LSTM-based Network for Current Harmonic Distortion Time Series Forecasting

Garrido-Zafra, Joaquin; Gil-de-Castro, Aurora; Calleja-Madueño, Antonio; Linan-Reyes, Matias; Moreno-Garcia, IM; Moreno-Munoz, Antonio

doi:10.1109/eeeic/icpseurope57605.2023.10194689

Cited by 1 publication

(2 citation statements)

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“…The research [27] presents a thorough assessment of the progress developed in using DL for forecasting PQ indices time series, revealing that this field is still developing. For this scenario, an LSTM network is proposed to predict the steady state of PQ indices time series, which assesses the current distortion at the point of common coupling (PCC) of a residence.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, many papers in specialized journals present research on the presence of harmonic currents, load unbalances, or overcurrents. In recent years, there has been a significant amount of research conducted on power quality and harmonic currents, focusing on the utilization of artificial intelligence and machine learning techniques [22][23][24][25][26][27][28][29][30]. Our research also focuses on utilizing deep learning and recurrent neural networks (RNN) for predicting the power factor in hot rolling mils factories.…”

Section: Introductionmentioning

confidence: 99%

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Power Factor Modelling and Prediction at the Hot Rolling Mills’ Power Supply Using Machine Learning Algorithms

Panoiu,

Ivascanu

2024

Mathematics

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The power supply is crucial in the present day due to the negative impacts of poor power quality on the electric grid. In this research, we employed deep learning methods to investigate the power factor, which is a significant indicator of power quality. A multi-step forecast was developed for the power factor in the power supply installation of a hot rolling mill, extending beyond the horizontal line. This was conducted using data obtained from the respective electrical supply system. The forecast was developed via hybrid RNN (recurrent neural networks) incorporating LSTM (long short-term memory) and GRU (gated recurrent unit) layers. This research utilized hybrid recurrent neural network designs with deep learning methods to build several power factor models. These layers have advantages for time series forecasting. After conducting time series forecasting, qualitative indicators of the prediction were identified, including the sMAPE (Symmetric Mean Absolute Percentage Error) and regression coefficient. In this paper, the authors examined the quality of applied models and forecasts utilizing these indicators, both in the short term and long term.

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Section: Literature Reviewmentioning

confidence: 99%