A hierarchical hybrid neural model with time integrators in long-term load forecasting
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A novel hierarchical hybrid neural model to the problem of long-term load forecasting is proposed in this paper. The neural model is made up of two self-organizing map nets—one on top of the other—and a single-layer perceptron. It has application into domains which require time series analysis. The model is compared to a mutilated architecture of it, and to a multilayer perceptron. The hierarchical, the mutilated, and the multilayer perceptron models are trained and assessed on load data extracted from a North-American electric utility. They are required to predict either once every week or once every month the electric peak-load during the next two years. The results from the experiments show that the performance of HHNM on long-term load forecasts is better than that of the mutilated model, and much better than that of the MLP model.
KeywordsNeural networks Time-series forecasting Long-term electrical load forecasting
This research is supported by CNPq, Brazil.
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