Records with Keyword: Artificial Intelligence
Showing records 1 to 25 of 204. [First] Page: 1 2 3 4 5 Last
Artificial Intelligence and Carbon Emissions in Manufacturing Firms: The Moderating Role of Green Innovation
Yixuan Chen, Shanyue Jin
February 10, 2024 (v1)
Subject: Environment
Keywords: Artificial Intelligence, carbon emissions, green management innovation, green product innovation, green technology innovation
Carbon emissions have gained worldwide attention in the industrial era. As a key carbon-emitting industry, achieving net-zero carbon emissions in the manufacturing sector is vital to mitigating the negative effects of climate change and achieving sustainable development. The rise of intelligent technologies has driven industrial structural transformations that may help achieve carbon reduction. Artificial intelligence (AI) technology is an important part of digitalization, providing new technological tools and directions for the low carbon development of enterprises. This study selects Chinese A-share listed companies in the manufacturing industry from 2012 to 2021 as the research objects and uses a fixed-effects regression model to study the relationship between AI and carbon emissions. This study clarifies the significance of enterprise AI technology applications in realizing carbon emissions reduction and explores the regulatory mechanism from the perspective of the innovation effec... [more]
A Novel Hybrid Optimization Approach for Fault Detection in Photovoltaic Arrays and Inverters Using AI and Statistical Learning Techniques: A Focus on Sustainable Environment
Ahmad Abubakar, Mahmud M. Jibril, Carlos F. M. Almeida, Matheus Gemignani, Mukhtar N. Yahya, Sani I. Abba
November 30, 2023 (v1)
Keywords: Artificial Intelligence, boosted tree algorithms, Elman neural network, Fault Detection, Gaussian processes regression, multi-layer perceptron, sustainable development
Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman neural network (ENN), boosted tree algorithms (BTA), multi-layer perceptron (MLP), and Gaussian processes regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages its strengths for the accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction. The fault detection and diagnosis were assessed using several statistical criteria includi... [more]
Enhancing Power Generation Stability in Oscillating-Water-Column Wave Energy Converters through Deep-Learning-Based Time Delay Compensation
Chan Roh
July 7, 2023 (v1)
Keywords: Artificial Intelligence, deep learning algorithm, maximum power point tracking, optimal control, oscillating-water-column wave energy converter, output power performance, rated power control, Renewable and Sustainable Energy, time delay
Oscillating-water-column wave energy converters (OWC-WECs) are gaining attention for their high energy potential and environmental friendliness. However, their irregular input energy characteristics pose challenges to achieving stable power generation, particularly due to high peak power compared to average power. This study focuses on stable rating control to enable continuous power generation in the presence of irregular wave energy. It is difficult to precisely configure the existing rated power controllers due to physical time delays; this impacts system stability and utilization. To address this, we propose a rated power controller that compensates for system time delays using a deep learning algorithm. By predicting the valve control angle in advance and analyzing the input data for angle estimation, we successfully compensate for the physical time delay. The performance of the proposed rated power controller, incorporating the deep learning algorithm, is evaluated by analyzing t... [more]
An Artificial Intelligence Method for Flowback Control of Hydraulic Fracturing Fluid in Oil and Gas Wells
Ruixuan Li, Hangxin Wei, Jingyuan Wang, Bo Li, Xue Zheng, Wei Bai
July 7, 2023 (v1)
Keywords: Artificial Intelligence, deep learning neural network, hydraulic fracture, process control
Hydraulic fracturing is one of the main ways to increase oil and gas production. However, with existing methods, the diameter of the nozzle cannot be easily adjusted. This therefore results in ‘sand production’ in flowback fluid, affecting the application of hydraulic fracturing. This is because it is difficult to identify the one-dimensional series signal of fracturing fluid collected on site. In order to avoid ‘sand production’ in the flowback fluid, the nozzle should be properly controlled. Aiming to address this problem, a novel augmented residual deep learning neural network (AU-RES) is proposed that can identify the characteristics of multiple one-dimensional time series signals and effectively predict the diameter of the nozzle. The AU-RES network includes three parts: signal conversion layer, residual and convolutional layer, fully connected layer (including regression layer). Firstly, a spatial conversion algorithm for multiple one-dimensional time series signals is proposed,... [more]
