Learning algorithm of artificial neural network factor forecasting power consumption of users

Shiralievich, Tavarov Saijon; Ivanovic, Sidorov Alexander; Mamanazarovna, Shonazarova Shakhnoza; Olimovich, Sultonov Olamafruz; Yunusov, P.A.

doi:10.11591/eei.v11i2.3172

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…The error in the data obtained using the proposed model with membership functions when compared with the data of the readings of electricity meters installed at electricity consumers (population) does not exceed 5% [22]. Therefore, the high adequacy of the proposed model can be argued.…”

Section: Discussionmentioning

confidence: 84%

“…Finding weights for indicators was carried out by the method of constructing an analytical hierarchical process, proposed by T.L. Saaty [22]. At this stage, the priorities of these indicators relative to each other were determined, which ultimately influenced the content and type of management decision.…”

Section: Methodsmentioning

confidence: 99%

“…Management of household power consumption and monitoring of an intelligent network use artificial intelligence methods [19][20][21][22][23]. Recently, several new problems have arisen in the operation of urban power supply systems.…”

mentioning

confidence: 99%

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Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

et al. 2023

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Seasonal fluctuations in electricity consumption, and uneven loading of supply lines reduce not only the energy efficiency of networks, but also contribute to a decrease in the service life of elements of power supply systems. To solve the problem of forecasting power consumption, it is proposed to use the theory of fuzzy sets to assess the effective development of the energy system of the Republic of Tajikistan. According to the statistical data of power consumption for the previous period, a fuzzy logic model with membership functions is proposed, which makes it possible to evaluate consumer satisfaction using the criteria unsatisfactory, satisfactory, conditionally satisfactory, and satisfactory, as well as the efficiency of the consumption mode of compliance using the criteria high, medium, and low, allowing the evaluation of the efficiency plan for the development of the energy system of the Republic of Tajikistan. To obtain and set more accurate data on electricity consumption, calculations were made for the winter period of the year. Based on the proposed calculation model of fuzzy logic, a quantitative component of electricity consumption, the corresponding satisfaction of the consumer, and the efficiency of the regime for nine cities of the Republic of Tajikistan were proposed in the form of diagrams of seasonal electricity consumption. The obtained seasonal power consumption makes it possible to improve the accuracy of estimating power consumption, thereby equalizing the balance of consumption and generation.

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Section: Discussionmentioning

confidence: 84%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

et al. 2023

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“…One line represents a training sample, one column represents a test sample, one value represents a test sample, and one value represents the relationship between the two data, which is relatively large, so it is necessary to use this data for reconstruction. For example, for a training sample, rows 1,3, and 4 of the test samples were not 0, indicating that they were related to the training sample, so 1,3, and 4 test samples should be selected during reconstruction [18]. On the other hand, due to the sparsity of the data, the data has zero in behavior, that is, the samples with noise are not associated with other test samples.…”

Section: K Nmentioning

confidence: 99%

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

Yan Liu, Chaoran Bi, Yaoyu Wang

2024

jes

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In view of the great pressure of operation and maintenance of power energy data collection terminal after putting into operation, an intelligent fault diagnosis system for power equipment terminal based on computer was developed. This paper proposes an Agent based on autonomy, promotility, cooperation and interactivity. It also provides a flexible interactive communication interface and load balancing optimization method. It can meet a variety of data transmission specifications and a large number of data service transmission requirements. The SA-KNN algorithm is used to analyze the power quality. Methods The operation data of maintenance electricity were collected. Learning and cluster analysis are carried out by using relevant data, and the calculation model of automatic check device failure is established. At the same time, the outlier points in the collected data are used to accurately locate the fault points of the power grid. This method can realize the comprehensive fault diagnosis of the power grid by using the interaction between the power grid and each component.

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“…The research reveals several challenges and limitations regarding the FCW system. These are a self-learning algorithm [18], eye-tracking recognition and identification, and an adaptive driving assistance system. The key findings of this previous research show limitations regarding the eye closeness detection system such as blurring or high pixels on images [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence system for driver distraction by stacked deep learning classification

Qibtiah

Zin²,

Hassan³

2023

Bulletin EEI

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Increasing efforts in the transportation system have recently improved driver safety and reduced crash rates. Lack of attention and fatigue directly affect the driver's consciousness. Driver distraction is an essential driver-specific factor in the practical applicability of forward collision warning (FCW). However, there are still too many false alarms generated by the existing FCW system to be mitigated. This paper proposes facial detection to identify features and test anomalies' prediction against drivers using stacked convolutional neural network (CNN) layers. The proposed model used overlapping HAAR and stacked CNN features to identify classifications of eye areas, such as open or closed. In addition to the sliding query window's overall intensity information. The conventional HAAR function, which elevates the brightness of nearby regions, is still preferable. This method considers current intelligent transportation system-based solutions to minimize distraction effects by continuously comparing with flexible thresholds. The experimental results are analyzed from accurate driving datasets. At 456 iterations, the results acquired over 80% accuracy, while loss is near zero. The implication of driver's risk tolerance is further explored in this manner. Several risks are connected to driving any type of transportation system.

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Learning algorithm of artificial neural network factor forecasting power consumption of users

Cited by 5 publications

References 15 publications

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

Forecasting of Electricity Consumption by Household Consumers Using Fuzzy Logic Based on the Development Plan of the Power System of the Republic of Tajikistan

Application of SSA-KNN Algorithm in Fault Analysis of Power Metering Automation System

Artificial intelligence system for driver distraction by stacked deep learning classification

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