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- Title
A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem.
- Authors
Belhaiza, Slim; Al-Abdallah, Sara
- Abstract
Demand response management (DRM) plays a crucial role in the prospective development of smart grids. The precise estimation of electricity demand for individual houses is vital for optimizing the operation and planning of the power system. Accurate forecasting of the required components holds significance as it can substantially impact the final cost, mitigate risks, and support informed decision-making. In this paper, a forecasting approach employing neural networks for smart grid demand-side management is proposed. The study explores various enhanced artificial neural network (ANN) architectures for forecasting smart grid consumption. The performance of the ANN approach in predicting energy demands is evaluated through a comparison with three statistical models: a time series model, an auto-regressive model, and a hybrid model. Experimental results demonstrate the ability of the proposed neural network framework to deliver accurate and reliable energy demand forecasts.
- Subjects
DEMAND forecasting; ARTIFICIAL neural networks; FORECASTING; ENERGY demand management; ELECTRIC power consumption; ENERGY consumption
- Publication
Energies (19961073), 2024, Vol 17, Issue 10, p2329
- ISSN
1996-1073
- Publication type
Article
- DOI
10.3390/en17102329