Journal articles: '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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Al-Hasan, A. Y., A. A. Ghoneim, and A. H. Abdullah. "Optimizing electrical load pattern in Kuwait using grid connected photovoltaic systems." Energy Conversion and Management 45, no. 4 (March 2004): 483–94. http://dx.doi.org/10.1016/s0196-8904(03)00163-8.

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Narayanan, Siddharth, Rajagopalan Badrinarayanan, and Zhen Lu. "A Comparative Study on the Effects of Power Supply Patterns in Microgrids for DC Powered Electrical Appliances." Advanced Materials Research 986-987 (July 2014): 167–71. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.167.

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To increase the energy efficiency and reduce the cost, the DC-Microgrids will have a very promising future in the power supply systems. In this paper, two power supply patterns for modern appliances, the central rectifier pattern and the distributed rectifier pattern, are investigated and compared. A concept of equivalent efficiency of diode bridge rectifier for DC-powered appliances is introduced and then it is applied in the model simplification. A Curved-Load model is constructed which is based on the specific efficiency characteristics of diode bridge rectifier in a particular current range and the statistics for appliance’s energy consumption as a percentage of the total. Using the Curved-Load model, the comparison of two microgrid system models was conducted with the help of simulations using Ansoft Simplorer 9.0 aiming to examine the conversion efficiency of rectifier patterns.

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Khalid, Khairuddin, Azah Mohamed, Ramizi Mohamed, and Hussain Shareef. "Performance Comparison of Artificial Intelligence Techniques for Non-intrusive Electrical Load Monitoring." Bulletin of Electrical Engineering and Informatics 7, no. 2 (June 1, 2018): 143–52. http://dx.doi.org/10.11591/eei.v7i2.1190.

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The increased awareness in reducing energy consumption and encouraging response from the use of smart meters have triggered the idea of non-intrusive load monitoring (NILM). The purpose of NILM is to obtain useful information about the usage of electrical appliances usually measured at the main entrance of electricity to obtain aggregate power signal by using a smart meter. The load operating states based on the on/off loads can be detected by analysing the aggregate power signals. This paper presents a comparative study for evaluating the performance of artificial intelligence techniques in classifying the type and operating states of three load types that are usually available in commercial buildings, such as fluorescent light, air-conditioner and personal computer. In this NILM study, experiments were carried out to collect information of the load usage pattern by using a commercial smart meter. From the power parameters captured by the smart meter, effective signal analysis has been done using the time time (TT)-transform to achieve accurate load disaggregation. Load feature selection is also considered by using three power parameters which are real power, reactive power and the TT-transform parameters. These three parameters are used as inputs for training the artificial intelligence techniques in classifying the type and operating states of the loads. The load classification results showed that the proposed extreme learning machine (ELM) technique has successfully achieved high accuracy and fast learning compared with artificial neural network and support vector machine. Based on validation results, ELM achieved the highest load classification with 100% accuracy for data sampled at 1 minute time interval.

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Chicco, Gianfranco, Octavian-Marcel Ionel, and Radu Porumb. "Electrical Load Pattern Grouping Based on Centroid Model With Ant Colony Clustering." IEEE Transactions on Power Systems 28, no. 2 (May 2013): 1706–15. http://dx.doi.org/10.1109/tpwrs.2012.2220159.

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Notaristefano, Antonio, Gianfranco Chicco, and Federico Piglione. "Data size reduction with symbolic aggregate approximation for electrical load pattern grouping." IET Generation, Transmission & Distribution 7, no. 2 (February 1, 2013): 108–17. http://dx.doi.org/10.1049/iet-gtd.2012.0383.

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Chen, Guo Jin, Ming Xu, Ting Ting Liu, Jing Ni, Dong Xie, and Yan Ping Zhang. "Fault Pattern Recognition for Partial Discharge of Electrical Power Equipment Based on Properties of Electrical Materials." Advanced Materials Research 644 (January 2013): 105–9. http://dx.doi.org/10.4028/www.scientific.net/amr.644.105.

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Partial discharge causes mainly the insulation deterioration. It is the significant symptom and manifestation, and is an important factor of the insulation failure for the electrical power equipment. On the basis of analyzing the physical model of partial discharge, this paper used the online monitoring technology of partial discharge that combines the ultra high frequency (UHF) method and the acoustic emission (AE) method, studied the fault pattern recognition method of partial discharge based on the case-based reasoning algorithm, and established the intelligent fault identification system of partial discharge based on the case-based reasoning. The system can accurately and reliably identify the fault mode type, the specific fault location and severity of partial discharge for the electrical power equipment to make the health evaluation and improve the reliability. Through the application of the new materials and new technology, the load loss of the transformer can drop by 15%, the no-load loss can decline by 50% and the fee of electricity loss can down by 32.5%.

