Evolution of knowledge mining from data in power systems: The Big Data Analytics breakthrough

Dominguez, Xavier; Prado, Alvaro; Arboleya, Pablo; Terzija, Vladimir

doi:10.1016/j.epsr.2023.109193

Cited by 15 publications

(4 citation statements)

References 82 publications

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“…Data Mining (DM) & Decision Tree (CART) Data mining is an iterative process rather than a linear one. Its main objectives are to describe, predict, and prescribe (Dominguez et al 2023). The concept of classi cation and regression trees was introduced by Braiman.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Bridge Damage Factors Using Decision Tree Technique (Case Study: Concrete Rad Bridges in ZANJAN Province-IRAN)

Medghalchi,

Rashvad,

Delnavaz

2023

Preprint

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The current management system for concrete road bridges in Iran is primarily based on a descriptive model, which assesses the condition of bridge elements and assigns grades based on the level of deterioration. However, there have been concerns raised about the use of descriptive statistics when analyzing large amounts of data. In order to address these concerns, this research incorporates the data mining method known as decision tree analysis. The decision tree analysis involves in-depth examination of data, including objective observations by experts and calculations of damage indices. We have introduced this method to replace descriptive analysis in his research. To conduct this study, various data such as physical characteristics, atmospheric and climatic conditions, traffic conditions, and deterioration indices (derived from established formulas and tables) of concrete bridges with spans exceeding 6 meters on a main 5-axis road in Zanjan province were collected. The decision tree method, along with Rapid Miner software, was utilized to model and analyze this data. The research findings indicate that the decision tree method demonstrates a high level of accuracy in predicting and prioritizing factors that contribute to bridge damage, surpassing the capabilities of conventional models. Specifically, the study reveals that the percentage of heavy vehicles passing over a bridge is the most significant factor influencing its damage. Furthermore, it predicts that bridges with a percentage exceeding 19.5% would fall into the category of heavily damaged bridges.

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Section: Literature Reviewmentioning

confidence: 99%

Prediction of Bridge Damage Factors Using Decision Tree Technique (Case Study: Concrete Rad Bridges in ZANJAN Province-IRAN)

Medghalchi,

Rashvad,

Delnavaz

2023

Preprint

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“…In general, the IoE paradigm comprises billions of intelligent Wireless Sensor Nodes (WSNs), which measure different variables related to the power system behavior [ 3 ]. Nevertheless, additional information in the literature highlights concerns associated with large-scale IoE deployments within Wireless Sensor Networks [ 4 ]. For instance, wireless networked sensors pose issues such as elevated power consumption across a large set of sensors, ongoing maintenance limitations, and a shortened lifespan [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Triboelectric Nanogenerators for Electric Field Energy Harvesting

Menéndez,

Villacrés,

Prado

et al. 2024

Sensors

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Electric-field energy harvesters (EFEHs) have emerged as a promising technology for harnessing the electric field surrounding energized environments. Current research indicates that EFEHs are closely associated with Tribo-Electric Nano-Generators (TENGs). However, the performance of TENGs in energized environments remains unclear. This work aims to evaluate the performance of TENGs in electric-field energy harvesting applications. For this purpose, TENGs of different sizes, operating in single-electrode mode were conceptualized, assembled, and experimentally tested. Each TENG was mounted on a 1.5 HP single-phase induction motor, operating at nominal parameters of 8 A, 230 V, and 50 Hz. In addition, the contact layer was mounted on a linear motor to control kinematic stimuli. The TENGs successfully induced electric fields and provided satisfactory performance to collect electrostatic charges in fairly variable electric fields. Experimental findings disclosed an approximate increase in energy collection ranging from 1.51% to 10.49% when utilizing TENGs compared to simple EFEHs. The observed correlation between power density and electric field highlights TENGs as a more efficient energy source in electrified environments compared to EFEHs, thereby contributing to the ongoing research objectives of the authors.

