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Going Smart: Integrating Artificial Neural Network in the Energy Domain

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: closed (1 January 2024) | Viewed by 3763

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Prof. Dr. Javier M. Aguiar Pérez

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Department of Signal Theory and Communications and Telematics Engineering, University of Valladolid, 47011 Valladolid, Spain
Interests: smart grids; green communications; artificial intelligence; machine learning; deep learning; big data; Internet of Things
Special Issues, Collections and Topics in MDPI journals

Dr. María Á. Pérez Juárez

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Dear Colleagues,

Smart grids are playing an increasingly important role in the context of so-called smart environments. These grids incorporate distributed intelligence to the traditional electricity distribution networks, enabling them to extract users’ consumption patterns and, therefore, to predict energy consumption, leading to a balance between energy supply and demand, and contributing to efficient energy management and important energy savings.

On the other side, artificial intelligence refers to the intelligence of machines. Contrary to humans, machines can identify patterns within large amounts of data (big data), with limited resources and/or time. Moreover, the computational capacity of machines is not decreased with time and/or fatigue. In the energy domain, the data are collected by the distributed intelligent elements of smart grids in the context of the Internet of Things paradigm. Possible types of learning methods used by artificial intelligence include machine learning and deep learning. More specifically, practitioners are already using deep learning models in the energy domain, including convolutional neural networks (CNNs), recurrent neural networks (RNN), long short-term memory (LSTM), deep Q-networks (DQNs) and conditional restricted Boltzmann machine (CRBM), however, it is necessary to constantly support and disseminate new developments in this field.

It is also remarkable that a significant amount of the energy worldwide is consumed by technological devices and infrastructures, which includes the intelligence of smart grids. Therefore, it is critical to develop sustainable solutions with low-cost deployments, that are able to minimize energy consumption, as well as the associated carbon footprint.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

Artificial intelligence applied to the energy system
Big data applied to the energy system
Carbon footprint
Deep learning applied to the energy system
Energy consumption
Energy demand
Energy efficiency
Energy forecasting
Green communications
Internet of Things applied to the energy system
Machine learning applied to the energy system
Microgrids
Renewable energies
Smart buildings
Smart environments
Smart grid

We look forward to receiving your contributions.

Prof. Dr. Javier M. Aguiar Pérez
Prof. Dr. María Á. Pérez Juárez
Guest Editors

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Keywords

artificial intelligence
big data
deep learning
energy consumption
energy efficiency
energy forecasting
green communications
internet of things
machine learning
smart grid

Published Papers (3 papers)

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Research

22 pages, 317 KiB

Open AccessArticle

Incremental Green Investment Rule Induction Using Intelligent Rough Sets from an Energy Perspective

by Chun-Che Huang, Wen-Yau Liang, Horng-Fu Chuang, Tzu-Liang (Bill) Tseng and Yi-Chun Shen

Sustainability 2024, 16(9), 3655; https://0-doi-org.brum.beds.ac.uk/10.3390/su16093655 - 26 Apr 2024

Viewed by 329

Abstract

The United Nations Sustainable Development Goals (SDGs) agenda has stated the importance of green investment. Energy-related green investment involves intricate economic behavior and ecological objectives. Green investment definitely requires agile decisions, e.g., rule-based decisions, to respond to changes outside the country. The identification of significant rules with numerous result features and the assurance of the stability and robustness of the rules in decision-making are crucial for green energy investment. The rough set (RS) methodology works well for processing qualitative data that are difficult to examine with traditional statistical methods in order to induce decision rules. The RS methodology starts with the analysis of the limits of discernibility of a subset of objects belonging to the domain to induce rules. However, traditional RS methods cannot incrementally generate rules with outcome features when new objects are added, which frequently occurs in green energy investment with the inclusion of big data. In this paper, an intelligent RS approach is proposed. This approach effectively identifies the rules that either stay the same or are altered based on four classified cases after a new object is introduced; it is novel because it can deal with a complicated investment environment by imposing multiple outcome features, specifically when it is required to flexibly extract new decision rules via adding new data sets. Full article

