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Journal articles on the topic 'Electrical engineering-time series analysis'

Author: Grafiati

Published: 6 September 2023

Last updated: 31 July 2025

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1

Meena Chavan. "Time Series Analysis in Electrical Load Forecasting." Panamerican Mathematical Journal 35, no. 1s (2024): 210–19. http://dx.doi.org/10.52783/pmj.v35.i1s.2307.

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Time series analysis in electricity load projection is a key part of the energy sector because it makes it possible to control and distribute energy more efficiently. It is important to have accurate load forecasts in order to balance supply and demand, make power systems run more efficiently, and lower operating costs. This essay covers a wide range of ways that time series analysis can be used to guess how much electricity will be used. The study looks at a number of different models, such as the autoregressive integrated moving average (ARIMA), exponential smoothing, and machine learning me

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2

OSTERHAGE, HANNES, and KLAUS LEHNERTZ. "NONLINEAR TIME SERIES ANALYSIS IN EPILEPSY." International Journal of Bifurcation and Chaos 17, no. 10 (2007): 3305–23. http://dx.doi.org/10.1142/s0218127407019081.

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The framework of the theory of nonlinear dynamics provides powerful concepts and algorithms to study complicated dynamics such as brain electrical activity (electroencephalogram, EEG). Although different influencing factors render the use of nonlinear measures in a strict sense problematic, converging evidence from various investigations now indicates that nonlinear EEG analysis provides a means to reliably characterize different states of physiological and pathophysiological brain function. We here focus on applications of nonlinear EEG analysis in epileptology. Epilepsy affects more than 50

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3

Broersen, P. M. T., and S. de Waele. "Time series analysis in a frequency subband." IEEE Transactions on Instrumentation and Measurement 52, no. 4 (2003): 1054–60. http://dx.doi.org/10.1109/tim.2003.814823.

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4

Beňová, Mariana, Branislav Dobrucký, Jozef Šedo, et al. "A Novel Approach to Transient Fourier Analysis for Electrical Engineering Applications." Applied Sciences 14, no. 21 (2024): 9888. http://dx.doi.org/10.3390/app14219888.

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This paper presents a detailed investigation into the application of transient Fourier analysis in select electrical engineering contexts. Two novel approaches for addressing transient analysis are introduced. The first approach combines the Fourier series with the Laplace–Carson (L-C) transform in the complex domain, utilizing complex time vectors to streamline the computation of the original function. The inverse transformation back into the time domain is achieved using the Cauchy-Heaviside (C-H) method. The second approach applies the Fourier transform (F-Τ) for the transient analysis of a

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5

Petitjean, Francois, and Jonathan Weber. "Efficient Satellite Image Time Series Analysis Under Time Warping." IEEE Geoscience and Remote Sensing Letters 11, no. 6 (2014): 1143–47. http://dx.doi.org/10.1109/lgrs.2013.2288358.

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6

Voloshko, Anatolii V., Yaroslav S. Bederak, and Oleksandr A. Kozlovskyi. "AN IMPROVED PRE-FORECASTING ANALYSIS OF ELECTRICAL LOADS OF PUMPING STATION." Resource-Efficient Technologies, no. 4 (February 21, 2020): 20–29. http://dx.doi.org/10.18799/24056537/2019/4/265.

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Relevance of research. In order to reduce energy losses, an accurate and timely forecast of the amount of consumed electricity is necessary. Accurate forecasting of electrical loads of industrial enterprises and their divisions (productions, workshops, departments etc.) allows planning of normal operating conditions, concluding contracts for the electricity supply with the electricity supply company under more favorable conditions, and improving the electricity quality, which ultimately affects the final cost of the products produced by an enterprise. So far, more than 150 forecasting methods

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7

Voloshko, A. V., Ya S. Bederak, and O. A. Kozlovskyi. "AN IMPROVED PRE-FORECASTING ANALYSIS OF ELECTRICAL LOADS OF PUMPING STATION." Resource-Efficient Technologies, no. 4 (February 21, 2020): 20–29. http://dx.doi.org/10.18799/24056529/2019/4/265.

