Short-term peak load forecasting: Statistical methods versus Artificial Neural Networks
| Autor: | Francisco Sandoval Hernández, Francisco Javier Marín Martín |
|---|---|
| Rok vydání: | 1997 |
| Předmět: |
Artificial neural network
Series (mathematics) Computer science business.industry Computation Computer Science::Neural and Evolutionary Computation Machine learning computer.software_genre Term (time) Mean absolute percentage error Robustness (computer science) Peak load Artificial intelligence business computer Selection (genetic algorithm) |
| Zdroj: | Biological and Artificial Computation: From Neuroscience to Technology ISBN: 9783540630470 IWANN |
| DOI: | 10.1007/bfb0032594 |
| Popis: | Two practical techniques: Time Series (TS) and Artificial Neural Networks (ANN), for the one-step-ahead short-term peak load forecasting have been proposed and discussed in this paper. We use weather variables since it is well known that better forecasting performances can be obtained taking them into account. The order selection of TS and the number input neurons of the ANN have are based on the computation of correlation functions. Their performances are evaluated through a simulation study. An extensive test activity of the two techniques shows that have better forecasting accuracy and robustness ANN models. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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