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Export Reference (APA) 
Martins, A. A. A. F., Pereira, F., Reis, F., Canacsinh, H., Lagarto, J., Cardoso, M. G. M. S....Amorim, M. J. (2025). SHORT-TERM ELECTRIC GRID LOAD FORECASTING. 7th International Conference on Numerical and Symbolic Computation.
Export Reference (IEEE) 
A. A. Martins et al.,  "SHORT-TERM ELECTRIC GRID LOAD FORECASTING", in 7th Int. Conf. on Numerical and Symbolic Computation, 2025
Export BibTeX 
@misc{martins2025_1765574680681,
	author = "Martins, A. A. A. F. and Pereira, F. and Reis, F. and Canacsinh, H. and Lagarto, J. and Cardoso, M. G. M. S. and Amorim, M. J.",
	title = "SHORT-TERM ELECTRIC GRID LOAD FORECASTING",
	year = "2025",
	url = "https://symcomp2025.isel.pt/"
}
Export RIS 
TY  - CPAPER
TI  - SHORT-TERM ELECTRIC GRID LOAD FORECASTING
T2  - 7th International Conference on Numerical and Symbolic Computation
AU  - Martins, A. A. A. F.
AU  - Pereira, F.
AU  - Reis, F.
AU  - Canacsinh, H.
AU  - Lagarto, J.
AU  - Cardoso, M. G. M. S.
AU  - Amorim, M. J.
PY  - 2025
UR  - https://symcomp2025.isel.pt/
AB  - This paper addresses the problem of short-term load forecasting on an electric
power grid. Accurate load prediction plays an important role on multiple aspects of electric grid operation and management, including risk assessment, maintenance and outage planning, coordination between different grid operators, contributing to improve efficiency and resiliency.
A prediction model based on artificial neural networks are employed to process data from the Portuguese power grid with a 15 minute sampling interval. In addition to the grid load data, additional inputs were added, including weather information and results from clustering of the time series. The system produces a 24 hour load forecast, for each 15 minute.
ER  -