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 |
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Electronic, Optical and Magnetic Materials