Name
Title | ||
|---|---|---|
Sliding Window Regression based Short-Term Load Forecasting of a Multi-Area Power System |
Abstract | ||
|---|---|---|
Short term load forecasting has an essential medium for reliable, economical and efficient operation of power system. Most of the existing forecasting approaches utilize fixed statistical models with large historical data for training the models. However, due to the recent integration of large distributed generation, the nature of load demand has become dynamic. Thus because of the dynamic nature of the power load demand, the performance of these models may deteriorate over time. To accommodate the dynamic nature of the load demands, we propose sliding window regression based dynamic model to predict the load demands of the multi-area power system. The proposed algorithm is tested on five zones of New York ISO. Results from our proposed algorithm are compared with four existing techniques to validate the performance superiority of the proposed algorithm. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CCECE.2019.8861915 | 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) |
Keywords | Field | DocType |
Rolling window regression,power load demand forecasting,multi-area power system,New York ISO | Sliding window protocol,Regression,Computer science,Control theory,Electric power system,Load forecasting,Statistical model,Distributed generation | Conference |
ISSN | ISBN | Citations |
0840-7789 | 978-1-7281-0320-4 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irfan Ahmad Khan | 1 | 0 | 0.34 |
| Adnan Akber | 2 | 0 | 0.34 |
| Yinliang Xu | 3 | 29 | 1.97 |