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Daraghmi, Yousef-Awwad, Eman Yaser Daraghmi, Motaz Daadoo, and Samer Alsaadi. "Forecasting for smart energy: an accurate and effificient negative binomial additive model." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 2 (November 1, 2020): 1000. http://dx.doi.org/10.11591/ijeecs.v20.i2.pp1000-1006.

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<div>Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient</div><div>energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char</div><div>acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns.</div><div>Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting</div><div>methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel</div><div>model for short-term electric load forecasting. The model adopts the negative binomial additive models</div><div>(NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season</div><div>ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of</div><div>load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM</div><div>captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has</div><div>low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its</div><div>accuracy and effificiency outperform those of the other models used in this context.</div>

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Chicco, Gianfranco. "Overview and performance assessment of the clustering methods for electrical load pattern grouping." Energy 42, no. 1 (June 2012): 68–80. http://dx.doi.org/10.1016/j.energy.2011.12.031.

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Sun, Mingyang, Ioannis Konstantelos, and Goran Strbac. "C-Vine Copula Mixture Model for Clustering of Residential Electrical Load Pattern Data." IEEE Transactions on Power Systems 32, no. 3 (May 2017): 2382–93. http://dx.doi.org/10.1109/tpwrs.2016.2614366.

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Hussain, Mashitah Mohd, Zuhaina Zakaria, Nofri Yenita Dahlan, Nur Iqtiyani Ilham, Zhafran Hussin, Noor Hasliza Abdul Rahman, and Md Azwan Md Yasin. "Short term forecasting of electrical consumption using a neural network: joint approximate diagonal eigenvalue." Indonesian Journal of Electrical Engineering and Computer Science 26, no. 1 (April 1, 2022): 56. http://dx.doi.org/10.11591/ijeecs.v26.i1.pp56-66.

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This article aims <span lang="EN-US">to estimate the load profiling of electricity that provides information on the electrical load demand. In achieving this research implemented the neural network algorithm of joint approximate diagonalisation of eigen-matrices (JADE) to describe the load profile pattern for each point. Nowadays, utility providers claim that natural sources are used to generate power by rising consumer demands for energy. However, occasionally utility workers need to know the demand at certain location, corresponding to maintenance issues or for any shutdown area involved.<br /> A distribution pattern based on the data can be predicted based on the incoming data profile without having detailed information of certain load bus, the concept of derivatives was relevant to forecast the types of distribution data. The model was constructed with load profile information based on three different locations, and the concept of derivative was recognized, including the type of incoming data. Historical data were captured from a selected location in Malaysia that was proposed to train the JADE algorithm from three different empirical distributions of consumers, recording every 15 minutes per day. The results were analyzed based on the error measurement and compared with the real specific load distribution feeder information of needed profiles.</span>

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Priyanto, Yun Tonce Kusuma, Vicky Mudeng, and Muhammad Robith. "Optimal Power Flow with Considering Voltage Stability using Chaotic Firefly Algorithm." International Journal of Sustainable Transportation Technology 2, no. 1 (April 30, 2019): 1–7. http://dx.doi.org/10.31427/ijstt.2019.2.1.1.

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In transportation technology, the development of electric vehicle is growing rapidly. In the future, the availability of electrical power is crucial because every electric tool needs electrical power. Power plant must provide electrical power for all consumer include an electric vehicle. Sustainability of electrical power transmission and distribution must be considered as critical due to its high power consumption in the community. One of the problem to supply electrical power is how to keep the system’s voltage stability. Several variations on the load pattern and topological can lead to a substantial poor impact on the system. However, generation cost must be considered by utilities’ operator. This paper demonstrates a recently developed metaheuristic method called Chaotic Firefly Algorithm (CFA). Our simulation shows that this method may perform better than several well-known metaheuristic methods. Therefore, CFA may become a promising method to solve optimal power flow considering voltage stability cases.

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V.V, Athira, and Sruthy S. "Investigation on the use of cold formed perforated steel sections as columns and purlins." International Journal of Engineering & Technology 7, no. 4.5 (September 22, 2018): 716. http://dx.doi.org/10.14419/ijet.v7i4.5.25066.

