Relevant bibliographies by topics / Electrical Load Pattern

Academic literature on the topic 'Electrical Load Pattern'

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Published: 14 March 2023

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Journal articles on the topic "Electrical Load Pattern"

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Satriawan, I. Made, I. Made Mataram, and A. A. Ngurah Amrita. "PERAMALAN BEBAN LISTRIK JANGKA PENDEK MENGGUNAKAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DI GARDU INDUK NUSA DUA BALI." Jurnal SPEKTRUM 7, no. 1 (March 7, 2020): 83. http://dx.doi.org/10.24843/spektrum.2020.v07.i01.p12.

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Electric load in Nusa Dua Bali has increased from 2013-2017 by an average of 11.83%. The increase in electric load requires the electrical energy service provider to be able to adjust the electricity demand and be able to increase its reliability, The effort that can be done is to predict the electric load. Electric load forecasting can be done by various methods, ANFISo (Adaptiveo Neuroo Fuzzyo Inferenceo Systemo) is one method that is often used in forecasting electrical loads. ANFIS is able to explain the reasoning process and do data learning. The data used are the electric load, temperature, humidity and time, the data was chosen because changes in temperature and humidity affect people's habitual patterns in using air conditioners (electric load patterns). The electric load pattern is trained 100 times using ANFIS with the type of membership function is trimf, and [3 3 3 3] is the number of membership function. The indicator to determining the accuracy of the electrical load forecasting pattern results with the real electric load pattern used the MAPE (Mean Absolute Percentage Error) value, which the MAPE standard value that good is less than 10%. The test results from this study produced a MAPE value of 6.98%.
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Micu, Marian Bogdan, Maricel Adam, and Mihai Andruscă. "Nonintrusive Electrical Loads Pattern Determination." Bulletin of the Polytechnic Institute of Iași. Electrical Engineering, Power Engineering, Electronics Section 67, no. 1 (March 1, 2021): 65–74. http://dx.doi.org/10.2478/bipie-2021-0005.

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Abstract The paper presents a possibility to determine the electrical patterns for the electrical loads through nonintrusive monitoring of their operating regimes. The electrical patterns are determined on the basis of the electrical parameters acquired for each load from the electrical network analysed. The determination of the electrical patterns is useful for the management of electrical energy consumption. The easiness of the nonintrusive monitoring technique is determined by the possibility of acquiring the electrical parameters from a single measurement point from the electrical network. From the electrical parameters acquired can be obtained information for electrical loads consumption recognition and their operating regimes, for certain time intervals, and it can be established the technical condition for each load.
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Wu, Sheng, and Kwok L. Lo. "Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic." Processes 8, no. 11 (October 30, 2020): 1385. http://dx.doi.org/10.3390/pr8111385.

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Non-intrusive load monitoring is a vital part of an overall load management scheme. One major disadvantage of existing non-intrusive load monitoring methods is the difficulty to accurately identify loads with similar electrical characteristics. To overcome the various switching probability of loads with similar characteristics in a specific time period, a new non-intrusive load monitoring method is proposed in this paper which will modify monitoring results based on load switching probability distribution curve. Firstly, according to the addition theorem of load working currents, the complex current is decomposed into the independently working current of each load. Secondly, based on the load working current, the initial identification of load is achieved with current frequency domain components, and then the load switching times in each hour is counted due to the initial identified results. Thirdly, a back propagation (BP) neural network is trained by the counted results, the switching probability distribution curve of an identified load is fitted with the BP neural network. Finally, the load operation pattern is profiled according to the switching probability distribution curve, the load operation pattern is used to modify identification result. The effectiveness of the method is verified by the measured data. This approach combines the operation pattern of load to modify the identification results, which improves the ability to identify loads with similar electrical characteristics.
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Chicco, G., and I. S. Ilie. "Support Vector Clustering of Electrical Load Pattern Data." IEEE Transactions on Power Systems 24, no. 3 (August 2009): 1619–28. http://dx.doi.org/10.1109/tpwrs.2009.2023009.

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Mado, Ismit, Antonius Rajagukguk, Aris Triwiyatno, and Arif Fadllullah. "Short-Term Electricity Load Forecasting Model Based DSARIMA." International Journal of Electrical, Energy and Power System Engineering 5, no. 1 (February 28, 2022): 6–11. http://dx.doi.org/10.31258/ijeepse.5.1.6-11.

