Share and Export
Share and Export
Fay, Damien, Ringwood, John, Condon, Marissa and Kelly, Michael (2003) 24-h electrical load data — a sequential or partitioned time series? Neurocomputing, 55 (3-4). pp. 469-498. ISSN 0925-2312
Preview
JR-24-hour-2003.pdf
Available under License Creative Commons Attribution Non-commercial Share Alike.
Download (545kB) | Preview
Abstract
Variations in electrical load are, among other things, hour of the day dependent, introducing a dilemma for the forecaster: whether to partition the data and use a separate model for each hour of the day (the parallel approach), or use a single model (the sequential approach). This paper examines which approach is appropriate for forecasting hourly electrical load in Ireland. It is found that, with the exception of some hours of the day, the sequential approach is superior. The final solution however, uses a combination of linear sequential and parallel neural models in a multi-time scale formulation.
| Item Type: | Article |
|---|---|
| Keywords: | Load forecasting; Time series analysis; Multi-layer perceptrons; Principal component analysis; |
| Academic Unit: | Faculty of Science and Engineering > Electronic Engineering |
| Item ID: | 9504 |
| Identification Number: | 10.1016/S0925-2312(03)00390-4 |
| Depositing User: | Professor John Ringwood |
| Date Deposited: | 01 Jun 2018 14:05 |
| Journal or Publication Title: | Neurocomputing |
| Publisher: | Elsevier |
| Refereed: | Yes |
| Related URLs: | |
| URI: | https://mural.maynoothuniversity.ie/id/eprint/9504 |
| Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
Repository Staff Only (login required)
- Item control page
Downloads
Downloads per month over past year
