The digital transformation of the energy sector toward the Smart Grid paradigm, intelligent energy management, and distributed energy integration poses new requirements for computer science. Issues related to the automation of power grid management, multidimensional analysis of data generated in Smart Grids, and optimization of decision-making processes require urgent solutions. The article aims to analyze the use of selected artificial intelligence (AI) algorithms to support the abovementioned issues. In particular, machine learning methods, metaheuristic algorithms, and intelligent fuzzy inference systems were analyzed. Examples of the analyzed algorithms were tested in crucial domains of the energy sector. The study analyzed cybersecurity, Smart Grid management, energy saving, power loss minimization, fault diagnosis, and renewable energy sources. For each domain of the energy sector, specific engineering problems were defined, for which the use of artificial intelligence algorithms... [more]

Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission fra... [more]

Fluctuations in the critical current along the length of high-temperature superconducting (HTS) tapes manufactured in the form of coated conductors is a common manufacturing phenomenon. These fluctuations originate in the generation of weak points through the length of HTS tapes that may cause quenching later. By means of the propagation of quenches in HTS tapes, the reliability, stability, and the performance of the device and the system that contain HTS tapes could be seriously degraded. In this study, an artificial intelligence technique based on artificial neural networks (ANN) was proposed to estimate the probability of quenches in HTS tapes caused by weak points. For this purpose, six different HTS tapes were considered with different widths, total thicknesses, and thicknesses of sub-layers. Then, for each one of these tapes, different operating conditions were considered, where the operating temperature changed from 40 K to 80 K, in 1 K steps. Under each operating temperature, d... [more]

This research applies machine learning methods to build predictive models of Net Load Imbalance for the Resource Sufficiency Flexible Ramping Requirement in the Western Energy Imbalance Market. Several methods are used in this research, including Reconstructability Analysis, developed in the systems community, and more well-known methods such as Bayesian Networks, Support Vector Regression, and Neural Networks. The aims of the research are to identify predictive variables and obtain a new stand-alone model that improves prediction accuracy and reduces the INC (ability to increase generation) and DEC (ability to decrease generation) Resource Sufficiency Requirements for Western Energy Imbalance Market participants. This research accomplishes these aims. The models built in this paper identify wind forecast, sunrise/sunset and the hour of day as primary predictors of net load imbalance, among other variables, and show that the average size of the INC and DEC capacity requirements can be... [more]

Heat pipe systems have attracted increasing attention recently for application in various heat transfer-involving systems and processes. One of the obstacles in implementing heat pipes in many applications is their difficult-to-model operation due to the many parameters that affect their performance. A promising alternative to classical modeling that emerges to perform accurate modeling of heat pipe systems is artificial intelligence (AI)-based modeling. This research reviews the applications of AI techniques for the modeling and control of heat pipe systems. This work discusses the AI-based modeling of heat pipes focusing on the influence of chosen input parameters and the utilized prediction models in heat pipe applications. The article also highlights various important aspects related to the application of AI models for modeling heat pipe systems, such as the optimal AI model structure, the models overfitting under small datasets conditions, and the use of dimensionless numbers as i... [more]

An increase in consumption and inefficiency, fluctuating trends in demand and supply, and a lack of critical analytics for successful management are just some of the problems that the energy business throughout the world is currently facing. This study set out to assess the potential contributions that AI and ML technologies could make to the expansion of energy production in developing countries, where these issues are more pronounced because of the prevalence of numerous unauthorized connections to the electricity grid, where a large amount of energy is not being measured or paid for. This study primarily aims to address issues that arise due to frequent power outages and widespread lack of access to energy in a wide range of developing countries. Findings suggest that AI and ML have the potential to make major contributions to the fields of predictive turbine maintenance, energy consumption optimization, grid management, energy price prediction, and residential building energy deman... [more]

The well-known dispatcher training simulator (DTS), as a good tool to train power system dispatchers, has been widely used for over 40 years. However, with the high-speed development of the smart grid, the traditional DTSs have struggled to meet the power industry’s expectations. To enhance the effectiveness of dispatcher training, technical innovations in DTSs are becoming more and more demanding. Meanwhile, the ever-advancing artificial intelligence (AI) technology provides the basis for the design of intelligent DTSs. This paper systematically reviews the traditional DTS in terms of its origin, structure, and functions, as well as limitations in the context of the smart grid. Then, this paper summarizes the AI techniques commonly used in the field of power systems, such as expert systems, artificial neural networks, and the fuzzy set theory, and employs them to develop intelligent DTSs. Regarding a less studied aspect of DTSs, i.e., intelligent training control, we introduce the Ada... [more]