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“…As aforementioned, nowadays in the Big Data Era data are rapidly and continuously generated forming large datasets from many heterogeneous sources (MOHAN; CHAUDHURY;LALL, 2022) and it is processed to identify trends that can be used to support planning and decision-making processes (DOMINGUEZ et al, 2023). However, it is quite common for these datasets to have a considerable amount of missing data from many different reasons (SANTOS; Nunes da Silva;BESSANI, 2022;GUPTA, 2020;HOQUE, 2020), e.g., power source failures (ALEXOPOULOS; KALALAS;KORRES, 2020), environmental factors (JEONG; PARK;KO, 2021), missing evidence in scientific experiments (SULLIVAN et al, 2017), transmission network failure (ALEXOPOULOS; KALALAS;KORRES, 2020), human error (AGHAKHANI; ALHAJJ;CHANG, 2014), sensors failures (AGBO et al, 2022), among others.…”

Section: Data Imputationmentioning

confidence: 99%

“…These ordered sets of measurements over time are called time series (LUBBA et al, 2019). With this massive amount of data availability and given that discovering knowledge from data is always a subject of great interest and importance both in academic and industry (ZHONG; ZHANG, 2022;CAO et al, 2022;SIDDIQA et al, 2016), a set of theories and computational tools have been proposed and used to extract useful information from time series to assist in decision making in different areas (DOMINGUEZ et al, 2023). Searching on the Scopus database for documents that have the terms "knowledge", "from" and "data" in the title, abstract, or keywords, the result returned 391849 papers published between 2015 and 2023 in different subject areas.…”

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confidence: 99%

Evolving discrete dynamic bayesian networks: an approach for time series

Santos

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Knowledge discovery in time series datasets is a subject of great interest and importance in academics and industry. For such purpose, a set of theories and computational tools have been proposed and used to extract useful information from time series to assist in decision-making in different areas. Among the possibilities, Bayesian network is a probabilistic graphical model representing a set of random variables and their conditional statistical dependencies via a directed acyclic graph (DAG). This doctoral research proposes a methodology for dealing with time series based on evolving discrete Dynamic Bayesian Networks (EDBN) by an analytical threshold for selecting directed edges by the occurrence frequency as new datasets are collected. In this proposal, as new datasets are collected, the algorithm learns the structure of a DBN by using a score metric and the hill-climbing method and then uses the analytical threshold for selecting the directed edges between the nodes by the occurrence frequency. The developed method smoothly converges to a robust model and constantly adapts to the arrival of new data, obtaining more reliable network models. The discrete model is chosen to be a non-parametric approach that can be adequate for different data behaviour without manual modifications, i.e., totally data-driven. The proposal was evaluated by dealing with real datasets of time series in data imputation and CO 2 emissions forecasting during energy generation, which are two contexts that have received a lot of attention from researchers in recent years. Evaluating the results against widely used imputation methods, the proposed approach proved capable of handling data imputation in time series datasets for missing completely at random and for missing not at random. In the context of CO 2 emissions forecasting in multi-source power generation systems, real datasets of Belgium, Germany, Portugal, and Spain were used. The proposed approach showed to be capable of dealing with CO 2 emissions forecasting in the systems evaluated in this study. Comparing the results against a traditional DBN that not evolve the structure over time, the proposal developed was superior highlighting a contribution of performance improvement. The proposed method was also better when compared to other traditional methods. Moreover, the model also is computationally efficient, making the proposal a good option for embedding such an approach for dealing with time series in online applications.

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Evolution of knowledge mining from data in power systems: The Big Data Analytics breakthrough

Cited by 15 publications

References 82 publications

Prediction of Bridge Damage Factors Using Decision Tree Technique (Case Study: Concrete Rad Bridges in ZANJAN Province-IRAN)

Prediction of Bridge Damage Factors Using Decision Tree Technique (Case Study: Concrete Rad Bridges in ZANJAN Province-IRAN)

Assessment of Triboelectric Nanogenerators for Electric Field Energy Harvesting

Evolving discrete dynamic bayesian networks: an approach for time series

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