Learning (DL) model. An environmental sensing board was used to gather temperature, humidity, pressure, light level, motion, sound, and Carbon Dioxide (CO

_{2}

) data. The detection approach was deployed on an edge device to enable low-cost computing while increasing data security. The system was set up at a university office, which functioned as the primary case study testing location. We analyzed two Convolutional Neural Network (CNN) models to confirm the optimum alternative for edge deployment. A 2D-CNN technique was used for one day to identify occupancy in real-time. The model proved robust and reliable, with a 99.75% real-time prediction accuracy. Full article

(This article belongs to the Special Issue Internet of Energy and Artificial Intelligence for Sustainable Cities)

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20 pages, 4160 KiB

Open AccessArticle

Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes

by Aya Amer, Khaled Shaban and Ahmed Massoud

Energies 2022, 15(21), 8235; https://doi.org/10.3390/en15218235 - 04 Nov 2022

Cited by 1 | Viewed by 1274

Abstract

With smart grid advances, enormous amounts of data are made available, enabling the training of machine learning algorithms such as deep reinforcement learning (DRL). Recent research has utilized DRL to obtain optimal solutions for complex real-time optimization problems, including demand response (DR), where traditional methods fail to meet time and complex requirements. Although DRL has shown good performance for particular use cases, most studies do not report the impacts of various DRL settings. This paper studies the DRL performance when addressing DR in home energy management systems (HEMSs). The trade-offs of various DRL configurations and how they influence the performance of the HEMS are investigated. The main elements that affect the DRL model training are identified, including state-action pairs, reward function, and hyperparameters. Various representations of these elements are analyzed to characterize their impact. In addition, different environmental changes and scenarios are considered to analyze the model’s scalability and adaptability. The findings elucidate the adequacy of DRL to address HEMS challenges since, when appropriately configured, it successfully schedules from 73% to 98% of the appliances in different simulation scenarios and minimizes the electricity cost by 19% to 47%. Full article

(This article belongs to the Special Issue Internet of Energy and Artificial Intelligence for Sustainable Cities)

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16 pages, 1014 KiB

Open AccessArticle

An Auto-Extraction Framework for CEP Rules Based on the Two-Layer LSTM Attention Mechanism: A Case Study on City Air Pollution Forecasting

by Yuan Liu, Wangyang Yu, Cong Gao and Minsi Chen

Energies 2022, 15(16), 5892; https://doi.org/10.3390/en15165892 - 14 Aug 2022

Cited by 5 | Viewed by 1413

Abstract

Energy is at the center of human society and drives the technologies and overall human well-being. Today, artificial intelligence (AI) technologies are widely used for system modeling, prediction, control, and optimization in the energy sector. The internet of things (IoT) is the core of the third wave of the information industry revolution and AI. In the energy sector, tens of billions of IoT appliances are linked to the Internet, and these appliances generate massive amounts of data every day. Extracting useful information from the massive amount of data will be a very meaningful thing. Complex event processing (CEP) is a stream-based technique that can extract beneficial information from real-time data through pre-establishing pattern rules. The formulation of pattern rules requires strong domain expertise. Therefore, at present, the pattern rules of CEP still need to be manually formulated by domain experts. However, in the face of complex, massive amounts of IoT data, manually setting rules will be a very difficult task. To address the issue, this paper proposes a CEP rule auto-extraction framework by combining deep learning methods with data mining algorithms. The framework can automatically extract pattern rules from unlabeled air pollution data. The deep learning model we presented is a two-layer LSTM (long short-term memory) with an attention mechanism. The framework has two phases: in the first phase, the anomalous data is filtered out and labeled from the IoT data through the deep learning model we proposed, and then the pattern rules are mined from the labeled data through the decision tree data mining algorithm in the second phase. We compare other deep learning models to evaluate the feasibility of the framework. In addition, in the rule extraction stage, we use a decision tree data mining algorithm, which can achieve high accuracy. Experiments have shown that the framework we proposed can effectively extract meaningful and accurate CEP rules. The research work in this paper will help support the advancement of the sector of air pollution prediction, assist in the establishment of air pollution regulatory strategies, and further contribute to the development of a green energy structure. Full article

(This article belongs to the Special Issue Internet of Energy and Artificial Intelligence for Sustainable Cities)

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Review

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22 pages, 938 KiB

Open AccessReview

Neural Load Disaggregation: Meta-Analysis, Federated Learning and Beyond

by Hafsa Bousbiat, Yassine Himeur, Iraklis Varlamis, Faycal Bensaali and Abbes Amira

Energies 2023, 16(2), 991; https://doi.org/10.3390/en16020991 - 16 Jan 2023

Cited by 5 | Viewed by 2620

Abstract

Non-intrusive load monitoring (NILM) techniques are central techniques to achieve the energy sustainability goals through the identification of operating appliances in the residential and industrial sectors, potentially leading to increased rates of energy savings. NILM received significant attention in the last decade, reflected by the number of contributions and systematic reviews published yearly. In this regard, the current paper provides a meta-analysis summarising existing NILM reviews to identify widely acknowledged findings concerning NILM scholarship in general and neural NILM algorithms in particular. In addition, this paper emphasizes federated neural NILM, receiving increasing attention due to its ability to preserve end-users’ privacy. Typically, by combining several locally trained models, federated learning has excellent potential to train NILM models locally without communicating sensitive data with cloud servers. Thus, the second part of the current paper provides a summary of recent federated NILM frameworks with a focus on the main contributions of each framework and the achieved performance. Furthermore, we identify the non-availability of proper toolkits enabling easy experimentation with federated neural NILM as a primary barrier in the field. Thus, we extend existing toolkits with a federated component, made publicly available and conduct experiments on the REFIT energy dataset considering four different scenarios. Full article

(This article belongs to the Special Issue Internet of Energy and Artificial Intelligence for Sustainable Cities)

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