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

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.

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

Mathematical Problems in Engineering ◽

10.1155/2016/7910971 ◽

2016 ◽

Vol 2016 ◽

pp. 1-8 ◽

Cited By ~ 4

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.

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

Omsk Scientific Bulletin ◽

10.25206/1813-8225-2021-175-39-45 ◽

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

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Short-Term Load Forecasting Using Adaptive Annealing Learning Algorithm Based Reinforcement Neural Network

10.20944/preprints201609.0119.v1 ◽

2016 ◽

Author(s):

Cheng-Ming Lee ◽

Chia-Nan Ko

Keyword(s):

Neural Network ◽

Learning Algorithm ◽

Load Forecasting ◽

Support Vector ◽

Initial Structure ◽

Short Term ◽

Power Company ◽

Learning Rates ◽

Proposed Model ◽

Short Term Load Forecasting

A reinforcement learning algorithm is proposed to improve the accuracy of short-term load forecasting (STLF) in this article. The proposed model integrates radial basis function neural network (RBFNN), support vector regression (SVR), and adaptive annealing learning algorithm (AALA). In the proposed methodology, firstly, the initial structure of RBFNN is determined by using SVR. Then, an AALA with time-varying learning rates is used to optimize the initial parameters of SVR-RBFNN (AALA-SVR-RBFNN). In order to overcome the stagnation for searching optimal RBFNN, a particle swarm optimization (PSO) is applied to simultaneously find promising learning rates in AALA. Finally, the short-term load demands are predicted by using the optimal RBFNN. The performance of the proposed methodology is verified on the actual load dataset from Taiwan Power Company (TPC). Simulation results reveal that the proposed AALA-SVR-RBFNN can achieve a better load forecasting precision as compared to various RBFNNs.

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