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Perforations are required in columns and purlins of a trussed building for making connections, for fixing electrical fixtures etc. An inves- tigation was undertaken to study the effect of perforations in cold-formed C and Z sections used as columns and purlins. For this purpose, finite element model was developed using ANSYS software. Six different shapes of perforations were considered to investigate the shape that gives maximum buckling load. The perforation pattern which is optimum for the individual section was applied to frames and then to the building and optimum type of perforation is suggested. Buckling loads of frames and building with and without perforation was done separately and results were compared. By considering perforations with equal area, linear buckling analysis was done and stress pattern around perforation was studied. Even though the area reduction was equal, differently shaped perforations gave different buckling load. In this case, stress concentration has an important role; buckling load is higher for the shape with least stress concentration.

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Son, Namrye, Seunghak Yang, and Jeongseung Na. "Deep Neural Network and Long Short-Term Memory for Electric Power Load Forecasting." Applied Sciences 10, no. 18 (September 17, 2020): 6489. http://dx.doi.org/10.3390/app10186489.

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Forecasting domestic and foreign power demand is crucial for planning the operation and expansion of facilities. Power demand patterns are very complex owing to energy market deregulation. Therefore, developing an appropriate power forecasting model for an electrical grid is challenging. In particular, when consumers use power irregularly, the utility cannot accurately predict short- and long-term power consumption. Utilities that experience short- and long-term power demands cannot operate power supplies reliably; in worst-case scenarios, blackouts occur. Therefore, the utility must predict the power demands by analyzing the customers’ power consumption patterns for power supply stabilization. For this, a medium- and long-term power forecasting is proposed. The electricity demand forecast was divided into medium-term and long-term load forecast for customers with different power consumption patterns. Among various deep learning methods, deep neural networks (DNNs) and long short-term memory (LSTM) were employed for the time series prediction. The DNN and LSTM performances were compared to verify the proposed model. The two models were tested, and the results were examined with the accuracies of the six most commonly used evaluation measures in the medium- and long-term electric power load forecasting. The DNN outperformed the LSTM, regardless of the customer’s power pattern.

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Cen, Senfeng, Jae Hung Yoo, and Chang Gyoon Lim. "Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform." Energies 15, no. 4 (February 13, 2022): 1350. http://dx.doi.org/10.3390/en15041350.

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Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.

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Apena, Waliu Olalekan. "A Knowledge-Based Demand Side Management: Interruptible Direct Load Approach." European Journal of Engineering Research and Science 2, no. 6 (June 30, 2017): 71. http://dx.doi.org/10.24018/ejers.2017.2.6.399.

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The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.

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Apena, Waliu Olalekan. "A Knowledge-Based Demand Side Management: Interruptible Direct Load Approach." European Journal of Engineering and Technology Research 2, no. 6 (June 30, 2017): 71–73. http://dx.doi.org/10.24018/ejeng.2017.2.6.399.

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The study focussed on managing electrical energy supplied to consumers from distribution end through initial knowledge on data acquisition and embedded system. It was achieved by controlling the inductive loads at the consumer premise. The study re-shaped the load and energy demand curve by cycling customers’ inductive loads which are prone to drawing high currents such as air conditioner and water heaters. Data from power utilities were gathered and analysed using tools to generate waveform pattern for energy consumption. Mathematical models for air conditioners and water heaters were derived in order to remotely control the appliances with the aids of embedded system implemented on the consumer premise.

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Seo, Youn-Kyu, and Won-Hwa Hong. "Constructing electricity load profile and formulating load pattern for urban apartment in Korea." Energy and Buildings 78 (August 2014): 222–30. http://dx.doi.org/10.1016/j.enbuild.2014.03.007.

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Mado, Ismit, Ruslim, and Sugeng Riyanto. "A Matlab/Simulink Simulation Small Signal Stability of Single-Machine Infinite Bus Using Optimal Control Based on Load Cluster Patterns." International Journal of Electrical, Energy and Power System Engineering 3, no. 3 (October 12, 2020): 83–88. http://dx.doi.org/10.31258/ijeepse.3.3.83-88.

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Matlab/Simulink is sophisticated software that has been facilitated by MathWorks Inc. This device is increasingly being used in various fields of research. Likewise, it has great potential in the field of power system simulation. This paper presents a simulation of the optimal performance of the power generation system due to changes in load consumption. Small signal stability due to changes in electrical power usage at the load center is overcome by applying a load cluster pattern. The main objective of this research is to achieve control in a power generation system that is responsive and able to maintain stability in all operating conditions at the load center. Simulation results show the performance of optimal control of the power generation system in each load cluster. Contributions to improve the stability of the power plant system performance by 28.03 percent for frequency (F), 23.03 percent for voltage (V), and 29.5 percent for electric power (P).

