Records with Keyword: Artificial Intelligence
Showing records 51 to 75 of 209. [First] Page: 1 2 3 4 5 6 7 Last
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Jenny Cifuentes, Geovanny Marulanda, Antonio Bello, Javier Reneses
March 31, 2023 (v1)
Keywords: air temperature forecasting, Artificial Intelligence, Machine Learning, neural networks, support vector machines
Efforts to understand the influence of historical climate change, at global and regional levels, have been increasing over the past decade. In particular, the estimates of air temperatures have been considered as a key factor in climate impact studies on agricultural, ecological, environmental, and industrial sectors. Accurate temperature prediction helps to safeguard life and property, playing an important role in planning activities for the government, industry, and the public. The primary aim of this study is to review the different machine learning strategies for temperature forecasting, available in the literature, presenting their advantages and disadvantages and identifying research gaps. This survey shows that Machine Learning techniques can help to accurately predict temperatures based on a set of input features, which can include the previous values of temperature, relative humidity, solar radiation, rain and wind speed measurements, among others. The review reveals that Deep... [more]
A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems
Varaha Satra Bharath Kurukuru, Ahteshamul Haque, Mohammed Ali Khan, Subham Sahoo, Azra Malik, Frede Blaabjerg
March 29, 2023 (v1)
Keywords: Artificial Intelligence, condition monitoring, irradiance forecasting, optimal sizing, photovoltaic systems, reliability, transition control
The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms.
Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review
Maen Z. Kreishan, Ahmed F. Zobaa
March 29, 2023 (v1)
Keywords: Artificial Intelligence, droop control, generation and load uncertainties, islanded microgrid, multi-objective optimization, optimal allocation and operation, Renewable and Sustainable Energy
This review paper provides a critical interpretation and analysis of almost 150 dedicated optimization research papers in the field of droop-controlled islanded microgrids. The significance of optimal microgrid allocation and operation studies comes from their importance for further deployment of renewable energy, reliable and stable autonomous operation on a larger scale, and the electrification of rural and isolated communities. Additionally, a comprehensive overview of islanded microgrids in terms of structure, type, and hierarchical control strategy was presented. Furthermore, a larger emphasis was given to the main optimization problems faced by droop-controlled islanded microgrids such as allocation, scheduling and dispatch, reconfiguration, control, and energy management systems. The main outcome of this review in relation to optimization problem components is the classification of objective functions, constraints, and decision variables into 10, 9 and 6 distinctive categories,... [more]
AI and Text-Mining Applications for Analyzing Contractor’s Risk in Invitation to Bid (ITB) and Contracts for Engineering Procurement and Construction (EPC) Projects
Su Jin Choi, So Won Choi, Jong Hyun Kim, Eul-Bum Lee
March 28, 2023 (v1)
Keywords: Artificial Intelligence, engineering-procurement-construction (EPC), information retrieval, invitation-to-bid (ITB) document, Machine Learning, named-entity recognition (NER), natural language processing (NLP), phrasematcher, Python, spaCy, text mining
Contractors responsible for the whole execution of engineering, procurement, and construction (EPC) projects are exposed to multiple risks due to various unbalanced contracting methods such as lump-sum turn-key and low-bid selection. Although systematic risk management approaches are required to prevent unexpected damage to the EPC contractors in practice, there were no comprehensive digital toolboxes for identifying and managing risk provisions for ITB and contract documents. This study describes two core modules, Critical Risk Check (CRC) and Term Frequency Analysis (TFA), developed as a digital EPC contract risk analysis tool for contractors, using artificial intelligence and text-mining techniques. The CRC module automatically extracts risk-involved clauses in the EPC ITB and contracts by the phrase-matcher technique. A machine learning model was built in the TFA module for contractual risk extraction by using the named-entity recognition (NER) method. The risk-involved clauses col... [more]
Big Data Value Chain: Multiple Perspectives for the Built Environment
Gema Hernández-Moral, Sofía Mulero-Palencia, Víctor Iván Serna-González, Carla Rodríguez-Alonso, Roberto Sanz-Jimeno, Vangelis Marinakis, Nikos Dimitropoulos, Zoi Mylona, Daniele Antonucci, Haris Doukas
March 28, 2023 (v1)
Subject: Environment
Keywords: analytics, Artificial Intelligence, Big Data, building stock, Machine Learning
Current climate change threats and increasing CO2 emissions, especially from the building stock, represent a context where action is required. It is necessary to provide efficient manners to manage energy demand in buildings and contribute to a decarbonised future. By combining new technologies, such as artificial intelligence, Internet of things, blockchain, and the exploitation of big data towards solving real life problems, the way could be paved towards smart and energy-aware buildings. In this context, the aim of this paper is to present a critical review and an in-detail definition of the big data value chain for the built environment in Europe, covering multiple needs and perspectives: “policy”, “technology” and “business”, in order to explore the main challenges and opportunities in this area.