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Forecasting short-term electrical load is very important so that the quality of the electrical power supplied can be maintained properly. The study was conducted to measure the results of electrical load forecasting based on parameter estimates and the presentation of time series data. It is important to manage stationary data, both in terms of mean and variance. Data presentation is done by determining the value of variance through the Box-Cox transformation method and the mean value based on the ACF and PACF plots. This study considers the pattern of electricity consumption which contains double seasonal patterns. The results of previous studies show the electric power prediction model, the DSARIMA model with a MAPE of 2.06%. The condition of the model used to predict the electrical load still has a tendency not to be normally distributed and it is estimated that there are outliers. Improvements to the AR and MA parameters that meet the standard error tolerance value of 5 percent are increased in this study. The results showed improvement of parameters to predict electrical load with DSARIMA model. The significance of this study was obtained by the MAPE value of 1.56 percent when compared to the actual data.
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TABARES-OSPINA, HÉCTOR A., and MAURICIO OSORIO. "CHARACTERIZATION OF THE RESISTIVE AND INDUCTIVE LOADS OF AN ENERGY DISTRIBUTION SYSTEM WITH JULIA FRACTAL SETS." Fractals 28, no. 05 (August 2020): 2050082. http://dx.doi.org/10.1142/s0218348x20500826.

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The present paper characterizes the resistive and inductive loads of an electric distribution system by Julia fractal sets, in order to discover other observations enabling the elevation of new theoretical approaches. The result shows that indeed the electrical load reflects a clear graphic pattern in the fractal space of the Julia sets. This result, then, is a new contribution that extends the universal knowledge about fractal geometry.
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Dakhole, Jayash M. "Utilization of Electrical Vehicle Power to Different Load." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 30, 2021): 4751–54. http://dx.doi.org/10.22214/ijraset.2021.35646.

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When the power is in control of its own fleet of vehicles, the power grid will experience an increase in the amount of fluctuating energy consumption depending on the nature of the load presentation. Depending on the drawing density, electric batteries can be integrated to create a new volume of the overall load profile can increase the voltage of the tips. Fees and charges the pattern is not random, as they can affect the driver's travel habits and charging capabilities, which means that ANY integration as well as a significant impact will have cargo. An increasing number of loads and peaks in load may lead to the need to upgrade the network infrastructure in order to reduce the risk of loss, abandoned services and or damage to any components. But with well-designed incentives for users, the HOME variable that is in the electric vehicle charging (EVC) - based power consumption can be flexible load, which can help in the energy system load and reduce charging at the tips.
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Wang, Zengping, Bing Zhao, Haibo Guo, Lingling Tang, and Yuexing Peng. "Deep Ensemble Learning Model for Short-Term Load Forecasting within Active Learning Framework." Energies 12, no. 20 (October 9, 2019): 3809. http://dx.doi.org/10.3390/en12203809.

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Short term load forecasting (STLF) is one of the basic techniques for economic operation of the power grid. Electrical load consumption can be affected by both internal and external factors so that it is hard to forecast accurately due to the random influencing factors such as weather. Besides complicated and numerous internal patterns, electrical load shows obvious yearly, seasonal, and weekly quasi-periodicity. Traditional regression-based models and shallow neural network models cannot accurately learn the complicated inner patterns of the electrical load. Long short-term memory (LSTM) model features a strong learning capacity to capture the time dependence of the time series and presents the state-of-the-art performance. However, as the time span increases, LSTM becomes much harder to train because it cannot completely avoid the vanishing gradient problem in recurrent neural networks. Then, LSTM models cannot capture the dependence over large time span which is of potency to enhance STLF. Moreover, electrical loads feature data imbalance where some load patterns in high/low temperature zones are more complicated but occur much less often than those in mild temperature zones, which severely degrades the LSTM-based STLF algorithms. To fully exploit the information beneath the high correlation of load segments over large time spans and combat the data imbalance, a deep ensemble learning model within active learning framework is proposed, which consists of a selector and a predictor. The selector actively selects several key load segments with the most similar pattern as the current one to train the predictor, and the predictor is an ensemble learning-based deep learning machine integrating LSTM and multi-layer preceptor (MLP). The LSTM is capable of capturing the short-term dependence of the electrical load, and the MLP integrates both the key history load segments and the outcome of LSTM for better forecasting. The proposed model was evaluated over an open dataset, and the results verify its advantage over the existing STLF models.
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Jiang, Zigui, Rongheng Lin, and Fangchun Yang. "An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System." Sensors 21, no. 19 (September 28, 2021): 6466. http://dx.doi.org/10.3390/s21196466.