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Almohaimeed, Sulaiman A. "Electric Vehicle Deployment and Integration in the Saudi Electric Power System." World Electric Vehicle Journal 13, no. 5 (May 11, 2022): 84. http://dx.doi.org/10.3390/wevj13050084.

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The demand for electricity in Saudi Arabia has grown in the last few years due to the growth in the economy and the population. The country has invested in many solutions such as promoting renewable energy and shifting to generation mix to respond to this growing demand. However, Electric Vehicles (EVs) are used as an important factor in achieving the Saudi Vision 2030 in its environmental and economical parts. This work gives an overview on the Saudi electrical energy system and then investigates the impact EVs technology in the electricity sector in Saudi Arabia and its relevant consequences. A statistical analysis is used to quantify the number of EVs, travelled distance and traffic congestions, and State of Charge (SOC). The data were used to implement a daily load profile for EVs for a large population of vehicles. The obtained results show that the EVs peak loads occur during the late evening and early morning at different means. Interestingly, the work shows that the peak periods of EVs occur during the off-peak times of the daily load curve. This means that a large population of EVs can offer more flexibility and improvement to the electric grid, and the summative EV load of a large population of vehicles has a smooth pattern and will not affect the national electric system.

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Xiong, Ronglong, Fanmeng Kong, Xuehong Yang, Guangyuan Liu, and Wanhui Wen. "Pattern Recognition of Cognitive Load Using EEG and ECG Signals." Sensors 20, no. 18 (September 8, 2020): 5122. http://dx.doi.org/10.3390/s20185122.

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The matching of cognitive load and working memory is the key for effective learning, and cognitive effort in the learning process has nervous responses which can be quantified in various physiological parameters. Therefore, it is meaningful to explore automatic cognitive load pattern recognition by using physiological measures. Firstly, this work extracted 33 commonly used physiological features to quantify autonomic and central nervous activities. Secondly, we selected a critical feature subset for cognitive load recognition by sequential backward selection and particle swarm optimization algorithms. Finally, pattern recognition models of cognitive load conditions were constructed by a performance comparison of several classifiers. We grouped the samples in an open dataset to form two binary classification problems: (1) cognitive load state vs. baseline state; (2) cognitive load mismatching state vs. cognitive load matching state. The decision tree classifier obtained 96.3% accuracy for the cognitive load vs. baseline classification, and the support vector machine obtained 97.2% accuracy for the cognitive load mismatching vs. cognitive load matching classification. The cognitive load and baseline states are distinguishable in the level of active state of mind and three activity features of the autonomic nervous system. The cognitive load mismatching and matching states are distinguishable in the level of active state of mind and two activity features of the autonomic nervous system.

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Carpaneto, Enrico, Gianfranco Chicco, Roberto Napoli, and Mircea Scutariu. "Electricity customer classification using frequency–domain load pattern data." International Journal of Electrical Power & Energy Systems 28, no. 1 (January 2006): 13–20. http://dx.doi.org/10.1016/j.ijepes.2005.08.017.

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Moon, Jihoon, Yongsung Kim, Minjae Son, and Eenjun Hwang. "Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron." Energies 11, no. 12 (November 25, 2018): 3283. http://dx.doi.org/10.3390/en11123283.

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A stable power supply is very important in the management of power infrastructure. One of the critical tasks in accomplishing this is to predict power consumption accurately, which usually requires considering diverse factors, including environmental, social, and spatial-temporal factors. Depending on the prediction scope, building type can also be an important factor since the same types of buildings show similar power consumption patterns. A university campus usually consists of several building types, including a laboratory, administrative office, lecture room, and dormitory. Depending on the temporal and external conditions, they tend to show a wide variation in the electrical load pattern. This paper proposes a hybrid short-term load forecast model for an educational building complex by using random forest and multilayer perceptron. To construct this model, we collect electrical load data of six years from a university campus and split them into training, validation, and test sets. For the training set, we classify the data using a decision tree with input parameters including date, day of the week, holiday, and academic year. In addition, we consider various configurations for random forest and multilayer perceptron and evaluate their prediction performance using the validation set to determine the optimal configuration. Then, we construct a hybrid short-term load forecast model by combining the two models and predict the daily electrical load for the test set. Through various experiments, we show that our hybrid forecast model performs better than other popular single forecast models.

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Li, Xianyong, Yajun Du, and Yongquan Fan. "Fault Tolerance of Optical Hypercube Interconnection Networks with r -Communication Pattern." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–6. http://dx.doi.org/10.1155/2021/5358760.