Machine Learning in Solar Plants Inspection Automation
Jacek Starzyński, Paweł Zawadzki, Dariusz Harańczyk
March 28, 2023 (v1)
Keywords: Artificial Intelligence, photovoltaic modules inspection, renewable sources, solar energy
The emergence of large photovoltaic farms poses a new challenge for quick and economic diagnostics of such installations. This article presents this issue starting from a quantitative analysis of the impact of panel defects, faulty installation, and lack of farm maintenance on electricity production. We propose a low-cost and efficient method for photovoltaic (PV) plant quality surveillance that combines technologies such as an unmanned aerial vehicle (UAV), thermal imaging, and machine learning so that systematic inspection of a PV farm can be performed frequently. Most emphasis is placed on using deep neural networks to analyze thermographic images. We show how the use of the YOLO network makes it possible to develop a tool that performs the analysis of the image material already during the flyby.
Power Transmission Lines: Worldwide Research Trends
Francisca Alcayde-García, Esther Salmerón-Manzano, Miguel A. Montero, Alfredo Alcayde, Francisco Manzano-Agugliaro
March 28, 2023 (v1)
Keywords: arc flash, Artificial Intelligence, fault location, half-wave, leakage current, line inspection, lines design, magnetic fields, natural disasters, pattern recognition
The importance of the quality and continuity of electricity supply is increasingly evident given the dependence of the world economy on its daily and instantaneous operation. In turn, the network is made up of power transmission lines. This study has been carried out based on the Scopus database, where all the publications, over 5000 documents, related to the topic of the power transmission lines have been analyzed up to the year 2022. This manuscript aims to highlight the main global research trends in power transmission lines and to detect which are the emerging areas. This manuscript cover three main aspects: First, the main scientific categories of these publications and their temporal trends. Second, the countries and affiliations that contribute to the research and their main research topics. Third, identification of the main trends in the field using the detection of scientific communities by means of the clustering method. The three main scientific categories found were Enginee... [more]
SMART Computational Solutions for the Optimization of Selected Technology Processes as an Innovation and Progress in Improving Energy Efficiency of Smart Cities—A Case Study
Krzysztof Gaska, Agnieszka Generowicz
March 27, 2023 (v1)
Keywords: Artificial Intelligence, control systems, energy economics, energy efficiency of technological processes, fuel from wastes, smart cities, smart computational solution, wastewater treatment system
The paper presents advanced computational solutions for selected sectors in the context of the optimization of technology processes as an innovation and progress in improving energy efficiency of smart cities. The main emphasis was placed on the sectors of critical urban infrastructure, including in particular the use of algorithmic models based on artificial intelligence implemented in supervisory control systems (SCADA-type, including Virtual SCADA) of technological processes involving the sewage treatment systems (including in particular wastewater treatment systems) and waste management systems. The novelty of the presented solution involves the use of predictive diagnostic tools, based on multi-threaded polymorphic models supporting decision making processes during the control of a complex technological process and objects of distributed network systems (smart water grid, smart sewage system, smart waste management system) and solving problems of optimal control for smart dynamic... [more]
An Efficient Boosted C5.0 Decision-Tree-Based Classification Approach for Detecting Non-Technical Losses in Power Utilities
Muhammad Salman Saeed, Mohd Wazir Mustafa, Usman Ullah Sheikh, Touqeer Ahmed Jumani, Ilyas Khan, Samer Atawneh, Nawaf N. Hamadneh
March 27, 2023 (v1)
Keywords: Artificial Intelligence, boosted C5.0 decision tree, electricity theft detection, machine learning algorithms, power utilities
Electricity fraud in billing are the primary concerns for Distribution System Operators (DSO). It is estimated that billions of dollars are wasted annually due to these illegal activities. DSOs around the world, especially in underdeveloped countries, still utilize conventional time consuming and inefficient methods for Non-Technical Loss (NTL) detection. This research work attempts to solve the mentioned problem by developing an efficient energy theft detection model in order to identify the fraudster customers in a power distribution system. The key motivation for the present study is to assist the DSOs in their fight against energy theft. The proposed computational model initially utilizes a set of distinct features extracted from the monthly consumers’ consumption data, obtained from Multan Electric Power Company (MEPCO) Pakistan, to segregate the honest and the fraudulent customers. The Pearson’s chi-square feature selection algorithm is adopted to select the most relevant feature... [more]
