| bibtype |
J -
Journal Article
|
| ARLID |
0410494 |
| utime |
20240103182218.0 |
| mtime |
20060210235959.9 |
| title
(primary) (eng) |
Forecasting the short-term demand for electricity. Do neural networks stand a better chance? |
| specification |
|
| serial |
| ARLID |
cav_un_epca*0251006 |
| ISSN |
0169-2070 |
| title
|
International Journal of Forecasting |
| volume_id |
16 |
| volume |
1 (2000) |
| page_num |
71-83 |
| publisher |
|
|
| author
(primary) |
| ARLID |
cav_un_auth*0101078 |
| name1 |
Darbellay |
| name2 |
Georges A. |
| institution |
UTIA-B |
| fullinstit |
Ústav teorie informace a automatizace AV ČR, v. v. i. |
|
| author
|
| ARLID |
cav_un_auth*0100826 |
| name1 |
Sláma |
| name2 |
Marek |
| institution |
UIVT-O |
| fullinstit |
Ústav informatiky AV ČR, v. v. i. |
|
| COSATI |
12B |
| cas_special |
| project |
| project_id |
GA102/95/1311 |
| agency |
GA ČR |
| ARLID |
cav_un_auth*0004300 |
|
| research |
AV0Z1075907 |
| abstract
(eng) |
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. |
| RIV |
BB |
| department |
SI |
| permalink |
http://hdl.handle.net/11104/0130583 |
| ID_orig |
UTIA-B 20000210 |
| arlyear |
2000 |
| mrcbU63 |
cav_un_epca*0251006 International Journal of Forecasting 0169-2070 1872-8200 Roč. 16 č. 1 2000 71 83 Elsevier |
|