Voltage Forecasting Methods for Voltage Monitoring and Control Strategies
Palabras clave:
Autoregressive model, exponential smoothing, forecasting, recurrent neural networks, power system, voltage control, voltage monitoring, voltage prediction (en)
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Traditional forecasting applications in energy industry are centered on electrical demand and variable energy resources. However, with the increasing number of studies conducted proposing novel data-driven approaches for volage monitoring and control strategies, voltage forecasting arises as a research field that could support this challenging task in the real-time operation context wherein decision-making supporting tools are becoming more relevant and necessary at control centers. The present work explores the performance of traditional parametric models and a recurrent neural network (RNN) model for voltage forecasting within different time horizons and on several system conditions. Real-world voltage time series data from the Colombian power system operation was used to test the performance of the reviewed approaches.
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Derechos de autor 2023 Simposio Internacional sobre la Calidad de la Energía Eléctrica - SICEL

Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.