Records with Keyword: Big Data
Showing records 1 to 25 of 55. [First] Page: 1 2 3 Last
Method for Dynamic Prediction of Oxygen Demand in Steelmaking Process Based on BOF Technology
Kaitian Zhang, Zhong Zheng, Liu Zhang, Yu Liu, Sujun Chen
September 21, 2023 (v1)
Keywords: basic oxygen furnace mode, Big Data, dynamic prediction, oxygen demand, steelmaking
Oxygen is an important energy medium in the steelmaking process. The accurate dynamic prediction of oxygen demand is needed to guarantee molten steel quality, improve the production rhythm, and promote the collaborative optimization of production and energy. In this work, a analysis of the mechanism and of industrial big data was undertaken, and we found that the characteristic factors of Basic Oxygen Furnace (BOF) oxygen consumption were different in different modes, such as duplex dephosphorization, duplex decarbonization, and the traditional mode. Based on this, a dynamic-prediction modeling method for BOF oxygen demand considering mode classification is proposed. According to the characteristics of BOF production organization, a control module based on dynamic adaptions of the production plan was researched to realize the recalculation of the model predictions. A simulation test on industrial data revealed that the average relative error of the model in each BOF mode was less than... [more]
An App-Based Recommender System Based on Contrasting Automobiles
Hsiu-Wen Liu, Jei-Zheng Wu, Fang-Lin Wu
April 25, 2023 (v1)
Subject: Other
Keywords: association rule, Big Data, data mining, mobile application, recommendation system
Product recommendation systems are essential for enhancing customer experience, and integrating them with mobile apps is crucial for improving usability and fostering user engagement. This study proposes a hybrid approach that utilizes comparative facts from pairwise comparison data and comparison lists, with association rules as the method to formulate the recommendation system. The study employs a dataset from the New-Cars Database app, comprising 30,867 vehicle comparisons made by 5327 users across 40 car brands and 870 cars from 30 January 2015 to 2 April 2015. Two metrics are developed to measure the system’s output under varying support and confidence thresholds. The findings suggest that adjusting the support and confidence values can improve the breadth and depth of product recommendations. In addition, the unit of analysis can affect the recommendation system’s output, with comparison lists supplementing and expanding the exploration of potential outcomes. The proposed hybrid... [more]
Development and Application of a Big Data Analysis-Based Procedure to Identify Concerns about Renewable Energy
So-Yun Jeong, Jae-Wook Kim, Han-Young Joo, Young-Seo Kim, Joo-Hyun Moon
April 24, 2023 (v1)
Keywords: Big Data, carbon neutrality, frequency analysis, public concerns, Renewable and Sustainable Energy
To achieve carbon neutrality by 2050, Korea has been expanding its investment in renewal energy distribution and technology development. However, with this rapid expansion of renewable energy, public concern about it has grown. This study developed and used a big data analysis-based procedure to analyze the questions registered on Naver, the largest portal site in Korea, from 2008 to 2020 to identify public concern over renewable energy. The big data analysis-based procedure consisted of two steps. The first was a frequency analysis to identify the most frequently registered words. The second was to classify questions using term frequency-inverse document frequency (TF-IDF) weight and cosine similarity based on word2vec. The analysis revealed the most frequently registered words related to renewable energy, such as “solar power,” “power generation,” “energy,” and “wind power.” It also revealed the most frequently registered questions, such as those related to solar panel installation,... [more]
Designing a Smart Gateway for Data Fusion Implementation in a Distributed Electronic System Used in Automotive Industry
Mircea Rîșteiu, Remus Dobra, Alexandru Avram, Florin Samoilă, Georgeta Buică, Renato Rizzo, Dan Doru Micu
April 20, 2023 (v1)
Keywords: Big Data, data compatibility, data-fusion-ready, distributed electronic circuit testing, embedded programming, Industry-4.0-ready, intelligent decision support, on-board diagnostics interface, predictive and automated service
This paper focuses on the interdisciplinary research on the design of a smart gateway for managing the dynamic error code testing collected and generated by the Electronic Control Unit (ECU) from the automotive industry. The techniques used to exchange information between the ECU code errors and knowledge bases, based on data fusion methods, allowed us to consolidate and ensure data reliability, and then to optimize processed data in our distributed electronic systems, as the basic state for Industry 4.0 standards. At the same time, they offered optimized data packets when the gateway was tested as a service integrator for ECU maintenance. The embedded programming solutions offered us safe, reliable, and flexible data packet management results on both communication systems (Transmission Control Protocol/Internet Provider (TCP/IP) and Controller Area Network (CAN) Bus) on the Electronic Control Unit (ECU) tested for diesel, high-pressure common rail engines. The main goal of this paper... [more]
