Short-term electric load forecasting is an important requirement for electric system operation. This paper employs a feed-forward neural network with a back-propagation algorithm for three types of short-term electric load forecasting: daily peak (valley) load, hourly load and the total load. The fo
β¦ LIBER β¦
Short-term load forecasting using an artificial neural network
β Scribed by Lee, K.Y.; Cha, Y.T.; Park, J.H.
- Book ID
- 120300346
- Publisher
- IEEE
- Year
- 1992
- Tongue
- English
- Weight
- 819 KB
- Volume
- 7
- Category
- Article
- ISSN
- 0885-8950
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
Short-term electric load forecasting usi
β
Subhes C. Bhattacharyya; Le Tien Thanh
π
Article
π
2004
π
John Wiley and Sons
π
English
β 131 KB
π 1 views
[IEEE 2010 12th International Conference
β
Hamid, M.B. Abdul; Rahman, T.K. Abdul
π
Article
π
2010
π
IEEE
β 542 KB
Very short-term load forecasting using a
β
Charytoniuk, W.; Chen, M.-S.
π
Article
π
2000
π
IEEE
π
English
β 470 KB
Short term load forecasting using an ada
β
T.S. Dillon; S. Sestito; S. Leung
π
Article
π
1991
π
Elsevier Science
π
English
β 517 KB
Weather sensitive short-term load foreca
β
Chen, S.-T.; Yu, D.C.; Moghaddamjo, A.R.
π
Article
π
1992
π
IEEE
π
English
β 680 KB
Short-term inflow forecasting using an a
β
Z. X. Xu; J. Y. Li
π
Article
π
2002
π
John Wiley and Sons
π
English
β 230 KB
π 1 views
## Abstract The primary objective of this study is to investigate the possibility of including more temporal and spatial information on shortβterm inflow forecasting, which is not easily attained in the traditional timeβseries models or conceptual hydrological models. In order to achieve this objec