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Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks

With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wasta...

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Autores principales: Mahjoub, Sameh, Chrifi-Alaoui, Larbi, Marhic, Bruno, Delahoche, Laurent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185376/
https://www.ncbi.nlm.nih.gov/pubmed/35684681
http://dx.doi.org/10.3390/s22114062
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author Mahjoub, Sameh
Chrifi-Alaoui, Larbi
Marhic, Bruno
Delahoche, Laurent
author_facet Mahjoub, Sameh
Chrifi-Alaoui, Larbi
Marhic, Bruno
Delahoche, Laurent
author_sort Mahjoub, Sameh
collection PubMed
description With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment.
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spelling pubmed-91853762022-06-11 Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks Mahjoub, Sameh Chrifi-Alaoui, Larbi Marhic, Bruno Delahoche, Laurent Sensors (Basel) Article With the steep rise in the development of smart grids and the current advancement in developing measuring infrastructure, short term power consumption forecasting has recently gained increasing attention. In fact, the prediction of future power loads turns out to be a key issue to avoid energy wastage and to build effective power management strategies. Furthermore, energy consumption information can be considered historical time series data that are required to extract all meaningful knowledge and then forecast the future consumption. In this work, we aim to model and to compare three different machine learning algorithms in making a time series power forecast. The proposed models are the Long Short-Term Memory (LSTM), the Gated Recurrent Unit (GRU) and the Drop-GRU. We are going to use the power consumption data as our time series dataset and make predictions accordingly. The LSTM neural network has been favored in this work to predict the future load consumption and prevent consumption peaks. To provide a comprehensive evaluation of this method, we have performed several experiments using real data power consumption in some French cities. Experimental results on various time horizons show that the LSTM model produces a better result than the GRU and the Drop-GRU forecasting methods. There are fewer prediction errors and its precision is finer. Therefore, these predictions based on the LSTM method will allow us to make decisions in advance and trigger load shedding in cases where consumption exceeds the authorized threshold. This will have a significant impact on planning the power quality and the maintenance of power equipment. MDPI 2022-05-27 /pmc/articles/PMC9185376/ /pubmed/35684681 http://dx.doi.org/10.3390/s22114062 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Mahjoub, Sameh
Chrifi-Alaoui, Larbi
Marhic, Bruno
Delahoche, Laurent
Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title_full Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title_fullStr Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title_full_unstemmed Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title_short Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
title_sort predicting energy consumption using lstm, multi-layer gru and drop-gru neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9185376/
https://www.ncbi.nlm.nih.gov/pubmed/35684681
http://dx.doi.org/10.3390/s22114062
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