Evaluation of Operation State of Power Grid Based on Random Matrix Theory and Qualitative Trend Analysis
Jie Yang, Weiqing Sun, Meiling Ma
April 18, 2023 (v1)
Keywords: Big Data, bulk power grids, qualitative trend analysis, random matrix theory, stable operation of power grid
Bulk power grid interconnection and the access of various smart devices make the current grid highly complex. Timely and accurately identifying the power grid operation state is crucial for monitoring the operation stability of the power grid. For this purpose, an evaluation method of the power grid operation state based on random matrix theory and qualitative trend analysis is proposed. This method constructs two evaluation indicators based on the operation data of the power grid, which cannot only find out whether the current state of the power grid is stable but can also find out whether there is a bad operation trend in the current power grid. Compared with the traditional method, this method analyzes the power grid’s operation state from the big data perspective. It does not need to consider the complex network structure and operation mechanism of the actual power grid. Finally, the effectiveness and feasibility of the method are verified by the simulations of the IEEE 118-bus sys... [more]
Suggesting a Stochastic Fractal Search Paradigm in Combination with Artificial Neural Network for Early Prediction of Cooling Load in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: Artificial Intelligence, Big Data, building energy, cooling load, deep learning, energy-efficiency, HVAC, Machine Learning, nature-inspired metaheuristic, smart buildings, smart city, zero energy
Early prediction of thermal loads plays an essential role in analyzing energy-efficient buildings’ energy performance. On the other hand, stochastic algorithms have recently shown high proficiency in dealing with this issue. These are the reasons that this study is dedicated to evaluating an innovative hybrid method for predicting the cooling load (CL) in buildings with residential usage. The proposed model is a combination of artificial neural networks and stochastic fractal search (SFS−ANNs). Two benchmark algorithms, namely the grasshopper optimization algorithm (GOA) and firefly algorithm (FA) are also considered to be compared with the SFS. The non-linear effect of eight independent factors on the CL is analyzed using each model’s optimal structure. Evaluation of the results outlined that all three metaheuristic algorithms (with more than 90% correlation) can adequately optimize the ANN. In this regard, this tool’s prediction error declined by nearly 23%, 18%, and 36% by applying... [more]
Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: air conditioning, Artificial Intelligence, Big Data, consumption prediction, deep learning, Energy Efficiency, heating loads, heating ventilation, Machine Learning, metaheuristic, operational research
A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation =... [more]
An Innovative Metaheuristic Strategy for Solar Energy Management through a Neural Networks Framework
Hossein Moayedi, Amir Mosavi
April 14, 2023 (v1)
Keywords: Artificial Intelligence, artificial neural networks, Big Data, deep learning, electrical power modeling, Machine Learning, metaheuristic, photovoltaic, solar energy, solar irradiance, solar power
Proper management of solar energy as an effective renewable source is of high importance toward sustainable energy harvesting. This paper offers a novel sophisticated method for predicting solar irradiance (SIr) from environmental conditions. To this end, an efficient metaheuristic technique, namely electromagnetic field optimization (EFO), is employed for optimizing a neural network. This algorithm quickly mines a publicly available dataset for nonlinearly tuning the network parameters. To suggest an optimal configuration, five influential parameters of the EFO are optimized by an extensive trial and error practice. Analyzing the results showed that the proposed model can learn the SIr pattern and predict it for unseen conditions with high accuracy. Furthermore, it provided about 10% and 16% higher accuracy compared to two benchmark optimizers, namely shuffled complex evolution and shuffled frog leaping algorithm. Hence, the EFO-supervised neural network can be a promising tool for th... [more]
Smarter Together: Progressing Smart Data Platforms in Lyon, Munich, and Vienna
Naomi Morishita-Steffen, Rémi Alberola, Baptiste Mougeot, Étienne Vignali, Camilla Wikström, Uwe Montag, Emmanuel Gastaud, Brigitte Lutz, Gerhard Hartmann, Franz Xaver Pfaffenbichler, Ali Hainoun, Bruno Gaiddon, Antonino Marvuglia, Maria Beatrice Andreucci
April 13, 2023 (v1)
Keywords: Big Data, data management system, lighthouse cities, smart city initiatives, urban modeling
