Solving Bilevel Programming Problems Using a Neural Network Approach and Its Application to Power System Environment

2011 ◽  
Vol 4 (6) ◽  
pp. 387-393 ◽  
Author(s):  
Shamshul Bahar YAAKOB ◽  
Junzo WATADA
Keyword(s):  
Neural Network ◽  
Power System ◽  
Bilevel Programming ◽  
Network Approach ◽  
Neural Network Approach ◽  
System Environment ◽  
Bilevel Programming Problems

scholarly journals A hybrid neural network approach to bilevel programming problems

2007 ◽  
Vol 20 (8) ◽  
pp. 880-884 ◽  
Author(s):  
Kuen-Ming Lan ◽  
Ue-Pyng Wen ◽  
Hsu-Shih Shih ◽  
E. Stanley Lee
Keyword(s):  
Neural Network ◽  
Bilevel Programming ◽  
Network Approach ◽  
Neural Network Approach ◽  
Hybrid Neural Network ◽  
Bilevel Programming Problems

scholarly journals Ensemble Neural Network Approach for Accurate Load Forecasting in a Power System

2009 ◽  
Vol 19 (2) ◽  
pp. 303-315 ◽  
Author(s):  
Krzysztof Siwek ◽  
Stanisław Osowski ◽  
Ryszard Szupiluk
Keyword(s):  
Neural Network ◽  
Power System ◽  
Mean Squared Error ◽  
Load Forecasting ◽  
Weighted Averaging ◽  
Percentage Error ◽  
Network Approach ◽  
Three Solutions ◽  
Neural Network Approach ◽  
Ensemble Neural Network

Ensemble Neural Network Approach for Accurate Load Forecasting in a Power SystemThe paper presents an improved method for 1-24 hours load forecasting in the power system, integrating and combining different neural forecasting results by an ensemble system. We will integrate the results of partial predictions made by three solutions, out of which one relies on a multilayer perceptron and two others on self-organizing networks of the competitive type. As the expert system we will apply different integration methods: simple averaging, SVD based weighted averaging, principal component analysis and blind source separation. The results of numerical experiments, concerning forecasting the hourly load for the next 24 hours of the Polish power system, will be presented and discussed. We will compare the performance of different ensemble methods on the basis of the mean absolute percentage error, mean squared error and maximum percentage error. They show a significant improvement of the proposed ensemble method in comparison to the individual results of prediction. The comparison of our work with the results of other papers for the same data proves the superiority of our approach.

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