Abstract
The feasibility of using a simple neural network for short-term load forecasting is investigated. A combined linear and nonlinear neural network is developed. The forecasts are computed using weights which are reestimated using only very recent observations. The model operation is tested by using load data obtained from a winter-peaking utility in the Northeastern USA. The results show that the error in most weeks is small, less than 4-5%. This validation test proves that the method is feasible and able to produce accurate forecasts under normal conditions.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - IEEE International Symposium on Circuits and Systems |
| Publisher | Publ by IEEE |
| Pages | 2942-2945 |
| Number of pages | 4 |
| Volume | 4 |
| State | Published - 1990 |
| Event | 1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) - New Orleans, LA, USA Duration: May 1 1990 → May 3 1990 |
Other
| Other | 1990 IEEE International Symposium on Circuits and Systems Part 4 (of 4) |
|---|---|
| City | New Orleans, LA, USA |
| Period | 5/1/90 → 5/3/90 |
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ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials
Cite this
Conceptual approach to the application of neural network for short-term load forecasting. / Peng, T. M.; Hubele, N. F.; Karady, G. G.
Proceedings - IEEE International Symposium on Circuits and Systems. Vol. 4 Publ by IEEE, 1990. p. 2942-2945.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Conceptual approach to the application of neural network for short-term load forecasting
AU - Peng, T. M.
AU - Hubele, N. F.
AU - Karady, G. G.
PY - 1990
Y1 - 1990
N2 - The feasibility of using a simple neural network for short-term load forecasting is investigated. A combined linear and nonlinear neural network is developed. The forecasts are computed using weights which are reestimated using only very recent observations. The model operation is tested by using load data obtained from a winter-peaking utility in the Northeastern USA. The results show that the error in most weeks is small, less than 4-5%. This validation test proves that the method is feasible and able to produce accurate forecasts under normal conditions.
AB - The feasibility of using a simple neural network for short-term load forecasting is investigated. A combined linear and nonlinear neural network is developed. The forecasts are computed using weights which are reestimated using only very recent observations. The model operation is tested by using load data obtained from a winter-peaking utility in the Northeastern USA. The results show that the error in most weeks is small, less than 4-5%. This validation test proves that the method is feasible and able to produce accurate forecasts under normal conditions.
UR - http://www.scopus.com/inward/record.url?scp=0025636786&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0025636786&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0025636786
VL - 4
SP - 2942
EP - 2945
BT - Proceedings - IEEE International Symposium on Circuits and Systems
PB - Publ by IEEE
ER -