Machine Learning Algorithms and Fundamentals as Emerging Safety Tools in Preservation of Fruits and Vegetables: A Review
Vinay Kumar Pandey, Shivangi Srivastava, Kshirod Kumar Dash, Rahul Singh, Shaikh Ayaz Mukarram, Béla Kovács, Endre Harsányi
July 7, 2023 (v1)
Keywords: Artificial Intelligence, fruit preservation, Machine Learning, nanotechnology
Machine learning assists with food process optimization techniques by developing a model to predict the optimal solution for given input data. Machine learning includes unsupervised and supervised learning, data pre-processing, feature engineering, model selection, assessment, and optimization methods. Various problems with food processing optimization could be resolved using these techniques. Machine learning is increasingly being used in the food industry to improve production efficiency, reduce waste, and create personalized customer experiences. Machine learning may be used to improve ingredient utilization and save costs, automate operations such as packing and labeling, and even forecast consumer preferences to develop personalized products. Machine learning is also being used to identify food safety hazards before they reach the consumer, such as contaminants or spoiled food. The usage of machine learning in the food sector is predicted to rise in the near future as more busines... [more]
Control Strategy Based on Artificial Intelligence for a Double-Stage Absorption Heat Transformer
Suset Vázquez-Aveledo, Rosenberg J. Romero, Moisés Montiel-González, Jesús Cerezo
July 4, 2023 (v1)
Keywords: absorption heat transformer, aqueous lithium bromide, Artificial Intelligence, artificial neural network, fuzzy logic, heat pump
Thermal energy recovery systems have different candidates to mitigate CO2 emissions as recommended by the UN in its list of SDGs. One of these promising systems is thermal absorption transformers, which generally use lithium-water bromide as the working fluid. A Double Stage Heat Transformer (DSHT) is a thermal machine that allows the recovery of thermal energy at a higher temperature than it is supplied through the effect of steam absorption in a concentrated solution of lithium bromide. There are very precise thermodynamic models which allow us to calculate all the possible operating conditions of the DSHT. To perform the control of these systems, the use of Artificial Intelligence (AI) is proposed with two computational techniques—Fuzzy Logic (FL) and Artificial Neural Network (ANN)—to calculate in real-time the set of variables that maximize the product’s Gross Temperature Lift (GTL) and Coefficient of Performance (COP) in a DSHT. The values for Coefficient of Determination (R2), M... [more]
Evaluating the Efficacy of Intelligent Methods for Maximum Power Point Tracking in Wind Energy Harvesting Systems
Dallatu Abbas Umar, Gamal Alkawsi, Nur Liyana Mohd Jailani, Mohammad Ahmed Alomari, Yahia Baashar, Ammar Ahmed Alkahtani, Luiz Fernando Capretz, Sieh Kiong Tiong
June 13, 2023 (v1)
Keywords: Artificial Intelligence, MPPT, wind energy harvesting system
As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques com... [more]
Development of Surface Mining 4.0 in Terms of Technological Shock in Energy Transition: A Review
Sergey Zhironkin, Ekaterina Taran
May 23, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, Industry 4.0, Internet of Things, Surface Mining 4.0, technological shock, unmanned equipment
The expansion of end-to-end Industry 4.0 technologies in various industries has caused a technological shock in the mineral resource sector, wherein itsdigital maturity is lower than in the manufacturing sector. As a result of the shock, the productivity and profitability of raw materials extraction has begun to lag behind the industries of its deep processing, which, in the conditions of volatile raw materials markets, can provoke sectoral crises. The diffusion of Industry 4.0 technologies in the mining sector (Mining 4.0) can prevent a technological shock if they are implemented in all segments, including quarrying (Surface Mining 4.0). The Surface Mining 4.0 technological platform would connect the advanced achievements of the Fourth Industrial Revolution (end-to-end digital artificial intelligence technologies, cyber-physical systems and unmanned production with traditional geotechnology) without canceling them, but instead bringing them to a new level of productivity, resource con... [more]
Application of Wearable Gloves for Assisted Learning of Sign Language Using Artificial Neural Networks
Hyeon-Jun Kim, Soo-Whang Baek
April 28, 2023 (v1)
