Electricity demand forecast model based on meteorological and historical demand data using artificial neural networks

Resumen

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ccurate forecasting of electricity demand is crucial for im[1]proving transmission system operation through optimized use of resour[1]ces, operation planning, and minimized outages. The dynamic of elec[1]tricity demand depends on exogenous factors (e.g., meteorological con[1]ditions), but the relationships between demand and factors are complex and nonlinear, posing a challenge for accurate prediction. With the aim of predicting electricity demand, this work explores the relationship with meteorological conditions for the province of Entre Ríos (Argentina). We propose a recurrent neural network model based on long short-term memories, which receives the raw input data without feature extraction. We evaluate its performance and compare it with a state-of[1]the-art method. The exploratory analysis of the data shows that temperature extremes present a strong influence on consumption patterns. The proposed mod[1]els achieve a performance of 0.77 in determination coefficient when com[1]paring predicted electricity demand with observations. This indicates the potential as a powerful tool for optimizing the system operation in Entre Ríos.