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Relevance of research. In order to reduce energy losses, an accurate and timely forecast of the amount of consumed electricity is necessary. Accurate forecasting of electrical loads of industrial enterprises and their divisions (productions, workshops, departments etc.) allows planning of normal operating conditions, concluding contracts for the electricity supply with the electricity supply company under more favorable conditions, and improving the electricity quality, which ultimately affects the final cost of the products produced by an enterprise. So far, more than 150 forecasting methods

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8

Stoffer, David S. "Time series analysis by state space models." Automatica 39, no. 5 (2003): 954–55. http://dx.doi.org/10.1016/s0005-1098(03)00027-x.

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9

Broersen, P. M. T., and R. Bos. "Time-Series Analysis if Data Are Randomly Missing." IEEE Transactions on Instrumentation and Measurement 55, no. 1 (2006): 79–84. http://dx.doi.org/10.1109/tim.2005.861247.

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10

A., D. Awasare. "Fourier Series Analysis in Engineering: Advancements and Applications." Journal of Applied Mathematics and Statistical Analysis 5, no. 2 (2024): 21–24. https://doi.org/10.5281/zenodo.11208733.

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<em>Fourier series analysis stands as a cornerstone in engineering, offering powerful insights into the representation and manipulation of periodic functions. This research paper delves into the advancements and applications of Fourier series analysis across various engineering domains. Through an in-depth exploration of relevant scholarly works, the paper highlights key methodologies, techniques, and case studies where Fourier series analysis has been effectively employed. The abstract examines the utilization of Fourier series in signal processing, where it serves as a fundamental tool for s

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11

Wang, Jiang, Li Sun, Xiangyang Fei, and Bing Zhu. "Chaos analysis of the electrical signal time series evoked by acupuncture." Chaos, Solitons & Fractals 33, no. 3 (2007): 901–7. http://dx.doi.org/10.1016/j.chaos.2006.01.040.

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12

Cuomo, V., G. Di Bello, V. Lapenna, M. Macchiato, and C. Serio. "Parametric time series analysis of extreme events in earthquake electrical precursors." Tectonophysics 262, no. 1-4 (1996): 159–72. http://dx.doi.org/10.1016/0040-1951(95)00212-x.

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13

West, Bruce J., Artur Maciejewski, Miroslaw Latka, et al. "Wavelet analysis of scaling properties of gastric electrical activity." Journal of Applied Physiology 101, no. 5 (2006): 1425–31. http://dx.doi.org/10.1152/japplphysiol.01364.2004.

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We present a novel approach to the analysis of fluctuations in human myoelectrical gastric activity measured noninvasively from the surface of the abdomen. The time intervals between successive maxima of the wavelet transformed quasi-periodic electrogastrographic waveform define the gastric rate variability (GRV) time series. By using the method of average wavelet coefficients, the statistical fluctuations in the GRV signal in healthy individuals are determined to scale in time. Such scaling was previously found in a variety of physiological phenomena, all of which support the hypothesis that

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14

Vázquez, J., E. Olías, A. Barrado, and J. Pleite. "Analysis of long time series of environmental electromagnetic field." Electronics Letters 39, no. 1 (2003): 125. http://dx.doi.org/10.1049/el:20030052.

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15

Luo, Yu, and Yulin Wang. "A Statistical Time-Frequency Model for Non-stationary Time Series Analysis." IEEE Transactions on Signal Processing 68 (2020): 4757–72. http://dx.doi.org/10.1109/tsp.2020.3014607.

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16

Sinha, N. K. "Recursive estimation and time-series analysis: An introduction." Automatica 22, no. 3 (1986): 388. http://dx.doi.org/10.1016/0005-1098(86)90042-7.