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As power grids and optical interconnection networks are interdependent, the reliabilities of the optical networks are critical issues in power systems. The optical networks hold prominent performance including wide bandwidth, low loss, strong anti-interference capability, high fidelity, and reliable performance. They are regarded as promising alternatives to electrical networks for parallel processing. This paper is aimed at taking the first step in understanding the communication efficiencies of optical networks. For that purpose, on optical networks, we propose a series of novel notions including communication pattern, r -communication graph, reduced diameter, enhanced connectivity, r -diameter, and r -connectivity. Using these notions, we determine that the r -diameter and r -connectivity of the optical n -dimensional hypercube network are n / r and n 1 + n 2 + ⋯ + n r , respectively. Since the parameter r is variable, we can adjust different values of r on the basis of the wavelength resources and load of the optical networks, achieving enhanced communication efficiencies of these networks. Compared with the electric n -dimensional hypercube network, the proposed communication pattern on the optical hypercube network not only reduces the maximum communication delay of the conventional electrical hypercube significantly but also improves its fault tolerance remarkably.

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Dudek, Grzegorz. "Short-Term Load Forecasting Using Neural Networks with Pattern Similarity-Based Error Weights." Energies 14, no. 11 (May 31, 2021): 3224. http://dx.doi.org/10.3390/en14113224.

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Forecasting time series with multiple seasonal cycles such as short-term load forecasting is a challenging problem due to the complicated relationship between input and output data. In this work, we use a pattern representation of the time series to simplify this relationship. A neural network trained on patterns is an easier task to solve. Thus, its architecture does not have to be either complex and deep or equipped with mechanisms to deal with various time-series components. To improve the learning performance, we propose weighting individual errors of training samples in the loss function. The error weights correspond to the similarity between the training pattern and the test query pattern. This approach makes the learning process more sensitive to the neighborhood of the test pattern. This means that more distant patterns have less impact on the learned function around the test pattern and lead to improved forecasting accuracy. The proposed framework is useful for a wide range of complex time-series forecasting problems. Its performance is illustrated in several short-term load-forecasting empirical studies in this work. In most cases, error weighting leads to a significant improvement in accuracy.

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Jeong, Hyun Cheol, Minseok Jang, Taegon Kim, and Sung-Kwan Joo. "Clustering of Load Profiles of Residential Customers Using Extreme Points and Demographic Characteristics." Electronics 10, no. 3 (January 26, 2021): 290. http://dx.doi.org/10.3390/electronics10030290.

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In this paper, a systematic method is proposed to cluster the energy consumption patterns of residential customers by utilizing extreme points and demographic characteristics. The extreme points of the energy consumption pattern enable effective clustering of residential customers. Additionally, demographic characteristics can be used to determine an effective extreme point for the clustering algorithm. The K-means-based features selection method is used to classify energy consumption patterns of residential customers into six types. Furthermore, the type of energy consumption pattern can be identified depending on the characteristics of residential customers. The analytical results of this paper show that the extreme points are effective in clustering the energy consumption patterns of residential customers.

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Chen, W. H., and Z. H. Feng. "Planar reconfigurable pattern antenna by reactive-load switching." Microwave and Optical Technology Letters 47, no. 5 (2005): 506–7. http://dx.doi.org/10.1002/mop.21212.

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Mado, Ismit. "Best Estimation Of Double Seasonal Pattern Electric Load Parameters Using Least Squares Method." Journal FORTEI-JEERI 2, no. 1 (June 23, 2021): 27–31. http://dx.doi.org/10.46962/forteijeeri.v2i1.11.

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Forecasting is an important tool in planning an effective and efficient use of electrical loads. This paper presents an improvement in parameter estimation from previous studies. The results of previous studies indicate that the DSARIMA model is with MAPE about 2.06 percent. This model produces white noise residuals, but not normally distributed, which is thought to be due to outliers. Data smoothing is done to get the best data pattern. The analysis results show that the AR parameter iteration of the best DSARIMA model that is appropriate for short-term forecasting is with MAPE about 1.56 percent.

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Madokoro, Hirokazu, Kazuhisa Nakasho, Nobuhiro Shimoi, Hanwool Woo, and Kazuhito Sato. "Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns." Sensors 20, no. 5 (March 5, 2020): 1415. http://dx.doi.org/10.3390/s20051415.