In a context where digital giants are increasingly influencing the actions decided by public policies, smart data platforms are a tool for collecting a great deal of information on the territory and a means of producing effective public policies to meet contemporary challenges, improve the quality of the city, and create new services. Within the framework of the Smarter Together project, the cities of Lyon (France), Munich (Germany), and Vienna (Austria) have integrated this tool into their city’s metabolism and use it at different scales. Nevertheless, the principle remains the same: the collection (or even dissemination) of internal and external data to the administration will enable the communities, companies, not-for-profit organizations, and civic administrations to “measure” the city and identify areas for improvement in the territory. Furthermore, through open data logics, public authorities can encourage external partners to become actors in territorial action by using findings... [more]
Estimation Model of Total Energy Consumptions of Electrical Vehicles under Different Driving Conditions
Seyed Mahdi Miraftabzadeh, Michela Longo, Federica Foiadelli
April 13, 2023 (v1)
Keywords: Big Data, electric vehicles (EVs), energy consumption, energy demands, estimation model, smart grid
The ubiquitous influence of E-mobility, especially electrical vehicles (EVs), in recent years has been considered in the electrical power system in which CO2 reduction is the primary concern. Having an accurate and timely estimation of the total energy demand of EVs defines the interaction between customers and the electrical power grid, considering the traffic flow, power demand, and available charging infrastructures around a city. The existing EV energy prediction methods mainly focus on a single electric vehicle energy demand; to the best of our knowledge, none of them address the total energy that all EVs consume in a city. This situation motivated us to develop a novel estimation model in the big data regime to calculate EVs’ total energy consumption for any desired time interval. The main contribution of this article is to learn the generic demand patterns in order to adjust the schedules of power generation and prevent any electrical disturbances. The proposed model successfull... [more]
Optimizing Clinical Workflow Using Precision Medicine and Advanced Data Analytics
Kevin Zhai, Mohammad S. Yousef, Sawsan Mohammed, Nader I. Al-Dewik, M. Walid Qoronfleh
April 11, 2023 (v1)
Keywords: Artificial Intelligence, Big Data, clinical workflow, cloud computing, healthcare fusion, IMS, information management system, Machine Learning, medical records, patient-centered care, population health, precision medicine, precision prescription, ROBIN
Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 1... [more]
Algorithm for Customizing the Material Selection Process for Application in Power Engineering
Katarina Tomičić-Pupek, Ilija Srpak, Ladislav Havaš, Dunja Srpak
April 11, 2023 (v1)
Subject: Materials
Keywords: Ashby map, Big Data, material selection, power engineering, supply chain management
Disruptions in the global market are influencing value and supply chains reminding businesses and industries that variability and diversity of supply chains may be essential for surviving and sustainability. Operations management of any business has to address these challenges in order to avoid any serious interruptions in supply of materials in production industries by seeking substitute inputs. At the same time, the technological development offers new materials with similar quality properties, making thereby the substitute material search more difficult in terms of selecting appropriate materials with a level of quality which is similar enough. Another aspect in shifting can be found in more social-related reasons addressing changes in the value chains like traceability, low carbonization, and a more customer-oriented approach, because of moving towards green digital business. In this sense the intention of this work was to propose an algorithm for customizing the process of identif... [more]
Methods to Optimize Carbon Footprint of Buildings in Regenerative Architectural Design with the Use of Machine Learning, Convolutional Neural Network, and Parametric Design
Mateusz Płoszaj-Mazurek, Elżbieta Ryńska, Magdalena Grochulska-Salak
April 3, 2023 (v1)
Subject: Environment
Keywords: AI, Algorithms, Artificial Intelligence, Big Data, circular economy, computer vision, GHG emissions, life cycle assessment, Machine Learning, neural networks, Optimization, parametric, sustainable architecture
The analyzed research issue provides a model for Carbon Footprint estimation at an early design stage. In the context of climate neutrality, it is important to introduce regenerative design practices in the architect’s design process, especially in early design phases when the possibility of modifying the design is usually high. The research method was based on separate consecutive research works−partial tasks: Developing regenerative design guidelines for simulation purposes and for parametric modeling; generating a training set and a testing set of building designs with calculated total Carbon Footprint; using the pre-generated set to train a Machine Learning Model; applying the Machine Learning Model to predict optimal building features; prototyping an application for a quick estimation of the Total Carbon Footprint in the case of other projects in early design phases; updating the prototyped application with additional features; urban layout analysis; preparing a new approach based... [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.
