| Title | Short-term load forecasting with neural network ensembles: A comparative study |
|---|---|
| Publication Type | Articolo su Rivista peer-reviewed |
| Year of Publication | 2011 |
| Authors | De Felice, Matteo, and Yao X. |
| Journal | IEEE Computational Intelligence Magazine |
| Volume | 6 |
| Pagination | 47-56 |
| ISSN | 1556603X |
| Keywords | Accurate prediction, Comparative studies, Dispatch problems, Dynamic pricing, Electric grids, Electric load forecasting, Energy demands, Energy generations, Energy management, Forecasting, Load demand, Load forecasting, Neural network ensembles, Neural networks, Operation management, Scheduling, Short term load forecasting, Significant impacts, Time span |
| Abstract | Load Forecasting plays a critical role in the management, scheduling and dispatching operations in power systems, and it concerns the prediction of energy demand in different time spans. In future electric grids, to achieve a greater control and flexibility than in actual electric grids, a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected loads, and give vital information to make decisions on energy generation and purchase, especially market-based dynamic pricing strategies. Furthermore, accurate prediction would have a significant impact on operation management, e.g. preventing overloading and allowing an efficient energy storage. © 2011 IEEE. |
| Notes | cited By 42 |
| URL | https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960527815&doi=10.1109%2fMCI.2011.941590&partnerID=40&md5=aefe221e4c6b78f15e40e3397280e105 |
| DOI | 10.1109/MCI.2011.941590 |
| Citation Key | DeFelice201147 |