Using SCADA Data for Wind Turbine Condition Monitoring: A Systematic Literature Review
Jorge Maldonado-Correa, Sergio Martín-Martínez, Estefanía Artigao, Emilio Gómez-Lázaro
March 27, 2023 (v1)
Keywords: Artificial Intelligence, condition monitoring, fault prediction, SCADA data, wind turbine
Operation and maintenance (O&M) activities represent a significant share of the total expenditure of a wind farm. Of these expenses, costs associated with unexpected failures account for the highest percentage. Therefore, it is clear that early detection of wind turbine (WT) failures, which can be achieved through appropriate condition monitoring (CM), is critical to reduce O&M costs. The use of Supervisory Control and Data Acquisition (SCADA) data has recently been recognized as an effective solution for CM since most modern WTs record large amounts of parameters using their SCADA systems. Artificial intelligence (AI) techniques can convert SCADA data into information that can be used for early detection of WT failures. This work presents a systematic literature review (SLR) with the aim to assess the use of SCADA data and AI for CM of WTs. To this end, we formulated four research questions as follows: (i) What are the current challenges of WT CM? (ii) What are the WT components to wh... [more]
Artificial Intelligence-Based Weighting Factor Autotuning for Model Predictive Control of Grid-Tied Packed U-Cell Inverter
Mostefa Mohamed-Seghir, Abdelbasset Krama, Shady S. Refaat, Mohamed Trabelsi, Haitham Abu-Rub
March 27, 2023 (v1)
Keywords: Artificial Intelligence, Model Predictive Control, packed U-cell (PUC) inverter, weighting factor autotuning
The tuning of weighting factor has been considered as the most challenging task in the implementation of multi-objective model predictive control (MPC) techniques. Thus, this paper proposes an artificial intelligence (AI)-based weighting factor autotuning in the design of a finite control set MPC (FCS-MPC) applied to a grid-tied seven-level packed U-cell (PUC7) multilevel inverter (MLI). The studied topology is capable of producing a seven-level output voltage waveform and inject sinusoidal current to the grid with high power quality while using a reduced number of components. The proposed cost function optimization algorithm ensures auto-adjustment of the weighting factor to guarantee low injected grid current total harmonic distortion (THD) at different power ratings while balancing the capacitor voltage. The optimal weighting factor value is selected at each sampling time to guarantee a stable operation of the PUC inverter with high power quality. The weighting factor selection is p... [more]
Using Machine Learning to Enrich Building Databases—Methods for Tailored Energy Retrofits
Jenny von Platten, Claes Sandels, Kajsa Jörgensson, Viktor Karlsson, Mikael Mangold, Kristina Mjörnell
March 27, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, building database enrichment, building-specific information, energy performance certificate, energy retrofitting, energy transition, Google Street View, long-term renovation strategy, Machine Learning, support vector machine
Building databases are important assets when estimating and planning for national energy savings from energy retrofitting. However, databases often lack information on building characteristics needed to determine the feasibility of specific energy conservation measures. In this paper, machine learning methods are used to enrich the Swedish database of Energy Performance Certificates with building characteristics relevant for a chosen set of energy retrofitting packages. The study is limited to the Swedish multifamily building stock constructed between 1945 and 1975, as these buildings are facing refurbishment needs that advantageously can be combined with energy retrofitting. In total, 514 ocular observations were conducted in Google Street View of two building characteristics that were needed to determine the feasibility of the chosen energy retrofitting packages: (i) building type and (ii) suitability for additional façade insulation. Results showed that these building characteristic... [more]
A Method for the Combined Estimation of Battery State of Charge and State of Health Based on Artificial Neural Networks
Angelo Bonfitto
March 27, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, battery, electric vehicles, estimation, hybrid vehicles, state of charge, state of health
This paper proposes a method for the combined estimation of the state of charge (SOC) and state of health (SOH) of batteries in hybrid and full electric vehicles. The technique is based on a set of five artificial neural networks that are used to tackle a regression and a classification task. In the method, the estimation of the SOC relies on the identification of the ageing of the battery and the estimation of the SOH depends on the behavior of the SOC in a recursive closed-loop. The networks are designed by means of training datasets collected during the experimental characterizations conducted in a laboratory environment. The lithium battery pack adopted during the study is designed to supply and store energy in a mild hybrid electric vehicle. The validation of the estimation method is performed by using real driving profiles acquired on-board of a vehicle. The obtained accuracy of the combined SOC and SOH estimator is around 97%, in line with the industrial requirements in the auto... [more]