Keywords: Artificial Intelligence, internet of things, LSTM, neural network, RNN, wearable
This study proposes the design and application of wearable gloves that can recognize sign language expressions from input images via long short-term memory (LSTM) network models and can learn sign language through finger movement generation and vibration motor feedback. It is difficult for nondisabled people who do not know sign language to express sign language accurately. Therefore, we suggest the use of wearable gloves for sign language education to help nondisabled people learn and accurately express sign language. The wearable glove consists of a direct current motor, a link (finger exoskeleton) that can generate finger movements, and a flexible sensor that recognizes the degree of finger bending. When the coordinates of the hand move in the input image, the sign language motion is fed back through the vibration motor attached to the wrist. The proposed wearable glove can learn 20 Korean sign language words, and the data used for learning are configured to represent the joint coor... [more]
Artificial Intelligence for Energy Processes and Systems: Applications and Perspectives
Dorian Skrobek, Jaroslaw Krzywanski, Marcin Sosnowski, Ghulam Moeen Uddin, Waqar Muhammad Ashraf, Karolina Grabowska, Anna Zylka, Anna Kulakowska, Wojciech Nowak
April 28, 2023 (v1)
Keywords: Artificial Intelligence, deep learning, energy processes and systems, Machine Learning, neural networks
In recent years, artificial intelligence has become increasingly popular and is more often used by scientists and entrepreneurs. The rapid development of electronics and computer science is conducive to developing this field of science. Man needs intelligent machines to create and discover new relationships in the world, so AI is beginning to reach various areas of science, such as medicine, economics, management, and the power industry. Artificial intelligence is one of the most exciting directions in the development of computer science, which absorbs a considerable amount of human enthusiasm and the latest achievements in computer technology. This article was dedicated to the practical use of artificial neural networks. The article discusses the development of neural networks in the years 1940−2022, presenting the most important publications from these years and discussing the latest achievements in the use of artificial intelligence. One of the chapters focuses on the use of artific... [more]
Understanding & Screening of DCW through Application of Data Analysis of Experiments and ML/AI
Tony Thomas, Pushpa Sharma, Dharmendra Kumar Gupta
April 28, 2023 (v1)
Subject: Environment
Keywords: Artificial Intelligence, experimentation, Machine Learning, oil recovery mechanism, sustainable development, waterflood
An oil recovery technique, different composition waterflooding (DCW), dependent on the varying injected water composition has been the subject of various research work in the past decades. Research work has been carried out at the lab, well and field scale whereby the introduction of different injection water composition vis-a-vis the connate water is seen to bring about improvements in the oil recovery (improvements in both macroscopic and microscopic recoveries) based on the chemical reactions, while being sustainable from ease of implementation and reduced carbon footprint points of view. Although extensive research has been conducted, the main chemical mechanisms behind the oil recovery are not yet concluded upon. This research work performs a data analysis of the various experiments, identifies gaps in existing experimentation and proposes a comprehensive experimentation measurement reporting at the system, rock, brine and oil levels that leads to enhanced understanding of the und... [more]
Artificial Intelligence Methods in Hydraulic System Design
Grzegorz Filo
April 28, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, evolutionary algorithms, fuzzy logic, hydraulic system design
Reducing energy consumption and increasing operational efficiency are currently among the leading research topics in the design of hydraulic systems. In recent years, hydraulic system modeling and design techniques have rapidly expanded, especially using artificial intelligence methods. Due to the variety of algorithms, methods, and tools of artificial intelligence, it is possible to consider the prospects and directions of their further development. The analysis of the most recent publications allowed three leading technologies to be indicated, including artificial neural networks, evolutionary algorithms, and fuzzy logic. This article summarizes their current applications in the research, main advantages, and limitations, as well as expected directions for further development.
