Title :
Short-Term Load Forecasting with Neural Network Ensembles: A Comparative Study [Application Notes]
Author :
De Felice, Matteo ; Yao, Xin
Author_Institution :
Enea, Italy
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.
Keywords :
demand side management; load forecasting; neural nets; power generation dispatch; power grids; power system management; pricing; electric grids; energy demand; energy generation; load forecasting; market-based dynamic pricing; neural network; power systems dispatching; power systems management; power systems scheduling; Artificial neural networks; Computational modeling; Data models; Forecasting; Load forecasting; Neural networks; Power system planning; Predictive models;
Journal_Title :
Computational Intelligence Magazine, IEEE
DOI :
10.1109/MCI.2011.941590