Management Perspectives towards the Data-Driven Organization in the Energy Sector
Irina Bogdana Pugna, Dana Maria Boldeanu, Mirela Gheorghe, Gabriel Cozgarea, Adrian Nicolae Cozgarea
March 28, 2023 (v1)
Keywords: Big Data, big data analytics, data-driven organizational model, digitalization, Energy, EU Green Deal
This paper explores the current attitudes of managers and executives working in the energy sector towards the Data-Driven Organizational Model implied by Big Data. The aim is to explore and understand the current mindset of senior decision makers, since their success depends as much on cognitive and behavioral processes as on their technical competences. We adopt a grounded-theory approach, developing models of understanding and belief abductively, driven by the data obtained from participants through a reflection guide. We find that managers differ significantly in their understanding and engagement with their challenges; they display interest but differ in their commitment and enthusiasm; they identify a lack of strategy and skills as current barriers; and they are currently unwilling to trust data, treating evidence according to their own prior commitments. This is a significant barrier to establishing the Data-Driven Organizational Model. These findings raise concerns, and the pape... [more]
Extended Flow-Based Security Assessment for Real-Sized Transmission Network Planning
Maria Dicorato, Michele Trovato, Chiara Vergine, Corrado Gadaleta, Benedetto Aluisio, Giuseppe Forte
March 27, 2023 (v1)
Keywords: Big Data, distribution factors, flow-based procedure, N−1 security analysis, transmission network
The evolution of electric power systems involves several aspects, dealing with policy and economics as well as security issues. Moreover, due to the high variability of operating conditions, evolution scenarios have to be carefully defined. The aim of this paper is to propose a flow-based procedure for the preliminary security analysis of yearly network evolution scenarios at the real scale level. This procedure is based on hourly load and generation conditions given by market solutions, and exploits Power Transfer Distribution Factors and Line Outage Distribution Factors to determine N and N−1 conditions, properly accounting for possible islanding in the latter case. The analysis of overloads is carried out by dealing with big data analysis through statistic indicators, based on power system background, to draw out critical operating conditions and outages. The procedure is applied to a provisional model of a European high voltage network.
Big Data for Energy Management and Energy-Efficient Buildings
Vangelis Marinakis
March 24, 2023 (v1)
Keywords: Big Data, data-driven architecture, decision support, energy management, energy services, energy-efficient buildings
European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the pr... [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]
Exploratory Research of CO2, Noise and Metabolic Energy Expenditure in Lisbon Commuting
Angelo Soares, Cristina Catita, Carla Silva
March 22, 2023 (v1)
Subject: Environment
Keywords: Big Data, commuter exposure, indoor air quality, low-cost sensors, urban commuting
The lower cost of sensors is making possible the acquisition of big data sets in several applications and research areas. Indoor air quality and commuter exposure to pollutants are some of these areas, which can have impacts on our livelihood. The main objective of this exploratory research was to assemble portable equipment along with a prototype, one low-cost and easy to replicate in any location worldwide. We answer how CO2, noise and energy expenditure compare in different transportation modes with indoor environments (metro, bus and car). It was intended to be carried by a subject on all commutes. The low-cost equipment assembled has the ability to measure ambient CO2, noise levels, heart rate and geographic coordinates. The field campaign was conducted on an urban commuting route, in Lisbon city, between Rossio (downtown of Lisbon city) and Campo Grande (near FCUL campus). It took place during 3 weeks in school break and 3 weeks in the school period to grasp some differences betw... [more]
Sensor Data Compression Using Bounded Error Piecewise Linear Approximation with Resolution Reduction
Jeng-Wei Lin, Shih-wei Liao, Fang-Yie Leu
March 21, 2023 (v1)
Keywords: Big Data, bounded-error approximation, data compression, Internet of Things, piecewise linear, resolution reduction
Smart production as one of the key issues for the world to advance toward Industry 4.0 has been a research focus in recent years. In a smart factory, hundreds or even thousands of sensors and smart devices are often deployed to enhance product quality. Generally, sensor data provides abundant information for artificial intelligence (AI) engines to make decisions for these smart devices to collect more data or activate some required activities. However, this also consumes a lot of energy to transmit the sensor data via networks and store them in data centers. Data compression is a common approach to reduce the sensor data size so as to lower transmission energies. Literature indicates that many Bounded-Error Piecewise Linear Approximation (BEPLA) methods have been proposed to achieve this. Given an error bound, they make efforts on how to approximate to the original sensor data with fewer line segments. In this paper, we furthermore consider resolution reduction, which sets a new restri... [more]