Machine Learning Applications in Biofuels’ Life Cycle: Soil, Feedstock, Production, Consumption, and Emissions
Iftikhar Ahmad, Adil Sana, Manabu Kano, Izzat Iqbal Cheema, Brenno C. Menezes, Junaid Shahzad, Zahid Ullah, Muzammil Khan, Asad Habib
April 24, 2023 (v1)
Machine Learning (ML) is one of the major driving forces behind the fourth industrial revolution. This study reviews the ML applications in the life cycle stages of biofuels, i.e., soil, feedstock, production, consumption, and emissions. ML applications in the soil stage were mostly used for satellite images of land to estimate the yield of biofuels or a suitability analysis of agricultural land. The existing literature have reported on the assessment of rheological properties of the feedstocks and their effect on the quality of biofuels. The ML applications in the production stage include estimation and optimization of quality, quantity, and process conditions. The fuel consumption and emissions stage include analysis of engine performance and estimation of emissions temperature and composition. This study identifies the following trends: the most dominant ML method, the stage of life cycle getting the most usage of ML, the type of data used for the development of the ML-based models,... [more]
The Review of Carbon Capture-Storage Technologies and Developing Fuel Cells for Enhancing Utilization
Nehil Shreyash, Muskan Sonker, Sushant Bajpai, Saurabh Kr Tiwary, Mohd Ashhar Khan, Subham Raj, Tushar Sharma, Susham Biswas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, Carbon Capture, CO2 combustion, electrochemical conversion, fuel cell, storage and utilization
The amount of CO2 released in the atmosphere has been at a continuous surge in the last decade, and in order to protect the environment from global warming, it is necessary to employ techniques like carbon capture. Developing technologies like Carbon Capture Utilization and Storage aims at mitigating the CO2 content from the air we breathe and has garnered immense research attention. In this review, the authors have aimed to discuss the various technologies that are being used to capture the CO2 from the atmosphere, store it and further utilize it. For utilization, researchers have developed alternatives to make profits from CO2 by converting it into an asset. The development of newer fuel cells that consume CO2 in exchange for electrical power to drive the industries and produce valuable hydrocarbons in the form of fuel has paved the path for more research in the field of carbon utilization. The primary focus on the article is to inspect the environmental and economic feasibility of n... [more]
AI and Data Democratisation for Intelligent Energy Management
Vangelis Marinakis, Themistoklis Koutsellis, Alexandros Nikas, Haris Doukas
April 24, 2023 (v1)
Keywords: Artificial Intelligence, data democratisation, data sharing, decarbonisation, decision support, energy data spaces, energy management, interoperability
Despite the large number of technology-intensive organisations, their corporate know-how and underlying workforce skill are not mature enough for a successful rollout of Artificial Intelligence (AI) services in the near-term. However, things have started to change, owing to the increased adoption of data democratisation processes, and the capability offered by emerging technologies for data sharing while respecting privacy, protection, and security, as well as appropriate learning-based modelling capabilities for non-expert end-users. This is particularly evident in the energy sector. In this context, the aim of this paper is to analyse AI and data democratisation, in order to explore the strengths and challenges in terms of data access problems and data sharing, algorithmic bias, AI transparency, privacy and other regulatory constraints for AI-based decisions, as well as novel applications in different domains, giving particular emphasis on the energy sector. A data democratisation fr... [more]
An Artificial Intelligence Empowered Cyber Physical Ecosystem for Energy Efficiency and Occupation Health and Safety
Petros Koutroumpinas, Yu Zhang, Steve Wallis, Elizabeth Chang
April 21, 2023 (v1)
Subject: Environment
Keywords: Artificial Intelligence, COVID-19, cyber-physical system, ecosystem, Energy Efficiency, OHS, remote industries, smart meter