A Novel Protection Scheme for Solar Photovoltaic Generator Connected Networks Using Hybrid Harmony Search Algorithm-Bollinger Bands Approach
Vipul N. Rajput, Kartik S. Pandya, Junhee Hong, Zong Woo Geem
March 27, 2023 (v1)
Keywords: Artificial Intelligence, Bollinger Bands, directional overcurrent relay, Harmony Search Algorithm, microgrid protection, optimum relay coordination, power system protection, solar photovoltaic generator, statistical test, voltage restrained overcurrent relay
This paper introduces a new protection system for solar photovoltaic generator (SPVG)-connected networks. The system is a combination of voltage-restrained overcurrent relays (VROCRs) and directional overcurrent relays (DOCRs). The DOCRs are implemented to sense high fault current on the grid side, and VROCRs are deployed to sense low fault current supplied by the SPVG. Furthermore, a novel challenge for the optimal coordination of DOCRs-DOCRs and DOCRs-VROCRs is formulated. Due to the inclusion of additional constraints of VROCR, the relay coordination problem becomes more complicated. To solve this complex problem, a hybrid Harmony Search Algorithm-Bollinger Bands (HSA-BB) method is proposed. Also, the lower and upper bands in BB are dynamically adjusted with the generation number to assist the HSA in the exploration and exploitation stages. The proposed method is implemented on three different SPVG-connected networks. To exhibit the effectiveness of the proposed method, the obtained... [more]
Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting
Sue Ellen Haupt, Tyler C. McCandless, Susan Dettling, Stefano Alessandrini, Jared A. Lee, Seth Linden, William Petzke, Thomas Brummet, Nhi Nguyen, Branko Kosović, Gerry Wiener, Tahani Hussain, Majed Al-Rasheedi
March 24, 2023 (v1)
Keywords: Artificial Intelligence, Machine Learning, renewable energy forecasting, solar energy, wind energy
A modern renewable energy forecasting system blends physical models with artificial intelligence to aid in system operation and grid integration. This paper describes such a system being developed for the Shagaya Renewable Energy Park, which is being developed by the State of Kuwait. The park contains wind turbines, photovoltaic panels, and concentrated solar renewable energy technologies with storage capabilities. The fully operational Kuwait Renewable Energy Prediction System (KREPS) employs artificial intelligence (AI) in multiple portions of the forecasting structure and processes, both for short-range forecasting (i.e., the next six hours) as well as for forecasts several days out. These AI methods work synergistically with the dynamical/physical models employed. This paper briefly describes the methodology used for each of the AI methods, how they are blended, and provides a preliminary assessment of their relative value to the prediction system. Each operational AI component add... [more]
Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting
Tyler McCandless, Pedro Angel Jiménez
March 24, 2023 (v1)
Keywords: Artificial Intelligence, Machine Learning, random forests, remote sensing, solar power forecasting, supervised learning
In order for numerical weather prediction (NWP) models to correctly predict solar irradiance reaching the earth’s surface for more accurate solar power forecasting, it is important to initialize the NWP model with accurate cloud information. Knowing where the clouds are located is the first step. Using data from geostationary satellites is an attractive possibility given the low latencies and high spatio-temporal resolution provided nowadays. Here, we explore the potential of utilizing the random forest machine learning method to generate the cloud mask from GOES-16 radiances. We first perform a predictor selection process to determine the optimal predictor set for the random forest predictions of the horizontal cloud fraction and then determine the appropriate threshold to generate the cloud mask prediction. The results show that the random forest method performs as well as the GOES-16 level 2 clear sky mask product with the ability to customize the threshold for under or over predict... [more]
Energetic Map Data Imputation: A Machine Learning Approach
Tobias Straub, Mandy Nagy, Maxim Sidorov, Leonardo Tonetto, Michael Frey, Frank Gauterin
March 23, 2023 (v1)
Keywords: Artificial Intelligence, Big Data, classification, electric mobility, missing data imputation, regression, supervised machine learning