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17

Cong, L., G. Lu, Y. Chen, B. Deng, and X. Li. "Network congestion estimation using packet time series analysis." IET Communications 4, no. 8 (2010): 980. http://dx.doi.org/10.1049/iet-com.2009.0506.

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18

Abbasi, Ali Reza, Mohammad Reza Mahmoudi, and Mohammad Mehdi Arefi. "Transformer Winding Faults Detection Based on Time Series Analysis." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–10. http://dx.doi.org/10.1109/tim.2021.3076835.

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19

Malhotra, Akshay, Ioannis D. Schizas, and Vangelis Metsis. "Correlation Analysis-Based Classification of Human Activity Time Series." IEEE Sensors Journal 18, no. 19 (2018): 8085–95. http://dx.doi.org/10.1109/jsen.2018.2864207.

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20

Nayak, Nikhil, and Rujula Singh R. "5G Traffic Prediction with Time Series Analysis." International Journal of Innovative Technology and Exploring Engineering 10, no. 12 (2021): 36–40. http://dx.doi.org/10.35940/ijitee.l9555.10101221.

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In today’s day and age, a mobile phone has become a basic requirement needed for anyone to thrive. With the cellular traffic demand increasing so dramatically, it is now necessary to accurately predict the user traffic in cellular networks, to improve the performance in terms of resource allocation and utilization. Since traffic learning and prediction is a classical and appealing field, which still yields many meaningful results, there has been an increasing interest in leveraging Machine Learning tools to analyze the total traffic served in each region, to optimize the operation of the netwo

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21

Shi, Ying, Tao Yu, Qianjin Liu, Hanxin Zhu, Fusheng Li, and Yaxiong Wu. "An Approach of Electrical Load Profile Analysis Based on Time Series Data Mining." IEEE Access 8 (2020): 209915–25. http://dx.doi.org/10.1109/access.2020.3019698.

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22

Thomson, David J., and Frank L. Vernon. "Some Comments on the Analysis of “Big” Scientific Time Series." Proceedings of the IEEE 104, no. 11 (2016): 2220–49. http://dx.doi.org/10.1109/jproc.2016.2598218.

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23

Yan, Ruqiang, Yongbin Liu, and Robert Gao. "Correlation Dimension Analysis: A Non-linear Time Series Analysis for Data Processing." IEEE Instrumentation & Measurement Magazine 13, no. 6 (2010): 19–25. http://dx.doi.org/10.1109/mim.2010.5669609.

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24

Elger, Christian E., and Klaus Lehnertz. "Seizure prediction by non‐linear time series analysis of brain electrical activity." European Journal of Neuroscience 10, no. 2 (1998): 786–89. http://dx.doi.org/10.1046/j.1460-9568.1998.00090.x.

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25

Barzola-Monteses, Julio, Mónica Mite-León, Mayken Espinoza-Andaluz, Juan Gómez-Romero, and Waldo Fajardo. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case." Sustainability 11, no. 23 (2019): 6539. http://dx.doi.org/10.3390/su11236539.

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Electrical generation in Ecuador mainly comes from hydroelectric and thermo-fossil sources, with the former amounting to almost half of the national production. Even though hydroelectric power sources are highly stable, there is a threat of droughts and floods affecting Ecuadorian water reservoirs and producing electrical faults, as highlighted by the 2009 Ecuador electricity crisis. Therefore, predicting the behavior of the hydroelectric system is crucial to develop appropriate planning strategies and a good starting point for energy policy decisions. In this paper, we developed a time series

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26

Gorlov, M. I., A. V. Strogonov, and D. Yu Smirnov. "Transistor-degradation prediction by time-series analysis." Russian Microelectronics 35, no. 5 (2006): 337–44. http://dx.doi.org/10.1134/s106373970605009x.

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27

Serrano-Guerrero, Xavier, Guillermo Escrivá-Escrivá, Santiago Luna-Romero, and Jean-Michel Clairand. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles." Energies 13, no. 5 (2020): 1046. http://dx.doi.org/10.3390/en13051046.