Reducing energy waste is one of the primary concerns facing Remote Industrial Plants (RIP) and, in particular, the accommodations and operational plants located in remote areas. With the COVID-19 pandemic continuing to attack the health of workforce, managing the balance between energy efficiency and Occupation Health and Safety (OHS) in the workplace becomes another great challenge for the RIP. Maintaining this balance is difficult mainly because a full awareness of the OHS will generally consume more energy while reducing the energy cost may lead to a less effective OHS, and the existing literature has not seen a system that is designed for the RIPs to conserve energy usage and improve workforce OHS simultaneously. To bridge this gap, in this paper, we propose an AI Empowered Cyber Physical Ecosystem (AECPE) solution for the RIPs, which integrates Cyber-Physical Systems (CPS), artificial intelligence, and mobile networks. The preliminary results of lab experiments and field tests pro... [more]
Artificial Intelligence for the Prediction of the Thermal Performance of Evaporative Cooling Systems
Hafiz M. Asfahan, Uzair Sajjad, Muhammad Sultan, Imtiyaz Hussain, Khalid Hamid, Mubasher Ali, Chi-Chuan Wang, Redmond R. Shamshiri, Muhammad Usman Khan
April 21, 2023 (v1)
Keywords: Artificial Intelligence, direct evaporative cooling, evaporative cooling, indirect evaporative cooling, Maisotsenko evaporative cooling
The present study reports the development of a deep learning artificial intelligence (AI) model for predicting the thermal performance of evaporative cooling systems, which are widely used for thermal comfort in different applications. The existing, conventional methods for the analysis of evaporation-assisted cooling systems rely on experimental, mathematical, and empirical approaches in order to determine their thermal performance, which limits their applications in diverse and ambient spatiotemporal conditions. The objective of this research was to predict the thermal performance of three evaporation-assisted air-conditioning systems—direct, indirect, and Maisotsenko evaporative cooling systems—by using an AI approach. For this purpose, a deep learning algorithm was developed and lumped hyperparameters were initially chosen. A correlation analysis was performed prior to the development of the AI model in order to identify the input features that could be the most influential for the... [more]
Digital Transformation of Energy Companies: A Colombian Case Study
Sandra Giraldo, David la Rotta, César Nieto-Londoño, Rafael E. Vásquez, Ana Escudero-Atehortúa
April 19, 2023 (v1)
Subject: Energy Policy
Keywords: Artificial Intelligence, digital transformation, energy commercialization, energy trading, energy transition, hydropower projects, Industry 4.0, Renewable and Sustainable Energy, risk management
The United Nations established 17 Sustainable Development Goals (SDGs), and the fulfillment of the 7th, defined as “Ensure access to affordable, reliable, sustainable, and modern energy for all”, requires energy industry transitions and digital transformations, which implies that diverse stakeholders need to move fast to allow the growth of more flexible power systems. This paper contains the case report that addresses the commercial digital transformation process developed at AES Colombia, through the implementation of a modern platform based on specialized applications that use Industry 4.0 tools. The Chivor hydropower project, a 1000-MW powerplant that covers 6% of Colombia’s demand, which is owned by AES Colombia and constitutes its primary asset, is first described. Then, a description of Colombia’s complex market (energy matrix, trading and dispatch mechanisms, and future projects) is presented. Then, the methodology followed for the digital transformation process using modern to... [more]
Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence
Sofia Agostinelli, Fabrizio Cumo, Giambattista Guidi, Claudio Tomazzoli
April 19, 2023 (v1)
Keywords: Artificial Intelligence, digital construction, digital twin, edge computing, Energy Efficiency, energy management, nZEB