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The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.
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Rajabi, Amin, Mohsen Eskandari, Mojtaba Jabbari Ghadi, Li Li, Jiangfeng Zhang, and Pierluigi Siano. "A comparative study of clustering techniques for electrical load pattern segmentation." Renewable and Sustainable Energy Reviews 120 (March 2020): 109628. http://dx.doi.org/10.1016/j.rser.2019.109628.

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Dissertations / Theses on the topic "Electrical Load Pattern"

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Leeb, Steven B. "A conjoint pattern recognition approach to nonintrusive load monitoring." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/12607.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1993.
Includes bibliographical references (leaves 152-162).
by Steven Bruce Leeb.
Ph.D.
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Zhou, Enwang. "Evolutionary intelligent systems for pattern classification and price based electric load forecasting applications." Ann Arbor, Mich. : ProQuest, 2007. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3258041.

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Thesis (Ph.D. in Electrical Engineering)--S.M.U., 2007.
Title from PDF title page (viewed Mar. 18, 2008). Source: Dissertation Abstracts International, Volume: 68-03, Section: B, page: 1852. Adviser: Alireza Khotanzad. Includes bibliographical references.
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Zhong, Shiyin. "Electricity Load Modeling in Frequency Domain." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/75109.

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In today's highly competitive and deregulated electricity market, companies in the generation, transmission and distribution sectors can all benefit from collecting, analyzing and deep-understanding their customers' load profiles. This strategic information is vital in load forecasting, demand-side management planning and long-term resource and capital planning. With the proliferation of Advanced Metering Infrastructure (AMI) in recent years, the amount of load profile data collected by utilities has grown exponentially. Such high-resolution datasets are difficult to model and analyze due to the large size, diverse usage patterns, and the embedded noisy or erroneous data points. In order to overcome these challenges and to make the load data useful in system analysis, this dissertation introduces a frequency domain load profile modeling framework. This framework can be used a complementary technology alongside of the conventional time domain load profile modeling techniques. There are three main components in this framework: 1) the frequency domain load profile descriptor, which is a compact, modular and extendable representation of the original load profile. A methodology was introduced to demonstrate the construction of the frequency domain load profile descriptor. 2) The load profile Characteristic Attributes in the Frequency Domain (CAFD). Which is developed for load profile characterization and classification. 3) The frequency domain load profile statistics and forecasting models. Two different models were introduced in this dissertation: the first one is the wavelet load forecast model and the other one is a stochastic model that incorporates local weather condition and frequency domain load profile statistics to perform medium term load profile forecast. 7 different utilities load profile data were used in this research to demonstrate the viability of modeling load in the frequency domain. The data comes from various customer classes and geographical regions. The results have shown that the proposed framework is capable to model the load efficiently and accurately.
Ph. D.
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Du, Liang. "Advanced classification and identification of plugged-in electric loads." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50321.

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The total electricity consumption of plugged-in electric loads (PELs) currently accounts for more usage than any other single end-use service in residential and commercial buildings. Compared with other categories of electric loads, PELs possess significant potential to be efficiently controlled and managed in buildings. Therefore, accurate and reliable PEL identification methods that are used to collect identity and performance information are desired for many purposes. However, few existing electric load identification methods are designed for PELs to handle unique challenges such as the diversity within each type of PEL and similarity between different types of PELs equipped by similar front-end power supply units. The objective of this dissertation is to develop non-intrusive, accurate, robust, and applicable PEL identification algorithms utilizing voltage and current measurements. Based on the literature review of almost all existing features that describe electric loads and five types of existing methods for electric load identification, a two-level framework for PELs classification and identification is proposed. First, the supervised self-organizing map (SSOM) is adopted to classify a large number of PELs of different models and brands into several groups by their inherent similarities. Therefore, PELs with similar front-end power supply units or characteristics fall into the same group. The partitioned groups are verified by their power supply unit topology. That is, different groups should have different topologies. This dissertation proposes a novel combination of the SSOM framework and the Bayesian framework. Such a hybrid identifier can provide the probability of an unknown PEL belonging to a specific type of load. Within each classified group by the SSOM, both static and dynamic methods are proposed to distinguish PELs with similar characteristics. Static methods extract steady-state features from the voltage and current waveforms to train different computational intelligence algorithms such as the SSOM itself and the support vector machine (SVM). An unknown PEL is then presented to the trained algorithm for identification. In contrast to static methods, dynamic methods take into consideration the dynamics of long-term (minutes instead of milliseconds) waveforms of PELs and extract elements such as spikes, oscillations, steady-state operations, as well as similarly repeated patterns.
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Asad, Dilan. "Analysis of Patterns in Data for Increased Understanding of Residential Loads." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-214752.

