APPLICATION OF A CONVOLUTION NEURAL NETWORK TO INCREASE THE ACCURACY OF THE SHORT-TERM LOAD FORECASTING OF THE ELECTRICAL ENGINEERING COMPLEX OF THE SINGLE-AREA POWER GRID

2020 ◽  
Vol 2 (1) ◽  
pp. 044-047
Author(s):  
S. O. Khomutov ◽  
◽  
N. A. Serebryakov ◽  
Keyword(s):  
Neural Network ◽  
Electrical Engineering ◽  
Power Grid ◽  
Load Forecasting ◽  
Convolution Neural Network ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Application of adaptive ensemble neural network method for short-term load forecasting electrical engineering complex of regional electric grid

2021 ◽  
pp. 39-45
Author(s):  
N. A. Serebryakov ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Electrical Engineering ◽  
Load Forecasting ◽  
Electric Grid ◽  
Short Term ◽  
Neural Network Method ◽  
Short Term Load Forecasting ◽  
Network Method ◽  
Ensemble Neural Network

The article is devoted to the problem of improving the accuracy of short-term load forecasting of electrical engineering complex of regional electric grid with the use deep machine learning tools. The effectiveness of the application of the adaptive learning algorithm for deep neural networks for short-term load forecasting of this electrical complex has been investigated. The issues of application of convolutional and recurrent neural networks for short-term load forecasting are considered. A comparative analysis of the accuracy of the short-term load forecasting of electrical engineering complex of regional electric grid obtained using the ensemble neural network method and single neural networks are produced

Recurrent inception convolution neural network for multi short-term load forecasting

2019 ◽  
Vol 194 ◽  
pp. 328-341 ◽  
Author(s):  
Junhong Kim ◽  
Jihoon Moon ◽  
Eenjun Hwang ◽  
Pilsung Kang
Keyword(s):  
Neural Network ◽  
Load Forecasting ◽  
Convolution Neural Network ◽  
Short Term ◽  
Short Term Load Forecasting

scholarly journals Creation of a short-term load forecasting neural network model of electrical engineering complex of section regional electric grid 6-35 kV

2020 ◽  
Vol 6 (1) ◽  
pp. 80-91
Author(s):  
Stanislav O. Khomutov ◽  
Nikolay A. Serebryakov
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Electrical Engineering ◽  
Operation Mode ◽  
Load Forecasting ◽  
Electric Grid ◽  
Short Term ◽  
Math Model ◽  
Short Term Load Forecasting

Aim: Is developing of short-term load forecasting math model of the electrical engineering complex of the district regional electric grid 6-35 kV with the use of artificial neural networks. Methods: The tools of regression analysis and deep machine learning were used in the work. Results: The neural network model for short-term load forecasting of the electrical engineering complex of section regional electric grid 6-35 kV, which considered factors of time, meteorological conditions, disconnections of individual power transmission lines, the operation mode of electricity consumers with a capacity of over 670 kW, the fact of the availability of central heating and water supply, has been obtained. Conclusion: The developed neural network math model reduces the problem of short-term load forecasting to the search of matrix free coefficients through training on the available statistical data.

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