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This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recognizer based on machine learning. The linear characteristic between loads and output was obtained from a load test to evaluate sensor output characteristics. Moreover, the output values change linearly concomitantly with speed to attain the sensor with the equivalent load. We obtained benchmark datasets of continuous and discontinuous behavior patterns from ten subjects. Recognition targets using our sensor prototype and their monitoring system comprise five behavior patterns: sleeping, longitudinal sitting, lateral sitting, terminal sitting, and leaving the bed. We compared machine learning algorithms of five types to recognize five behavior patterns. The experimentally obtained results revealed that the proposed sensor system improved recognition accuracy for both datasets. Moreover, we achieved improved recognition accuracy after integration of learning datasets as a general discriminator.

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Li, Jinghua, Mengshu Zhu, and Junjie Liang. "Pattern simulation and analysis of generalized load profile coupling with active load and renewable energy power." International Journal of Electrical Power & Energy Systems 117 (May 2020): 105611. http://dx.doi.org/10.1016/j.ijepes.2019.105611.

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Papadopoulos, Demetrios, and Eleftherios Maltas. "Design, Operation and Economic Analysis of Autonomous Hybrid PV-Diesel Power Systems Including Battery Storage." Journal of Electrical Engineering 61, no. 1 (January 1, 2010): 3–10. http://dx.doi.org/10.2478/v10187-010-0001-z.

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Design, Operation and Economic Analysis of Autonomous Hybrid PV-Diesel Power Systems Including Battery Storage This paper presents a systematic techno-economic analysis of autonomous PV-Diesel energy system with battery storage. This hybrid type power system was developed and installed on the roof of the Electrical Engineering Laboratory building in the city of Xanthi, Greece, where a weather station is also installed providing necessary meteorological data since 2002. Such system can be generally used to supply electrical loads of isolated remote areas. The actual design of such a system is based on: a pre-defined load pattern to be supplied; the pertinent weather data; the relevant market prices; and the applicable recent economic rates (eg June 2009 for the Greek case). The system is operated on a predictive manner using a Programmable Logic Controller (PLC) which controls the main system parameters for safe and continuous power supply to meet reliably the desired load demand. Three distinct systems of this type and of equal capacity, which combine energy sources and battery storage have been proposed and assessed technically and economically.

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Aguirre, Luis Antonio, Daniela D. Rodrigues, Silvio T. Lima, and Carlos Barreira Martinez. "Dynamical prediction and pattern mapping in short-term load forecasting." International Journal of Electrical Power & Energy Systems 30, no. 1 (January 2008): 73–82. http://dx.doi.org/10.1016/j.ijepes.2007.11.001.

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Gan, Wenyang, Qishan Dong, and Zhenzhong Chu. "Fault Diagnosis Method for an Underwater Thruster, Based on Load Feature Extraction." Electronics 11, no. 22 (November 13, 2022): 3714. http://dx.doi.org/10.3390/electronics11223714.

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Targeting the problem of fault diagnosis in magnetic coupling underwater thrusters, a fault pattern classification method based on load feature extraction is proposed in this paper. By analyzing the output load characteristics of thrusters under typical fault patterns, the load torque model of the thrusters is established, and two characteristic parameters are constructed to describe the different fault patterns of thrusters. Then, a thruster load torque reconstruction method, based on the sliding mode observer (SMO), and the fault characteristic parameter identification method, based on the least square method (LSM), are proposed. According to the identified fault characteristic parameters, a thruster fault pattern classification method based on a support vector machine (SVM) is proposed. Finally, the feasibility and superiority of the proposed aspects are verified, through comparative simulation experiments. The results show that the diagnostic accuracy of this method is higher than 95% within 5 seconds of the thruster fault. The lowest diagnostic accuracy of thrusters with a single failure state is 96.75%, and the average diagnostic accuracy of thrusters with five fault states is 98.65%.

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Sunanda, Wahri, Muhammad Ali Raja Siregar, and Ghiri Basuki Putra. "PLANNING OF PHOTOVOLTAIC AND PLN HYBRID SYSTEM IN BANGKA BELITUNG UNIVERSITY." Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) 6, no. 2 (October 3, 2019): 56–60. http://dx.doi.org/10.33019/ecotipe.v6i2.1082.

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Electricity at Universitas Bangka Belitung is currently supplied by the Bangka Belitung Region PLN with a power supply of 690 kVA to meet electrical energy needs. This study analyzes the hybrid photovoltaic-PLN pattern in UBB to find out more economical systems. The calculation result of the more economical hybrid pattern is the pattern with the distribution of PLN's load of 70% -30% PLTS which results in a Net Present Cost (NPC) value of US $ 1,071 M and a Cost Of Energy (COE) of US $ 0.107.

