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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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4062 |
| _version_ | 1827663964837773312 |
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| author | Sameh Mahjoub Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche |
| author_facet | Sameh Mahjoub Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche |
| author_sort | Sameh Mahjoub |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T00:52:43Z |
| format | Article |
| id | doaj.art-3f5123bc84534658b3f50366894b7259 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T00:52:43Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-3f5123bc84534658b3f50366894b72592023-11-23T14:48:01ZengMDPI AGSensors1424-82202022-05-012211406210.3390/s22114062Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural NetworksSameh Mahjoub0Larbi Chrifi-Alaoui1Bruno Marhic2Laurent Delahoche3Laboratory of Innovative Technology (LTI, UR 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR 3899), University of Picardie Jules Verne, 80000 Amiens, FranceLaboratory of Innovative Technology (LTI, UR 3899), University of Picardie Jules Verne, 80000 Amiens, FranceWith 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.https://www.mdpi.com/1424-8220/22/11/4062neural networkstime seriesLSTMGRUDrop-GRUenergy consumption prediction |
| spellingShingle | Sameh Mahjoub Larbi Chrifi-Alaoui Bruno Marhic Laurent Delahoche Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks Sensors neural networks time series LSTM GRU Drop-GRU energy consumption prediction |
| 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 | neural networks time series LSTM GRU Drop-GRU energy consumption prediction |
| url | https://www.mdpi.com/1424-8220/22/11/4062 |
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