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Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the re

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28

Jessica, Andres. "Autonomous numerical methods for solving electrical circuits, a taylor series-based approach." i-manager's Journal on Circuits and Systems 12, no. 2 (2024): 1. https://doi.org/10.26634/jcir.12.2.21724.

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Electrical circuit analysis is a fundamental aspect of engineering, requiring accurate and efficient computational methods for solving differential equations governing circuit behaviour. Traditional numerical methods, such as Euler's and Runge-Kutta approaches, have limitations in accuracy and computational efficiency. This paper explores an autonomous numerical approach using the Taylor Series Method, implemented in the TKSL simulation system. The study compares the Taylor Series Method with conventional numerical techniques, evaluating accuracy, computational complexity, and stability. The r

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29

Kozłowski, Bartosz. "Time series denoising with wavelet transform." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2005): 91–95. http://dx.doi.org/10.26636/jtit.2005.3.320.

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This paper concerns the possibilities of applying wavelet analysis to discovering and reducing distortions occurring in time series.Wavelet analysis basics are briefly reviewed. WaveShrink method including three most common shrinking variants (hard, soft, and non-negative garrote shrinkage functions) is described. Another wavelet-based filtering method, with parameters depending on the length of wavelets, is introduced. Sample results of filtering follow the descriptions of both methods. Additionally the results of the use of both filtering methods are compared. Examples in this paper deal onl

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30

Liu, Xu, S. Islam, A. A. Chowdhury, and D. O. Koval. "Tired of Continuous Time-Series Analysis or Calculations?" IEEE Industry Applications Magazine 16, no. 5 (2010): 59–65. http://dx.doi.org/10.1109/mias.2010.937447.

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31

Korenberg, M. J., and L. D. Paarmann. "Orthogonal approaches to time-series analysis and system identification." IEEE Signal Processing Magazine 8, no. 3 (1991): 29–43. http://dx.doi.org/10.1109/79.127999.

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32

Kobayashi, Shin-ichi, Yasuyuki Shirai, Kazuo Hiyane, Fumihiro Kumeno, Hiroshi Inujima, and Noriyoshi Yamauchi. "Time Series Analysis of Technology Trends based on the Internet Resources." IEEJ Transactions on Electronics, Information and Systems 125, no. 5 (2005): 720–29. http://dx.doi.org/10.1541/ieejeiss.125.720.

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33

Erdoğan, Hediye, and Niyazi Arslan. "Identification of vertical total electron content by time series analysis." Digital Signal Processing 19, no. 4 (2009): 740–49. http://dx.doi.org/10.1016/j.dsp.2008.07.002.

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34

Faridi, Muhammad Zahir, Hina Ali, Tahira Bano Qasim, and Maheen Sadaf. "Electrical Consumption in the Industrial Sector of Pakistan: A Structural Time Series Analysis." Review of Economics and Development Studies 8, no. 2 (2022): 203–9. http://dx.doi.org/10.47067/reads.v8i2.453.

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The usage of energy is the central factor in promoting the contribution of the industrial sector economy to develop the economy. Indeed, Pakistan is a growing but recently united country with severe shortages of electricity resources. Therefore, the industry has a growing quantity of growth that leads to economic progress and also prioritizes inevitable challenges to stimulate industrial growth. The purpose of the study is to investigate the relationship between electrical consumption, oil consumption, and gas usage in manufacturing and its impact on the economic growth in Pakistan. Results de

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35

Alonso-Gonzalez, Alberto, Carlos Lopez-Martinez, Konstantinos P. Papathanassiou, and Irena Hajnsek. "Polarimetric SAR Time Series Change Analysis Over Agricultural Areas." IEEE Transactions on Geoscience and Remote Sensing 58, no. 10 (2020): 7317–30. http://dx.doi.org/10.1109/tgrs.2020.2981929.

