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A platform for research: civil engineering, architecture and urbanism
Application of improved artificial neural networks in short-term power load forecasting
Power load forecasting is a key element for power system management and planning. However, it has been proven to be a hard task due to various unstable factors. This paper presents a forecasting methodology based on this particular type of neural network. The scope of this study presents a solution for short-term load forecasting based on a three-stage model which starts with pattern recognition via self-organizing map (SOM), a clustering of the previous partition by K-means algorithm, and finally demand forecasting for each cluster with back propagation neural network (BPNN) improved by additional momentum and variable learning rate methods. The effectiveness of SOM-K-BPNN model has been verified by the final simulation which shows that the proposed model outperforms the BPNN model with default parameters and Grey System GM (1, 1); therefore, empirical results show that the proposed SOM-K-BPNN model is feasible and can fulfil the short-term load forecasting requirements of China.
Application of improved artificial neural networks in short-term power load forecasting
Power load forecasting is a key element for power system management and planning. However, it has been proven to be a hard task due to various unstable factors. This paper presents a forecasting methodology based on this particular type of neural network. The scope of this study presents a solution for short-term load forecasting based on a three-stage model which starts with pattern recognition via self-organizing map (SOM), a clustering of the previous partition by K-means algorithm, and finally demand forecasting for each cluster with back propagation neural network (BPNN) improved by additional momentum and variable learning rate methods. The effectiveness of SOM-K-BPNN model has been verified by the final simulation which shows that the proposed model outperforms the BPNN model with default parameters and Grey System GM (1, 1); therefore, empirical results show that the proposed SOM-K-BPNN model is feasible and can fulfil the short-term load forecasting requirements of China.
Application of improved artificial neural networks in short-term power load forecasting
Wei, Sun (author) / Mohan, Liu (author)
2015-07-01
12 pages
Article (Journal)
Electronic Resource
English
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