IoT Solution for AI-Enabled PRIVACY-PREServing with Big Data Transferring: An Application for Healthcare Using Blockchain
Mohamed Elhoseny, Khalid Haseeb, Asghar Ali Shah, Irshad Ahmad, Zahoor Jan, Mohammed. I. Alghamdi
March 9, 2023 (v1)
Keywords: Big Data, constraint network, embedded applications, insecure channels, Internet of things
Internet of Things (IoT) performs a vital role in providing connectivity between computing devices, processes, and things. It significantly increases the communication facilities and giving up-to-date information to distributed networks. On the other hand, the techniques of artificial intelligence offer numerous and valuable services in emerging fields. An IoT-based healthcare solution facilitates patients, hospitals, and professionals to observe real-time and critical data. In the literature, most of the solution suffers from data intermission, high ethical standards, and trustworthiness communication. Moreover, network interruption with recurrent expose of sensitive and personal health data decreases the reliance on network systems. Therefore, this paper intends to propose an IoT solution for AI-enabled privacy-preserving with big data transferring using blockchain. Firstly, the proposed algorithm uses a graph-modeling to develop a scalable and reliable system for gathering and trans... [more]
Carbon-Responsive Computing: Changing the Nexus between Energy and Computing
Dawn Nafus, Eve M. Schooler, Karly Ann Burch
March 7, 2023 (v1)
Subject: Environment
Keywords: Big Data, carbon footprint, carbon intensity, data center, demand response, edge computing, linked data, mixed qualitative and quantitative methods, smart grid, social aspects of energy
While extensive research has gone into demand response techniques in data centers, the energy consumed in edge computing systems and in network data transmission remains a significant part of the computing industry’s carbon footprint. The industry also has not fully leveraged the parallel trend of decentralized renewable energy generation, which creates new areas of opportunity for innovation in combined energy and computing systems. Through an interdisciplinary sociotechnical discussion of current energy, computer science and social studies of science and technology (STS) literature, we argue that a more comprehensive set of carbon response techniques needs to be developed that span the continuum of data centers, from the back-end cloud to the network edge. Such techniques need to address the combined needs of decentralized energy and computing systems, alongside the social power dynamics those combinations entail. We call this more comprehensive range “carbon-responsive computing,” a... [more]
Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant
Seung-Mo Je, Hyeyoung Ko, Jun-Ho Huh
March 7, 2023 (v1)
Keywords: Big Data, big data virtualization, electric power production model, game theory, photovoltaic power plant, power generation systems, Python, R-Studio, renewable, web crawling
This paper has tried to execute accurate demand forecasting by utilizing big data visualization and proposes a flexible and balanced electric power production big data virtualization based on a photovoltaic power plant. First of all, this paper has tried to align electricity demand and supply as much as possible using big data. Second, by using big data to predict the supply of new renewable energy, an attempt was made to incorporate new and renewable energy into the current power supply system and to recommend an efficient energy distribution method. The first presented problem that had to be solved was the improvement in the accuracy of the existing electricity demand for forecasting models. This was explained through the relationship between the power demand and the number of specific words in the paper that use crawling by utilizing big data. The next problem arose because the current electricity production and supply system stores the amount of new renewable energy by changing the... [more]
Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners
Isabella Yunfei Zeng, Shiqi Tan, Jianliang Xiong, Xuesong Ding, Yawen Li, Tian Wu
March 6, 2023 (v1)
Keywords: Big Data, China, light-duty vehicle, Machine Learning, real-world fuel consumption rate
Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, name... [more]
Online System for Power Quality Operational Data Management in Frequency Monitoring Using Python and Grafana
Jose-María Sierra-Fernández, Olivia Florencias-Oliveros, Manuel-Jesús Espinosa-Gavira, Juan-José González-de-la-Rosa, Agustín Agüera-Pérez, José-Carlos Palomares-Salas
March 3, 2023 (v1)
Keywords: Big Data, dashboard, data acquisition, data exchange, data visualization, frequency measurement, frequency stability, GPS reference, Grafana-based lab, power quality
This article proposes a measurement solution designed to monitor the instantaneous frequency in power systems. It uses a data acquisition module and a GPS receiver for time stamping and traceability. A Python-based module receives data, computes the frequency, and finally transfers the measurement results to a database. The frequency is calculated with two different methods, which are compared in the article. The stored data is visualized using the Grafana platform, thus demonstrating its potential for comparing scientific data. The system as a whole constitutes an efficient, low-cost solution as a data acquisition system.
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