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36

Aggarwal, Akarsh, Mohammed Alshehri, Manoj Kumar, Osama Alfarraj, Purushottam Sharma, and Kamal Raj Pardasani. "Landslide data analysis using various time-series forecasting models." Computers & Electrical Engineering 88 (December 2020): 106858. http://dx.doi.org/10.1016/j.compeleceng.2020.106858.

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37

Chen, Xiangrong, Yang Xu, and Xiaolong Cao. "Nonlinear time series analysis of partial discharges in electrical trees of XLPE cable insulation samples." IEEE Transactions on Dielectrics and Electrical Insulation 21, no. 4 (2014): 1455–61. http://dx.doi.org/10.1109/tdei.2014.004307.

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38

Riccio, Christian, Angelo Martone, Gaetano Zazzaro, and Luigi Pavone. "Training Datasets for Epilepsy Analysis: Preprocessing and Feature Extraction from Electroencephalography Time Series." Data 9, no. 5 (2024): 61. http://dx.doi.org/10.3390/data9050061.

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We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven

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39

Ding, Xue Cheng, Zheng You He, and Min Yu. "Traction Substation Electrical Main Connection Reliability Analysis." Advanced Materials Research 433-440 (January 2012): 7293–99. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7293.

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Traction substation reliability is of vital importance for railway transportation safety. To illustrate traction substation reliability, irreparable and reparable reliability models of three types of traction substation Electrical main connection have been established. Based on analysis of simple series and parallel reliability system, system irreparable reliability model is analyzed. The ways of how to get mean time to failure (MTTF) and mean time to first failure (MTTFF) of reparable system are achieved. By comparative analysis of the value of MTTF and MTTFF among three kinds of traction sub

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40

Feng, Jing. "Analysis of Driving Factors of Innovation and Entrepreneurship Based on Time Series Analysis." Journal of Sensors 2021 (October 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/8427336.

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In order to analyze the driving factors of innovation and entrepreneurship, based on the time series analysis algorithm, this paper combines the analysis requirements of innovation and entrepreneurship driving factors to improve the time series, uses decomposition methods to decompose the complex original data into relatively simple components and reconstruct them, and predicts the reconstructed components to integrate the final predicted value. Moreover, this paper introduces entrepreneurial attitude as an intermediary variable and verifies it through data collection and statistical analysis,

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41

Poskitt, D. S., and Shin-Ho Chung. "Markov chain models, time series analysis and extreme value theory." Advances in Applied Probability 28, no. 2 (1996): 405–25. http://dx.doi.org/10.2307/1428065.

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Markov chain processes are becoming increasingly popular as a means of modelling various phenomena in different disciplines. For example, a new approach to the investigation of the electrical activity of molecular structures known as ion channels is to analyse raw digitized current recordings using Markov chain models. An outstanding question which arises with the application of such models is how to determine the number of states required for the Markov chain to characterize the observed process. In this paper we derive a realization theorem showing that observations on a finite state Markov

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42

Poskitt, D. S., and Shin-Ho Chung. "Markov chain models, time series analysis and extreme value theory." Advances in Applied Probability 28, no. 02 (1996): 405–25. http://dx.doi.org/10.1017/s0001867800048552.

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Markov chain processes are becoming increasingly popular as a means of modelling various phenomena in different disciplines. For example, a new approach to the investigation of the electrical activity of molecular structures known as ion channels is to analyse raw digitized current recordings using Markov chain models. An outstanding question which arises with the application of such models is how to determine the number of states required for the Markov chain to characterize the observed process. In this paper we derive a realization theorem showing that observations on a finite state Markov

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43

Duan, Li, Jianxian Cai, Juan Liang, Danqi Chen, and Xiaoye Sun. "Identification and Analysis of Non-Stationary Time Series Signals Based on Data Preprocessing and Deep Learning." Traitement du Signal 39, no. 5 (2022): 1703–9. http://dx.doi.org/10.18280/ts.390528.

