A hybrid forecasting model based on date-framework strategy and improved feature selection technology for 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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A new short-term load forecasting model based on relevance vector machine

22nd International Conference and Exhibition on Electricity Distribution (CIRED 2013) ◽

10.1049/cp.2013.1250 ◽

2013 ◽

Author(s):

Zhinong Wei ◽

Shuaidong Huang ◽

Jiang Wu ◽

Xiaolu Li ◽

K.W. Cheung

Keyword(s):

Load Forecasting ◽

Relevance Vector Machine ◽

Forecasting Model ◽

Short Term ◽

Model Based ◽

Short Term Load Forecasting

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Short-Term Electricity Load Forecasting Model Based on EMD-GRU with Feature Selection

Energies ◽

10.3390/en12061140 ◽

2019 ◽

Vol 12 (6) ◽

pp. 1140 ◽

Cited By ~ 15

Author(s):

Xin Gao ◽

Xiaobing Li ◽

Bing Zhao ◽

Weijia Ji ◽

Xiao Jing ◽

...

Keyword(s):

Feature Selection ◽

Prediction Accuracy ◽

Load Forecasting ◽

Support Vector ◽

Forecasting Model ◽

Data Sets ◽

Short Term ◽

Model Based ◽

Electricity Load ◽

Electricity Load Forecasting

Many factors affect short-term electric load, and the superposition of these factors leads to it being non-linear and non-stationary. Separating different load components from the original load series can help to improve the accuracy of prediction, but the direct modeling and predicting of the decomposed time series components will give rise to multiple random errors and increase the workload of prediction. This paper proposes a short-term electricity load forecasting model based on an empirical mode decomposition-gated recurrent unit (EMD-GRU) with feature selection (FS-EMD-GRU). First, the original load series is decomposed into several sub-series by EMD. Then, we analyze the correlation between the sub-series and the original load series through the Pearson correlation coefficient method. Some sub-series with high correlation with the original load series are selected as features and input into the GRU network together with the original load series to establish the prediction model. Three public data sets provided by the U.S. public utility and the load data from a region in northwestern China were used to evaluate the effectiveness of the proposed method. The experiment results showed that the average prediction accuracy of the proposed method on four data sets was 96.9%, 95.31%, 95.72%, and 97.17% respectively. Compared to a single GRU, support vector regression (SVR), random forest (RF) models and EMD-GRU, EMD-SVR, EMD-RF models, the prediction accuracy of the proposed method in this paper was higher.

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