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The world community aims toward lowering thedependency of energy from fossil fuels, due to its negative impacton the environment. An increasing share of power from renewableenergy sources in the power grid causes a challenge withinstability in the grid. Applying data mining techniques on datafrom smart meters can recover patterns and information aboutresidents power consumption. Acquiring a greater understandingof residents power consumption support decision makers topropose adequate solutions to the challenge.This project uses a dataset from smart meters collected fromhouseholds in Sweden. The data is analyzed with the clusteringalgorithm K-Means++, to group the loads into similar patterns.Data analysis techniques are applied on the resulting clustersto analyze the household attributes in the clusters. Searchingfor certain household attributes that might be associated with aspecific type of load pattern.The results of the analysis imply that there are reoccurringload patterns. The results also showed that the load patternsduring extreme temperatures deviates from the load patterns inother temperatures. Analysis of the load patterns showed thatthese patterns are not necessarily associated with a specific setof household attributes.
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Farinaccio, Linda. "The disaggregation of whole-house electric load into the major end-uses using a rule-based pattern recognition algorithm." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0001/MQ43647.pdf.

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Book chapters on the topic "Electrical Load Pattern"

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Lezama, Fernando, Ansel Y. Rodríguez, Enrique Muñoz de Cote, and Luis Enrique Sucar. "Electrical Load Pattern Shape Clustering Using Ant Colony Optimization." In Applications of Evolutionary Computation, 491–506. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31204-0_32.

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Liu, LiQing, QiaoLin Ding, TieFeng Zhang, and JinBao Sun. "Power Load Pattern Recognition Method Based on FCM and Decision Tree." In Lecture Notes in Electrical Engineering, 345–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27287-5_55.

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Flasiński, Mariusz, Janusz Jurek, and Tomasz Peszek. "Application of Syntactic Pattern Recognition Methods for Electrical Load Forecasting." In Advances in Intelligent Systems and Computing, 599–607. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26227-7_56.

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Flasiński, Mariusz, Janusz Jurek, and Tomasz Peszek. "Parallel Processing Model for Syntactic Pattern Recognition-Based Electrical Load Forecast." In Parallel Processing and Applied Mathematics, 338–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-55224-3_32.

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Liu, LiQing, QiaoLin Ding, TieFeng Zhang, and Jian Chen. "Comparison of Two Kinds of Distance in Research on the Method of the Extraction of Load Pattern." In Lecture Notes in Electrical Engineering, 331–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27287-5_52.

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Nida-e-Falak and M. M. Tripathi. "Hybrid Forecasting Model Based on Nonlinear Auto-Regressive Exogenous Network, Fourier Transform, Self-organizing Map and Pattern Recognition Model for Hour Ahead Electricity Load Forecasting." In Lecture Notes in Electrical Engineering, 89–108. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6840-4_8.

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Jurek, Janusz. "Towards a Model of Semi-supervised Learning for the Syntactic Pattern Recognition-Based Electrical Load Prediction System." In Parallel Processing and Applied Mathematics, 533–43. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78024-5_46.

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Alhaj Ali, Alabbas, Doina Logofătu, Prachi Agrawal, and Sreshtha Roy. "Household Electric Load Pattern Consumption Enhanced Simulation by Random Behavior." In Intelligent Information and Database Systems, 290–302. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14799-0_25.

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Monetti, Valentina, Enrico Fabrizio, and Marco Filippi. "Influence of Different Temperature Control Patterns Through TRV on District Heating Loads." In Lecture Notes in Electrical Engineering, 251–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39581-9_25.

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Li, Qiudan, Stephen Shaoyi Liao, and Dandan Li. "A Clustering Model for Mining Consumption Patterns from Imprecise Electric Load Time Series Data." In Fuzzy Systems and Knowledge Discovery, 1217–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_152.

