scholarly journals An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Phase Space ◽  
Deep Neural Network ◽  
Time Series Data ◽  
Load Forecasting ◽  
Phase Space Reconstruction ◽  
Series Data ◽  
Forecasting Model ◽  
Model Based ◽  
Net Load

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.

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.

Public Buildings Baseline Load Forecasting Model

2011 ◽  
Vol 403-408 ◽  
pp. 2098-2101
Author(s):  
Qing Ma ◽  
Qi Qiang Li
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Bp Neural Network ◽  
Historical Data ◽  
Load Forecasting ◽  
Forecasting Model ◽  
Adjustment Factor ◽  
Public Buildings ◽  
The Public ◽  
Model Based

Based on the fact that public buildings baseline load is hard to predict effectively,a kind of BP neural networks forecasting model based on FCM optimization preprocesses which combines with adjustment factor is proposed. The method which adopts method of the FCM arithmetic divides the complicated historical data into gather of multiple proxy event day populations. Then, based on BP neural network forecasting model regulated by adjustment factor, public buildings baseline load forecasting model is introduced. The prediction results show that the prediction precision of the model is higher than that of linearity model, and it can predict the public buildings baseline load effectively.

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