The electric power quality has become a serious concern for electric utilities and end users owing to its undesirable effects on system capabilities and performance. Harmonic levels on power systems have been pronounced to a greater extent with the continuous growth in the application of solid-state and reactive power compensatory devices. Harmonics are the key constituents that are mainly responsible for power quality deterioration. Power system harmonics need to be correctly estimated and filtered to increase power quality. This research work focuses on accurate estimation of power system harmonics with the proposed hybrid weighted least-square multi-verse optimizer (WLS−MVO) based framework. Multi-verse optimizer replicates the phenomenon of the formation of new universes as described by multi-verse theory to solve complex real-world optimization problems. The proposed WLS−MVO framework is tested and validated by estimating the harmonics present in multiple test signals with differe... [more]

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were cal... [more]

Chemical Product Engineering (CPE) is marked by numerous challenges, such as the complexity of the properties−structure−ingredients−process relationship of the different products and the necessity to discover and develop constantly and quickly new molecules and materials with tailor-made properties. In recent years, artificial intelligence (AI) and machine learning (ML) methods have gained increasing attention due to their performance in tackling particularly complex problems in various areas, such as computer vision and natural language processing. As such, they present a specific interest in addressing the complex challenges of CPE. This article provides an updated review of the state of the art regarding the implementation of ML techniques in different types of CPE problems with a particular focus on four specific domains, namely the design and discovery of new molecules and materials, the modeling of processes, the prediction of chemical reactions/retrosynthesis and the support for... [more]

Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model... [more]

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-comput... [more]

Currently, a large amount of information is presented graphically. However, visually impaired individuals do not have access to visual information. Instead, they depend on tactile illustrations—raised lines, textures, and elevated graphics that are felt through touch—to perceive geometric and various other objects in textbooks. Tactile graphics are considered an important factor for students in the science, technology, engineering, and mathematics fields seeking a quality education because teaching materials in these fields are frequently conveyed with diagrams and geometric figures. In this paper, we conducted a systematic literature review to identify the current state of research in the field of automatic tactile graphics generation. Over 250 original research papers were screened and the most appropriate studies on automatic tactile graphic generation over the last six years were classified. The reviewed studies explained numerous current solutions in static and dynamic tactile gra... [more]

Vascular dementia (VD) is a cognitive impairment typical of advanced age with vascular etiology. It results from several vascular micro-accidents involving brain vessels carrying less oxygen and nutrients than it needs. This being a degenerative disease, the diagnosis often arrives too late, when the brain tissue is already damaged. Thus, prevention is the best solution to avoid irreversible cognitive impairment in patients with specific risk factors. Using the machine learning (ML) approach, our group evaluated Mini-Mental State Examination (MMSE) changes in patients affected by Alzheimer’s disease by considering different clinical parameters. We decided to apply a similar ML scheme to VD due to the consistent data obtained from the first work, including the assessment of various ML models (LASSO, RIDGE, Elastic Net, CART, Random Forest) for the outcome prediction (i.e., the MMSE modification throughout time). MMSE at recruitment, folate, MCV, PTH, creatinine, vitamin B12, TSH, and he... [more]

The quality-control process in manufacturing must ensure the product is free of defects and performs according to the customer’s expectations. Maintaining the quality of a firm’s products at the highest level is very important for keeping an edge over the competition. To maintain and enhance the quality of their products, manufacturers invest a lot of resources in quality control and quality assurance. During the assembly line, parts will arrive at a constant interval for assembly. The quality criteria must first be met before the parts are sent to the assembly line where the parts and subparts are assembled to get the final product. Once the product has been assembled, it is again inspected and tested before it is delivered to the customer. Because manufacturers are mostly focused on visual quality inspection, there can be bottlenecks before and after assembly. The manufacturer may suffer a loss if the assembly line is slowed down by this bottleneck. To improve quality, state-of-the-a... [more]

A car side-outer is an iron mold that is applied in the design and safety of the side of a vehicle, and is subjected to a complicated and detailed molding process. The side-outer has three features that make its quality inspection difficult to automate: (1) it is large; (2) there are many objects to inspect; and (3) it must fulfil high-quality requirements. Given these characteristics, the industrial vision system for the side-outer is nearly impossible to apply, and indeed there is no reference for an automated defect-inspection system for the side-outer. Manual inspection of the side-outer worsens the quality and cost competitiveness of the metal-cutting companies. To address these problems, we propose a large-scale Object-Defect Inspection System based on Regional Convolutional Neural Network (R-CNN; RODIS) using Artificial Intelligence (AI) technology. In this paper, we introduce the framework, including the hardware composition and the inspection method of RODIS. We mainly focus o... [more]

Bangladesh is blessed with abundant solar resources. Solar power is considered the most desirable energy source to mitigate the high energy demand of this densely populated country. Although various articles deal with solar energy applications in Bangladesh, no detailed review can be found in the literature. Therefore, in this study, we report on the current scenario of renewable energy in Bangladesh and the most significant potential of solar energy’s contribution among multiple renewable energy resources in mitigating energy demand. One main objective of this analysis was to outline the overall view of solar energy applications in Bangladesh to date, as well as the ongoing development of such projects. The technical and theoretical solar energy potential and the technologies available to harvest solar energy were also investigated. A detailed techno-economic design of solar power applications for the garment industry was also simulated to determine the potential of solar energy for t... [more]

