A hierarchical hybrid neural model with time integrators in long-term load forecasting

Autor:	Isaias Lima, Enzo Seraphim, Otavio A. S. Carpinteiro, J. Vantuil L. Pinto, Carlos A. M. Pinheiro, Edmilson M. Moreira
Rok vydání:	2009
Předmět:	Models of neural computation Artificial neural network Artificial Intelligence Computer science business.industry Multilayer perceptron Integrator Artificial intelligence Time series Perceptron business Software Term (time)
Zdroj:	Neural Computing and Applications. 18:1057-1063
ISSN:	1433-3058 0941-0643
DOI:	10.1007/s00521-009-0290-y
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c369072cca9ea62c850a3ead583f91eb https://doi.org/10.1007/s00521-009-0290-y Zobrazit plný text záznamu Plný text ve formátu PDF