Application of Bidirectional Recurrent Neural Network Combined With Deep Belief Network in Short-Term Load Forecasting

The importance of conducting potential analysis of load data and ensuring the effectiveness of feature selection cannot be overstated when it comes to enhancing the accuracy of short-term power load forecasting. Bisecting K-Means Algorithm is adopted for cluster analysis of the load data, the simila...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.160660-160670
Main Authors: Tang, Xianlun, Dai, Yuyan, Liu, Qing, Dang, Xiaoyuan, Xu, Jin
Format: Article
Language:English
Subjects:
Algorithms
Belief networks
Cluster analysis
Clustering algorithms
Correlation coefficients
Decomposition
deep belief network
Electricity consumption
Empirical analysis
Empirical mode decomposition
ensemble empirical mode decomposition
Forecasting
Load forecasting
Load modeling
Mathematical analysis
Neural networks
Neurons
Predictive models
recurrent neural network
Recurrent neural networks
Short-term power load forecasting
Training
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Summary:The importance of conducting potential analysis of load data and ensuring the effectiveness of feature selection cannot be overstated when it comes to enhancing the accuracy of short-term power load forecasting. Bisecting K-Means Algorithm is adopted for cluster analysis of the load data, the similarity data is categorized into the same cluster, and then the load data is decomposed into several Intrinsic Mode Functions (IMFs) by Ensemble Empirical Mode Decomposition (EEMD) in this study. Then the candidate features are selected by calculating Pearson correlation coefficient, and finally the forecasting input is constructed. A hybrid neural network forecasting model based on Deep Belief Network (DBN) and Bidirectional Recurrent Neural Network (Bi-RNN) is proposed. The method adopts unsupervised pre-training and supervised adjustment training methods and is verified on two different datasets. Compared with the forecasting results of other methods, it shows that the method can effectively improve the accuracy of load forecasting.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2950957