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

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.

scholarly journals Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Zhisheng Zhang ◽  
Wenjie Gong
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Quantum Computation ◽  
Load Forecasting ◽  
Feedforward Neural Network ◽  
Forecasting Model ◽  
Short Term ◽  
Feedback Connection ◽  
Model Based ◽  
Short Term Load Forecasting

Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means ofK-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

scholarly journals Analysis of Different Neural Networks and a New Architecture for Short-Term Load Forecasting

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1433 ◽  
Author(s):  
Lintao Yang ◽  
Honggeng Yang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Scale ◽  
Load Forecasting ◽  
Short Term ◽  
Electric System ◽  
Power Company ◽  
Network Methods ◽  
Short Term Load Forecasting ◽  
Electric Power Company

Short-term load forecasting (STLF) has been widely studied because it plays a very important role in improving the economy and security of electric system operations. Many types of neural networks have been successfully used for STLF. In most of these methods, common neural networks were used, but without a systematic comparative analysis. In this paper, we first compare the most frequently used neural networks’ performance on the load dataset from the State Grid Sichuan Electric Power Company (China). Then, considering the current neural networks’ disadvantages, we propose a new architecture called a gate-recurrent neural network (RNN) based on an RNN for STLF. By evaluating all the methods on our dataset, the results demonstrate that the performance of different neural network methods are related to the data time scale, and our proposed method is more accurate on a much shorter time scale, particularly when the time scale is smaller than 20 min.

Sign in / Sign up

Export Citation Format

Share Document