Despite a rapid increase of public interest for electric mobility, several factors still impede Battery Electric Vehicles’ (BEVs) acceptance. These factors include their limited range and inconvenient charging. For mitigating these limitations to users, certain BEV-specific services are required. Therefore, such services provide a reliable range prediction and routing, including charging-stop planning. The basis of these services is a precise and reliable Energy Demand (ED) prediction. For that matter, aggregated fleet-vehicle data combined with map-specific data (e.g., road slope) form an energetic map, which can serve for precise ED predictions. However, data coverage is paramount for these predictions, more specifically regarding gapless energetic maps. This work aims to eliminate the energetic map’s gaps using two Machine Learning (ML) approaches: regression and classification. The proposed ML solution builds upon the synergy between map-information and crowdsourced driving profile... [more]
Review of Computational Intelligence Methods for Local Energy Markets at the Power Distribution Level to Facilitate the Integration of Distributed Energy Resources: State-of-the-art and Future Research
Pavlos S. Georgilakis
March 22, 2023 (v1)
Keywords: aggregator, Artificial Intelligence, computational intelligence, distributed energy resources, distributed generation, distribution systems operation, local energy markets, smart distribution system, transactive energy
The massive integration of distributed energy resources in power distribution systems in combination with the active network management that is implemented thanks to innovative information and communication technologies has created the smart distribution systems of the new era. This new environment introduces challenges for the optimal operation of the smart distribution network. Local energy markets at power distribution level are highly investigated in recent years. The aim of local energy markets is to optimize the objectives of market participants, e.g., to minimize the network operation cost for the distribution network operator, to maximize the profit of the private distributed energy resources, and to minimize the electricity cost for the consumers. Several models and methods have been suggested for the design and optimal operation of local energy markets. This paper introduces an overview of the state-of-the-art computational intelligence methods applied to the optimal operatio... [more]
Forecasting Energy Consumption of a Public Building Using Transformer and Support Vector Regression
Junhui Huang, Sakdirat Kaewunruen
March 20, 2023 (v1)
Keywords: Artificial Intelligence, building energy performance, building physics, CO2 emissions, energy consumption, Machine Learning, net zero energy building, transformer
Most of the Artificial Intelligence (AI) models currently used in energy forecasting are traditional and deterministic. Recently, a novel deep learning paradigm, called ‘transformer’, has been developed, which adopts the mechanism of self-attention. Transformers are designed to better process and predict sequential data sets (i.e., historical time records) as well as to track any relationship in the sequential data. So far, a few transformer-based applications have been established, but no industry-scale application exists to build energy forecasts. Accordingly, this study is the world’s first to establish a transformer-based model to estimate the energy consumption of a real-scale university library and benchmark with a baseline model (Support Vector Regression) SVR. With a large dataset from 1 September 2017 to 13 November 2021 with 30 min granularity, the results using four historical electricity readings to estimate one future reading demonstrate that the SVR (an R2 of 0.92) presen... [more]
Gear Wear Detection Based on Statistic Features and Heuristic Scheme by Using Data Fusion of Current and Vibration Signals
Arturo Yosimar Jaen-Cuellar, Miguel Trejo-Hernández, Roque Alfredo Osornio-Rios, Jose Alfonso Antonino-Daviu
March 20, 2023 (v1)
Keywords: Artificial Intelligence, diagnosis, electrical machine, faults detection, industrial motors
Kinematic chains are ensembles of elements that integrate, among other components, with the induction motors, the mechanical couplings, and the loads to provide support to the industrial processes that require motion interchange. In this same line, the induction motor justifies its importance because this machine is the core that provides the power and generates the motion of the industrial process. However, also, it is possible to diagnose other types of faults that occur in other elements in the kinematic chain, which are reflected as problems in the motor operation. With this purpose, the coupling between the motor and the final load in the chain requires, in many situations, the use of a gearbox that balances the torque−velocity relationship. Thus, the gear wear in this component is addressed in many works, but the study of gradual wear has not been completely covered yet at different operating frequencies. Therefore, in this work, a methodology is proposed based on statistical fea... [more]
Parallel Automatic History Matching Algorithm Using Reinforcement Learning
Omar S. Alolayan, Abdullah O. Alomar, John R. Williams
March 20, 2023 (v1)
Keywords: Artificial Intelligence, history matching, parallel actor–critic, reinforcement learning, reservoir simulation
Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such a formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.