The research explores the potential of digital-twin-based methods and approaches aimed at achieving an intelligent optimization and automation system for energy management of a residential district through the use of three-dimensional data model integrated with Internet of Things, artificial intelligence and machine learning. The case study is focused on Rinascimento III in Rome, an area consisting of 16 eight-floor buildings with 216 apartment units powered by 70% of self-renewable energy. The combined use of integrated dynamic analysis algorithms has allowed the evaluation of different scenarios of energy efficiency intervention aimed at achieving a virtuous energy management of the complex, keeping the actual internal comfort and climate conditions. Meanwhile, the objective is also to plan and deploy a cost-effective IT (information technology) infrastructure able to provide reliable data using edge-computing paradigm. Therefore, the developed methodology led to the evaluation of th... [more]
Development of AI-Based Diagnostic Model for the Prediction of Hydrate in Gas Pipeline
Youngjin Seo, Byoungjun Kim, Joonwhoan Lee, Youngsoo Lee
April 19, 2023 (v1)
Subject: Optimization
Keywords: Artificial Intelligence, diagnostic model, gas hydrate, greedy layer-wise, stacked auto-encoder
For the stable supply of oil and gas resources, industry is pushing for various attempts and technology development to produce not only existing land fields but also deep-sea, where production is difficult. The development of flow assurance technology is necessary because hydrate is aggregated in the pipeline and prevent stable production. This study established a system that enables hydrate diagnosis in the gas pipeline from a flow assurance perspective. Learning data were generated using an OLGA simulator, and temperature, pressure, and hydrate volume at each time step were generated. Stacked auto-encoder (SAE) was used as the AI model after analyzing training loss. Hyper-parameter matching and structure optimization were carried out using the greedy layer-wise technique. Through time-series forecast, we determined that AI diagnostic model enables depiction of the growth of hydrate volume. In addition, the average R-square for the maximum hydrate volume was 97%, and that for the form... [more]
Battery State of Health Estimation with Improved Generalization Using Parallel Layer Extreme Learning Machine
Ethelbert Ezemobi, Andrea Tonoli, Mario Silvagni
April 19, 2023 (v1)
Keywords: Artificial Intelligence, automotive, Batteries, electric vehicles, energy reliability, hybrid vehicles, improved generalization, online application, parallel layer extreme learning machine, state of health
The online estimation of battery state of health (SOH) is crucial to ensure the reliability of the energy supply in electric and hybrid vehicles. An approach for enhancing the generalization of SOH estimation using a parallel layer extreme learning machine (PL-ELM) algorithm is analyzed in this paper. The deterministic and stable PL-ELM model is designed to overcome the drift problem that is associated with some conventional machine learning algorithms; hence, extending the application of a single SOH estimation model over a large set of batteries of the same type. The PL-ELM model was trained with selected features that characterize the SOH. These features are acquired as the discrete variation of indicator variables including voltage, state of charge (SOC), and energy releasable by the battery. The model training was performed with an experimental battery dataset collected at room temperature under a constant current load condition at discharge phases. Model validation was performed... [more]
Examination of Multivalent Diagnoses Developed by a Diagnostic Program with an Artificial Neural Network for Devices in the Electric Hybrid Power Supply System “House on Water”
Stanisław Duer, Konrad Zajkowski, Marta Harničárová, Henryk Charun, Dariusz Bernatowicz
April 19, 2023 (v1)
Keywords: Artificial Intelligence, diagnostic reasoning, hybrid power system, multivalent logic, technical diagnostics
This article presents the problem of diagnostic examination by the (DIAG) diagnostic system of devices of the House on Water (HoW) hybrid electric power system in the multi-valued (2, 3, and 4) state assessment. Forming the basis for the functioning of the (DIAG) diagnostic system is the measurement knowledge base of the object tested. For this purpose, the issues of building a diagnostic knowledge base for a hybrid power system for HoW are presented. The basis for obtaining diagnostic information for the measurement knowledge base is a functional and diagnostic analysis of the hybrid power system tested. The result of this analysis is a functional and diagnostic model of the research object. At the next stage of the work, on the basis of the model created, the sets of basic elements and the sets of measurement signals were determined together with the reference signals assigned. State classification in the (DIAG) system is based on an analysis of the value of the divergence metrics of... [more]