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Conference papers on the topic "Electrical Load Pattern"

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Porumb, Radu, Nicolae Golovanov, Cornel Toader, and Petru Postolache. "Electrical load pattern for LV consumers. A Romanian case." In 2012 47th International Universities Power Engineering Conference (UPEC). IEEE, 2012. http://dx.doi.org/10.1109/upec.2012.6398427.

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Pelka, Pawel, and Grzegorz Dudek. "Pattern-based Long Short-term Memory for Mid-term Electrical Load Forecasting." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206895.

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Paterakis, N. G., J. P. S. Catalao, A. Tascikaraoglu, A. G. Bakirtzis, and O. Erdinc. "Demand response driven load pattern elasticity analysis for smart households." In 2015 IEEE 5th International Conference on Power Engineering, Energy and Electrical Drives (POWERENG). IEEE, 2015. http://dx.doi.org/10.1109/powereng.2015.7266350.

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Sun, Mingyang, Ioannis Konstantelos, and Goran Strbac. "C-Vine copula mixture model for clustering of residential electrical load pattern data." In 2017 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2017. http://dx.doi.org/10.1109/pesgm.2017.8274202.

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Zheng, WanTing, Liu Zhu, QingZhu Wan, YuXiang Zheng, Kun Zhang, and J. HR D. "Refined Load Pattern Recognition Based on Double-layer ISODATA Clustering and SVM." In 2020 7th International Forum on Electrical Engineering and Automation (IFEEA). IEEE, 2020. http://dx.doi.org/10.1109/ifeea51475.2020.00136.

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Wu, Haiwei, Lin Lin, Jianan Wang, Song Yuan, and Yunyi Huang. "Power Load Pattern Recognition based on Improved K-Means and SVM Classifier." In 2022 4th International Conference on Electrical Engineering and Control Technologies (CEECT). IEEE, 2022. http://dx.doi.org/10.1109/ceect55960.2022.10030255.

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Seixas, J. M., L. P. Calôba, C. B. Prado, and J. C. R. Aguiar. "Neural Discriminating Analysis for a Nonintrusive Electrical Load Monitoring System." In Simpósio Brasileiro de Arquitetura de Computadores e Processamento de Alto Desempenho. Sociedade Brasileira de Computação, 1997. http://dx.doi.org/10.5753/sbac-pad.1997.22625.

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A nonintrusive electrical load monitoring system for household appliances is developed using neural networks. Appliances are characterized by features extracted from their transient and steady-state responses obtained from sampling information from the AC power line. A discriminating analysis is applied as an efficient way to achieve a compact neural discriminator which identifies seven classes of equipment. Over 100 different pieces of equipment studied, the system classifies correctly more than 90% of the sample. The system is implemented on a 16 node transputer based parallel machine to support massive application. A processing time smaller than 2µs is achieved for each pattern.
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Liu, Bo, Jinjiang Zhang, Zishuai Liu, Wenpeng Luan, Bochao Zhao, and Longfei Tian. "Regional multi-user load dataset simulation for NILM based on appliance power consumption pattern." In 2022 7th Asia Conference on Power and Electrical Engineering (ACPEE). IEEE, 2022. http://dx.doi.org/10.1109/acpee53904.2022.9783727.

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Srinidhi, S., Mayank Tiwari, Rajni Burra, Hombe Gowda, and Paul A. Siemers. "Bearing Wear Due to Mechanical Stresses and Electrical Currents." In ASME/STLE 2009 International Joint Tribology Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/ijtc2009-15255.

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Passage of current through moving conductive contacts results in electrical discharge and then melting of the material, which leads to wear. Such kind of bearing wear is common in electrical machines. There are however certain patterns which are unique to this kind wear. This wear pattern is called ‘fluting’, which are repetitive in nature. Electrical discharge can create higher surface roughness. Also the thermal and rheological properties of the lubricant play a big role in the film thickness formation. The passage of current through the lubricant also changes this and is determined by the electrical properties of the lubricant. In this work effect of bearing currents on a 7204 angular contact ball bearing is studied. This is tested with and without different cage materials with an axial load and no radial load, rotating at 2700 rpm. Four experiments were done at different-level of voltage, lube and cage material. Type of lubricant was seen to play a significant role in fluting.
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Siwek, Krzysztof, and Stanislaw Osowski. "Local Dynamic Fusion for 24-Hour Load Pattern Prediction in Power System." In 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). IEEE, 2018. http://dx.doi.org/10.1109/eeeic.2018.8494622.

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