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Deep learning is not the most accurate way for recognizing time series signals, and it is unable to identify non-stationary time series signals with numerous chaotic classes. Moreover, the signal detection benefits from data preprocessing have gone unnoticed. Therefore, this paper investigates the detection and analysis of non-stationary time series signals using deep learning and data preprocessing. The fitting model of the historical stationarity index is built based on the Gaussian mixture model of single Gaussian models, and the change point of the non-stationary time series signal is dete

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44

Nickerson, Paul V., Raheleh Baharloo, Amal A. Wanigatunga, Todd M. Manini, Patrick J. Tighe, and Parisa Rashidi. "Transition Icons for Time-Series Visualization and Exploratory Analysis." IEEE Journal of Biomedical and Health Informatics 22, no. 2 (2018): 623–30. http://dx.doi.org/10.1109/jbhi.2017.2704608.

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45

Petrov, L., P. Lewin, and Tadeusz Czaszejko. "On the applicability of nonlinear time series methods for partial discharge analysis." IEEE Transactions on Dielectrics and Electrical Insulation 21, no. 1 (2014): 284–93. http://dx.doi.org/10.1109/tdei.2014.6740751.

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46

Chen, Ying, and Xiang Jie Chen. "Analysis for TCSC Steady-State Characteristics." Advanced Materials Research 179-180 (January 2011): 1435–40. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1435.

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When the TCSC steady-state operation, the thyristor turn-on and turn-off time is definite, the changing for TCSC electric capacity voltage and thyristor electric current is with the periodicity and symmetry.Thyristor controlled series compensation technology is fixed series compensation technology foundation, which is meet the needs for adaptation electrical power system operation control developing. With changes the triggering angle for thyristor suitably, then can realize the TCSC equivalent reactance fast, continuously and adjusts smoothly, provides the controllable series compensation for

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47

Mamala, A., and W. Sciężor. "Evaluation of the Effect of Selected Alloying Elements on the Mechanical and Electrical Aluminium Properties." Archives of Metallurgy and Materials 59, no. 1 (2014): 413–17. http://dx.doi.org/10.2478/amm-2014-0069.

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Abstract Modern industry expects aluminum products with new, unusual, and well-defined functional properties. Last years we are able to notice constant development of aluminium alloys. In food industry, power engineering, electrical engineering and building engineering, flat rolled products of 1XXX series aluminium alloys are used.8XXX series alloys registered in Aluminium Association during last 20 years may be used as an alternative. These alloys have very good thermal and electrical conductivity and perfect technological formability. Moreover, these materials are able to obtain by aluminium

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48

Ozawa, Kazuhiro, ’Takahide Niimura, and Tomoaki Nakashima. "Fuzzy Time-Series Model of Electric Power Consumption." Journal of Advanced Computational Intelligence and Intelligent Informatics 4, no. 3 (2000): 188–94. http://dx.doi.org/10.20965/jaciii.2000.p0188.

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In this paper, the authors present a data analysis and estimation procedure of electrical power consumption under uncertain conditions. Tiraditional methods are based on statistical and probabilistic approaches but it may not be quite suitable to apply purely stochastic models to the data generated by human activities such as the power consumption. The authors introduce a new approach based on possibility theory and fuzzy autoregression, and apply it to the analysis of time-series data of electric power consumption. Two models, which are different in complexity, are presented, and the performa

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49

Lin, Bor-Ren, and Chien-Lan Huang. "Analysis of series resonant converter with series–parallel connection." International Journal of Electronics 98, no. 2 (2011): 249–62. http://dx.doi.org/10.1080/00207217.2010.520150.

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50

Gülal, Engin, Hediye Erdoğan, and İbrahim Tiryakioğlu. "Research on the stability analysis of GNSS reference stations network by time series analysis." Digital Signal Processing 23, no. 6 (2013): 1945–57. http://dx.doi.org/10.1016/j.dsp.2013.06.014.

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