Artificial Intelligence in Wind Speed Forecasting: A Review
Sandra Minerva Valdivia-Bautista, José Antonio Domínguez-Navarro, Marco Pérez-Cisneros, Carlos Jesahel Vega-Gómez, Beatriz Castillo-Téllez
March 20, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, ensemble prediction, wind speed forecasting
Wind energy production has had accelerated growth in recent years, reaching an annual increase of 17% in 2021. Wind speed plays a crucial role in the stability required for power grid operation. However, wind intermittency makes accurate forecasting a complicated process. Implementing new technologies has allowed the development of hybrid models and techniques, improving wind speed forecasting accuracy. Additionally, statistical and artificial intelligence methods, especially artificial neural networks, have been applied to enhance the results. However, there is a concern about identifying the main factors influencing the forecasting process and providing a basis for estimation with artificial neural network models. This paper reviews and classifies the forecasting models used in recent years according to the input model type, the pre-processing and post-processing technique, the artificial neural network model, the prediction horizon, the steps ahead number, and the evaluation metric.... [more]
Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends
Jorge De La Cruz, Eduardo Gómez-Luna, Majid Ali, Juan C. Vasquez, Josep M. Guerrero
March 17, 2023 (v1)
Keywords: Artificial Intelligence, fault classification, fault location, local measurement-based techniques, low-voltage and DC smart grids, microgrids, resiliency of smart grids, smart grids
Thanks to smart grids, more intelligent devices may now be integrated into the electric grid, which increases the robustness and resilience of the system. The integration of distributed energy resources is expected to require extensive use of communication systems as well as a variety of interconnected technologies for monitoring, protection, and control. The fault location and diagnosis are essential for the security and well-coordinated operation of these systems since there is also greater risk and different paths for a fault or contingency in the system. Considering smart distribution systems, microgrids, and smart automation substations, a full investigation of fault location in SGs over the distribution domain is still not enough, and this study proposes to analyze the fault location issues and common types of power failures in most of their physical components and communication infrastructure. In addition, we explore several fault location techniques in the smart grid’s distribu... [more]
Manufacturing Energy Efficiency and Industry 4.0
Konstantinos Salonitis
March 17, 2023 (v1)
Keywords: Artificial Intelligence, big data management in manufacturing, green manufacturing, industrial internet of things, industrial sustainability, industry 4.0 industrial cyber-physical systems (ICPS), machine learning for energy-efficient manufacturing
This Special Issue of Energies was devoted to the topic of “Manufacturing Energy Efficiency and Industry 4.0”. To a great extent, this issue follows the successful previous Special Issue on “Energy Efficiency of Manufacturing Processes and Systems”, which attracted some significant attention from scholars, practitioners, and policy-makers from all over the world. In total, six papers were published. The main topics included energy efficiency improvement in both the manufacturing process and system levels, as well as how this can be facilitated through the use of Industry 4.0.
Artificial Intelligence for Electric Vehicle Infrastructure: Demand Profiling, Data Augmentation, Demand Forecasting, Demand Explainability and Charge Optimisation
Vidura Sumanasena, Lakshitha Gunasekara, Sachin Kahawala, Nishan Mills, Daswin De Silva, Mahdi Jalili, Seppo Sierla, Andrew Jennings
March 17, 2023 (v1)
Keywords: Artificial Intelligence, charge optimisation, demand explainability, demand forecasting, demand profiling, electric vehicles, EV data augmentation
Electric vehicles (EVs) are advancing the transport sector towards a robust and reliable carbon-neutral future. Given this increasing uptake of EVs, electrical grids and power networks are faced with the challenges of distributed energy resources, specifically the charge and discharge requirements of the electric vehicle infrastructure (EVI). Simultaneously, the rapid digitalisation of electrical grids and EVs has led to the generation of large volumes of data on the supply, distribution and consumption of energy. Artificial intelligence (AI) algorithms can be leveraged to draw insights and decisions from these datasets. Despite several recent work in this space, a comprehensive study of the practical value of AI in charge-demand profiling, data augmentation, demand forecasting, demand explainability and charge optimisation of the EVI has not been formally investigated. The objective of this study was to design, develop and evaluate a comprehensive AI framework that addresses this gap... [more]
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