(This article belongs to the Special Issue Going Smart: Integrating Artificial Neural Network in the Energy Domain)

17 pages, 7689 KiB

Open AccessArticle

Development of Hybrid Energy Storage System Testbed with Instantaneous Discharge Controller for Shunt Active Filter Application

by Sreelekshmi R. S, Manjula G. Nair, Vyshak K., Sarika Khushalani Solanki and Tripta Thakur

Sustainability 2023, 15(14), 11247; https://0-doi-org.brum.beds.ac.uk/10.3390/su151411247 - 19 Jul 2023

Cited by 1 | Viewed by 881

Abstract

The high penetration of renewable energy sources has necessitated the use of more energy-storage devices in Smartgrids. The proposed work addresses the development and implementation of an Instantaneous Discharge Controller (IDC) for a hybrid energy storage system. The discharge control algorithm manages the discharge of the battery and supercapacitor and protects the battery from transient currents. Hybrid energy storage systems (HESSs) are well known for providing ideal attributes such as high-power density and high-energy density for many application areas, including electric vehicles and renewable energy-supported microgrids. However, the application of HESSs for supporting shunt active filters and protecting low power density storage systems from fast variations in load has not been proposed yet. In this context, a hybrid energy storage system (HESS) is proposed here to eliminate harmonics and to support the grid by providing real and reactive power supervened by varying load conditions. This paper proffers an innovative controller for a shunt active filter unified with an HESS to effectively manage storage devices for a microgrid connected to a grid. Full article

(This article belongs to the Special Issue Going Smart: Integrating Artificial Neural Network in the Energy Domain)

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21 pages, 5170 KiB

Open AccessArticle

Enhancing Power Grid Resilience through Real-Time Fault Detection and Remediation Using Advanced Hybrid Machine Learning Models

by Fahad M. Almasoudi

Sustainability 2023, 15(10), 8348; https://0-doi-org.brum.beds.ac.uk/10.3390/su15108348 - 21 May 2023

Cited by 5 | Viewed by 2089

Abstract

Ensuring a reliable and uninterrupted supply of electricity is crucial for sustaining modern and advanced societies. Traditionally, power systems analysis was mostly dependent on formal commercial software, mathematical models produced via a mix of data analysis, control theory, and statistical methods. As power grids continue to grow and the need for more efficient and sustainable energy systems arises, attention has shifted towards incorporating artificial intelligence (AI) into traditional power grid systems, making their upgrade imperative. AI-based prediction and forecasting techniques are now being utilized to improve power production, transmission, and distribution to industrial and residential consumers. This paradigm shift is driven by the development of new methods and technologies. These technologies enable faster and more accurate fault prediction and detection, leading to quicker and more effective fault removal. Therefore, incorporating AI in modern power grids is critical for ensuring their resilience, efficiency, and sustainability, ultimately contributing to a cleaner and greener energy future. This paper focuses on integrating artificial intelligence (AI) in modern power generation grids, particularly in the fourth industrial revolution (4IR) context. With the increasing complexity and demand for more efficient and reliable power systems, AI has emerged as a possible approach to solve these difficulties. For this purpose, real-time data are collected from the user side, and internal and external grid faults occurred during a time period of three years. Specifically, this research delves into using state-of-the-art machine learning hybrid models at end-user locations for fault prediction and detection in electricity grids. In this study, hybrid models with convolution neural networks (CNN) have been developed, such as CNN-RNN, CNN-GRU, and CNN-LSTM. These approaches are used to explore how these models can automatically identify and diagnose faults in real-time, leading to faster and more effective fault detection and removal with minimum losses. By leveraging AI technology, modern power grids can become more resilient, efficient, and sustainable, ultimately contributing to a cleaner and greener energy future. Full article

(This article belongs to the Special Issue Going Smart: Integrating Artificial Neural Network in the Energy Domain)

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