Forecasting the short-term demand for electricity. Do neural networks stand a better chance?

0410494 - UTIA-B 20000210 RIV NL eng J - Journal Article
Darbellay, Georges A. - Sláma, Marek
Forecasting the short-term demand for electricity. Do neural networks stand a better chance?
International Journal of Forecasting. Roč. 16, č. 1 (2000), s. 71-83. ISSN 0169-2070. E-ISSN 1872-8200
R&D Projects: GA ČR GA102/95/1311
Institutional research plan: AV0Z1075907
Subject RIV: BB - Applied Statistics, Operational Research
Impact factor: 0.677, year: 2000

We address a problem faced by every supplier of electricity, i.e. forecasting the short-term electricity consumption. The introduction of new techniques has often been justifed by invoking the nonlinearity of the problem. First, we introduce a nonlinear measure of statistical dependence. Second, we analyse the linear and the nonlinear autocorrelation functions of the Czech electric comsumption. Third, we compare the predictions of nonlinear models with linear models.
Permanent Link: http://hdl.handle.net/11104/0130583