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Singh, Lakhwinder, J. S. Dhillon, and R. C. Chauhan. "Evaluation of Best Weight Pattern for Multiple Criteria Load Dispatch." Electric Power Components and Systems 34, no. 1 (January 2006): 21–35. http://dx.doi.org/10.1080/15325000691001520.

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Yoo, Yeuntae, and Seokheon Cho. "Analysis of the Impact of Particulate Matter on Net Load and Behind-the-Meter PV Decoupling." Electronics 11, no. 14 (July 20, 2022): 2261. http://dx.doi.org/10.3390/electronics11142261.

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With the increasing penetration of the photovoltaic (PV) generator, uncertainty surrounding the power system has increased simultaneously. The uncertainty of PV generation output has an impact on the load demand forecast due to the presence of behind-the-meter (BtM) PV generation. As it is hard to assess the amount of BtM PV generation, the load demand pattern can be distorted depending on the solar irradiation level. In several literature works, the influence of the load demand pattern from BtM PV generation is modeled using environmental data sets such as the level of solar irradiation, temperature, and past load demand data. The particulate matter is a severe meteorological event in several countries that can reduce the level of solar irradiation on the surface. The accuracy of the forecast model for PV generation and load demand can be exacerbated if the impact of the particulate matter is not properly considered. In this paper, the impact of particulate matter to load demand patterns is analyzed for the power system with high penetration of BtM PV generation. Actual meteorological data are gathered for the analysis and correlations between parameters are built using Gaussian process regression.

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46

Chaaraoui, Samer, Matthias Bebber, Stefanie Meilinger, Silvan Rummeny, Thorsten Schneiders, Windmanagda Sawadogo, and Harald Kunstmann. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms." Energies 14, no. 2 (January 13, 2021): 409. http://dx.doi.org/10.3390/en14020409.

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Ghana suffers from frequent power outages, which can be compensated by off-grid energy solutions. Photovoltaic-hybrid systems become more and more important for rural electrification due to their potential to offer a clean and cost-effective energy supply. However, uncertainties related to the prediction of electrical loads and solar irradiance result in inefficient system control and can lead to an unstable electricity supply, which is vital for the high reliability required for applications within the health sector. Model predictive control (MPC) algorithms present a viable option to tackle those uncertainties compared to rule-based methods, but strongly rely on the quality of the forecasts. This study tests and evaluates (a) a seasonal autoregressive integrated moving average (SARIMA) algorithm, (b) an incremental linear regression (ILR) algorithm, (c) a long short-term memory (LSTM) model, and (d) a customized statistical approach for electrical load forecasting on real load data of a Ghanaian health facility, considering initially limited knowledge of load and pattern changes through the implementation of incremental learning. The correlation of the electrical load with exogenous variables was determined to map out possible enhancements within the algorithms. Results show that all algorithms show high accuracies with a median normalized root mean square error (nRMSE) <0.1 and differing robustness towards load-shifting events, gradients, and noise. While the SARIMA algorithm and the linear regression model show extreme error outliers of nRMSE >1, methods via the LSTM model and the customized statistical approaches perform better with a median nRMSE of 0.061 and stable error distribution with a maximum nRMSE of <0.255. The conclusion of this study is a favoring towards the LSTM model and the statistical approach, with regard to MPC applications within photovoltaic-hybrid system solutions in the Ghanaian health sector.

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Kiprijanovska, Ivana, Simon Stankoski, Igor Ilievski, Slobodan Jovanovski, Matjaž Gams, and Hristijan Gjoreski. "HousEEC: Day-Ahead Household Electrical Energy Consumption Forecasting Using Deep Learning." Energies 13, no. 10 (May 25, 2020): 2672. http://dx.doi.org/10.3390/en13102672.

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Short-term load forecasting is integral to the energy planning sector. Various techniques have been employed to achieve effective operation of power systems and efficient market management. We present a scalable system for day-ahead household electrical energy consumption forecasting, named HousEEC. The proposed forecasting method is based on a deep residual neural network, and integrates multiple sources of information by extracting features from (i) contextual data (weather, calendar), and (ii) the historical load of the particular household and all households present in the dataset. Additionally, we compute novel domain-specific time-series features that allow the system to better model the pattern of energy consumption of the household. The experimental analysis and evaluation were performed on one of the most extensive datasets for household electrical energy consumption, Pecan Street, containing almost four years of data. Multiple test cases show that the proposed model provides accurate load forecasting results, achieving a root-mean-square error score of 0.44 kWh and mean absolute error score of 0.23 kWh, for short-term load forecasting for 300 households. The analysis showed that, for hourly forecasting, our model had 8% error (22 kWh), which is 4 percentage points better than the benchmark model. The daily analysis showed that our model had 2% error (131 kWh), which is significantly less compared to the benchmark model, with 6% error (360 kWh).