Feasibility of Black-Box Time Domain Modeling of Single-Phase Photovoltaic Inverters Using Artificial Neural Networks
Elias Kaufhold, Simon Grandl, Jan Meyer, Peter Schegner
April 19, 2023 (v1)
Keywords: Artificial Intelligence, converter, Modelling, photovoltaics, power electronics, power quality
This paper introduces a new black-box approach for time domain modeling of commercially available single-phase photovoltaic (PV) inverters in low voltage networks. An artificial neural network is used as a nonlinear autoregressive exogenous model to represent the steady state behavior as well as dynamic changes of the PV inverter in the frequency range up to 2 kHz. The data for the training and the validation are generated by laboratory measurements of a commercially available inverter for low power applications, i.e., 4.6 kW. The state of the art modeling approaches are explained and the constraints are addressed. The appropriate set of data for training is proposed and the results show the suitability of the trained network as a black-box model in time domain. Such models are required, i.e., for dynamic simulations since they are able to represent the transition between two steady states, which is not possible with classical frequency-domain models (i.e., Norton models). The demonstr... [more]
Liquified Petroleum Gas-Fuelled Vehicle CO2 Emission Modelling Based on Portable Emission Measurement System, On-Board Diagnostics Data, and Gradient-Boosting Machine Learning
Maksymilian Mądziel
April 18, 2023 (v1)
Keywords: Artificial Intelligence, Carbon Dioxide, emission modelling, LPG, Machine Learning, portable emission measurement system, vehicle emission
One method to reduce CO2 emissions from vehicle exhaust is the use of liquified petroleum gas (LPG) fuel. The global use of this fuel is high in European countries such as Poland, Romania, and Italy. There are a small number of computational models for the purpose of estimating the emissions of LPG vehicles. This work is one of the first to present a methodology for developing microscale CO2 emission models for LPG vehicles. The developed model is based on data from road tests using the portable emission measurement system (PEMS) and on-board diagnostic (OBDII) interface. This model was created from a previous exploratory data analysis while using gradient-boosting machine learning methods. Vehicle velocity and engine RPM were chosen as the explanatory variables for CO2 prediction. The validation of the model indicates its good precision, while its use is possible for the analysis of continuous CO2 emissions and the creation of emission maps for environmental analyses in urban areas. T... [more]
Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control
Matteo Acquarone, Claudio Maino, Daniela Misul, Ezio Spessa, Antonio Mastropietro, Luca Sorrentino, Enrico Busto
April 18, 2023 (v1)
Keywords: Artificial Intelligence, fuel consumption, hybrid electric vehicles, real-time control, reinforcement learning
The real-time control optimization of electrified vehicles is one of the most demanding tasks to be faced in the innovation progress of low-emissions mobility. Intelligent energy management systems represent interesting solutions to solve complex control problems, such as the maximization of the fuel economy of hybrid electric vehicles. In the recent years, reinforcement-learning-based controllers have been shown to outperform well-established real-time strategies for specific applications. Nevertheless, the effects produced by variation in the reward function have not been thoroughly analyzed and the potential of the adoption of a given RL agent under different testing conditions is still to be assessed. In the present paper, the performance of different agents, i.e., Q-learning, deep Q-Network and double deep Q-Network, are investigated considering a full hybrid electric vehicle throughout multiple driving missions and introducing two distinct reward functions. The first function aim... [more]
Showing records 1 to 25 of 204. [First] Page: 1 2 3 4 5 Last
[Show All Keywords]