Name
Abstract | ||
|---|---|---|
Power demand forecasting is a critical task for achieving efficiency and reliability in power grid operation. Accurate forecasting allows grid operators to better maintain the balance of supply and demand as well as to optimize operational cost for generation and transmission. This article proposes a novel neural network architecture PowerNet, which can incorporate multiple heterogeneous features, such as historical energy consumption data, weather data, and calendar information, for the power demand forecasting task. Compared to two recent works based on Gradient Boosting Tree (GBT) and Support Vector Regression (SVR), PowerNet demonstrates a decrease of 33.3% and 14.3% in forecasting error, respectively. We further provide empirical results the two operational considerations that are crucial when using PowerNet in practice, i.e., how far in the future the model can forecast with a decent accuracy and how often we should re-train the forecasting model to retain its modeling capability. Finally, we briefly discuss a multilayer anomaly detection approach based on PowerNet. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Other Computer Science | Journal |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yao Cheng | 1 | 36 | 6.21 |
| Chang Xu | 2 | 77 | 7.27 |
| Daisuke Mashima | 3 | 86 | 13.02 |
| Vrizlynn L. L. Thing | 4 | 63 | 8.42 |
| Yongdong Wu | 5 | 356 | 43.05 |