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Sueoka, Yuichiro, Takamasa Tahara, Masato Ishikawa, and Koichi Osuka. "Statistical Exploration of Distributed Pattern Formation Based on Minimalistic Approach." Journal of Robotics and Mechatronics 31, no. 6 (December 20, 2019): 905–12. http://dx.doi.org/10.20965/jrm.2019.p0905.

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In this paper, we discuss the pattern formation of objects that can be stacked and transported by distributed autonomous agents. Inspired by the social behavior of termite colonies, which often build elaborate three-dimensional structures (nest towers), this paper explores the mechanism of termite-like agents through a computational and minimalistic approach. We introduce a cellular automata model (i.e., spatially discretized) for the agents and the objects they can transport, where each agent follows a “rule” determined by the assignment of fundamental actions (move/ load/ unload) based on the state of its neighboring cells. To evaluate the resulting patterns from the viewpoint of structural complexity and agent effort, we classify the patterns using the Kolmogorov dimension and higher-order local autocorrelation, two well-known statistical techniques in image processing. We find that the Kolmogorov dimension provides a good metric for the structural complexity of a pattern, whereas the higher-order local autocorrelation is an effective means of identifying particular local patterns.

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Peram, Prashanthi, and Kumar Narayanan. "Diffusion Convolutional Recurrent Neural Network-Based Load Forecasting During COVID-19 Pandemic Situation." Revue d'Intelligence Artificielle 36, no. 5 (December 23, 2022): 689–95. http://dx.doi.org/10.18280/ria.360505.

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Infected by the novel coronavirus (COVID-19 – C-19) pandemic, worldwide energy generation and utilization have altered immensely. It remains unfamiliar in any case that traditional short-term load forecasting methodologies centered upon single-task, single-area, and standard signals could precisely catch the load pattern during the C-19 and must be cautiously analyzed. An effectual administration and finer planning by the power concerns remain of higher importance for precise electrical load forecasting. There presents a higher degree of unpredictability’s in the load time series (TS) that remains arduous in doing the precise short-term load forecast (SLF), medium-term load forecast (MLF), and long-term load forecast (LLF). For excerpting the local trends and capturing similar patterns of short and medium forecasting TS, we proffer Diffusion Convolutional Recurrent Neural Network (DCRNN), which attains finer execution and normalization by employing knowledge transition betwixt disparate forecasting jobs. This as well evens the portrayals if many layers remain stacked. The paradigms have been tested centered upon the actual life by performing comprehensive experimentations for authenticating their steadiness and applicability. The execution has been computed concerning squared error, Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Consequently, the proffered DCRNN attains 0.0534 of MSE in the Chicago area, 0.1691 of MAPE in the Seattle area, and 0.0634 of MAE in the Seattle area.

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Locana Diwy, Ida Ayu Vadanti, Amien Rahardjo, Aji Nur Widyanto, Faiz Husnayain, and Rudy Setiabudy. "Lockdown Impact due to Corona Pandemic on Electric Power Quality and Its Alternative Solutions for a University Office Building." ELKHA 14, no. 2 (October 20, 2022): 110. http://dx.doi.org/10.26418/elkha.v14i2.56226.

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The pandemic has changed the usage pattern of electrical appliances in buildings. This new pattern can create new problems in terms of power quality and thus affect the electrical system’s reliability. The purpose of this study is to determine the power quality of the Dean Office Building, Faculty of Engineering, Universitas Indonesia, and propose alternative solutions to its problems. To determine the quality of electric power, the related parameters such as voltage, current, power factor, and harmonics are measured first. The measurement results are compared with existing standards. If these standards cannot be met, damage to electrical equipment can occur. Out of eight parameters, three did not meet the criteria. First, overvoltage on average voltage with the range of 231.5-232.8 V. Second, IHDi nominal on the 3rd, 5th, 7th, and 15th harmonics for Thursday, April 7, 2022 on 06:11 with the value of 70.3%, 55.34%, 27.1%, and 8.07%. And third, the minimum power factor for the T phase with the value of 0.7219 is still less than 0.85. Possible solutions include checking and changing the supply transformer tap-changer, making an energy monitoring system and wiring diagram for understanding the load profile of the building, and using a single-tuned filter for harmonic currents.

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Journal articles on the topic 'Electrical Load Pattern'

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