The growing success of smart grids (SGs) is driving increased interest in load forecasting (LF) as accurate predictions of energy demand are crucial for ensuring the reliability, stability, and efficiency of SGs. LF techniques aid SGs in making decisions related to power operation and planning upgrades, and can help provide efficient and reliable power services at fair prices. Advances in artificial intelligence (AI), specifically in machine learning (ML) and deep learning (DL), have also played a significant role in improving the precision of demand forecasting. It is important to evaluate different LF techniques to identify the most accurate and appropriate one for use in SGs. This paper conducts a systematic review of state-of-the-art forecasting techniques, including traditional techniques, clustering-based techniques, AI-based techniques, and time series-based techniques, and provides an analysis of their performance and results. The aim of this paper is to determine which LF tech... [more]

Achieving clean energy goals will require significant advances in regard to addressing the computational needs for next-generation renewable-dominated power grids. One critical obstacle that lies in the way of transitioning today’s power grid to a renewable-dominated power grid is the lack of a faster-than-real-time stability assessment technology for operating a fast-changing power grid. This paper proposes an artificial intelligence (AI) -based method that predicts the system’s stability margin information (e.g., the frequency nadir in the frequency stability assessment and the critical clearing time (CCT) value in the transient stability assessment) directly from the system operating conditions without performing the conventional time-consuming time-domain simulations over detailed dynamic models. Since the AI method shifts the majority of the computational burden to offline training, the online evaluation is extremely fast. This paper has tested the AI-based stability assessment me... [more]

: Wind speed and solar radiation are two of the most well-known and widely used renewable energy sources worldwide. Coal, natural gas, and petroleum are examples of fossil fuels that are not replenished and are thus non-renewable energy sources due to their high carbon content and the methods by which they are generated. To predict energy production of renewable sources, researchers use energy forecasting techniques based on the recent advances in machine learning approaches. Numerous prediction methods have significant drawbacks, including high computational complexity and inability to generalize for various types of sources of renewable energy sources. Methodology: In this paper, we proposed a novel approach capable of generalizing the prediction accuracy for both wind speed and solar radiation forecasting data. The proposed approach is based on a new optimization algorithm and a new stacked ensemble model. The new optimization algorithm is a hybrid of Al-Biruni Earth Radius (BER) an... [more]

The current study aims to investigate and compare the effects of waste plastic oil blended with n-butanol on the characteristics of diesel engines and exhaust gas emissions. Waste plastic oil produced by the pyrolysis process was blended with n-butanol at 5%, 10%, and 15% by volume. Experiments were conducted on a four-stroke, four-cylinder, water-cooled, direct injection diesel engine with a variation of five engine loads, while the engine’s speed was fixed at 2500 rpm. The experimental results showed that the main hydrocarbons present in WPO were within the range of diesel fuel (C13−C18, approximately 74.39%), while its specific gravity and flash point were out of the limit prescribed by the diesel fuel specification. The addition of n-butanol to WPO was found to reduce the engine’s thermal efficiency and increase HC and CO emissions, especially when the engine operated at low-load conditions. In order to find the suitable ratio of n-butanol blends when the engine operated at the tes... [more]

The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely colla... [more]

Intelligent inverters have the capability to interact with the grid and supply supplemental services. Solar inverters designed for the future will have the ability to self-govern, self-adapt, self-secure, and self-heal themselves. Based on the available capacity, the ancillary service rendered by a solar inverter is referred to as moonlighting. Inverters that communicate with the grid but are autonomous can switch between the grid forming mode and the grid following control mode as well. Self-adaptive grid-interactive inverters can keep their dynamics stable with the assistance of adaptive controllers. Inverters that interact with the grid are also capable of self-adaptation Grid-interactive inverters may be vulnerable to hacking in situations in which they are forced to rely on their own self-security to determine whether malicious setpoints have been entered. To restate, an inverter can be referred to as a “smart inverter” when it is self-tolerant, self-healing, and provides ancillar... [more]

There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system′s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key ro... [more]

The competitiveness of companies in emerging countries implies many European countries must transform their production systems to be more efficient. Indeed, the new context created by the COVID-19 pandemic increases the necessity of digital transformation and focuses attention on its limited uptake by manufacturing companies. In France, the Industry 4.0 concepts are already implemented in large companies. Despite the demonstration and validation of their benefits, SMEs are reluctant to move towards implementation. This problem of SME performance improvement increases with the current geopolitical situation in Europe (raw materials and gasoil cost). It is thus urgent and paramount to find a better solution for encouraging SMEs in their transformation. Taking note of the brakes on uptake of Industry 4.0 concepts in SMEs, the objectives of this paper are to find levers to accelerate implementation of Industry 4.0 concepts in SMEs, through the development and the deployment of a sustainabl... [more]