A Neural Network Forecasting Approach for the Smart Grid Demand Response Management Problem

Slim Belhaiza () and Sara Al-Abdallah
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Slim Belhaiza: Department of Mathematics and IRC for Smart Logistics and Mobility, College of Computing & Mathematics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
Sara Al-Abdallah: Department of Mathematics, College of Computing & Mathematics, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia

Energies, 2024, vol. 17, issue 10, 1-14

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.

Keywords: forecasting; neural networks; smart grid (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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