Principal component neural networks: theory and applications
Principal component neural networks: theory and applications
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Daily Electrical Power Curves: Classification and Forecasting Using a Kohonen Map
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Methodology for long-term prediction of time series
Neurocomputing
Methods of integration of ensemble of neural predictors of time series - comparative analysis
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part I
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The paper presents the neural network approach to the precise 24-hour load pattern prediction for the next day in the power system. In this approach we use the ensemble of few neural network predictors working in parallel. The predicted series containing 24 values of the load pattern generated by the neural predictors are combined together using principal component analysis. Few principal components form the input vector for the final stage predictor composed of another neural network. The developed system of prediction was tested on the real data of the Polish Power System. The results have been compared to the appropriate values generated by other methods.