Predicting Energy Consumption Using LSTM, Multi-Layer GRU and Drop-GRU Neural Networks
University of Sfax · Laboratoire des technologies innovantes · +1 more institution
Abstract
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…
Citation impact
- FWCI
- 20.49
- Percentile
- 100%
- References
- 27
Authors
4- SMSameh MahjoubCorresponding
University of Sfax, Laboratoire des technologies innovantes, Université de Picardie Jules Verne
- LCLarbi Chrifi‐Alaoui
Laboratoire des technologies innovantes, Université de Picardie Jules Verne
- BMBruno Marhic
Laboratoire des technologies innovantes, Université de Picardie Jules Verne
- LDLaurent Delahoche
Laboratoire des technologies innovantes, Université de Picardie Jules Verne
Topics & keywords
- Computer science
- Artificial neural network
- Energy consumption
- Time series
- Artificial intelligence
- Power consumption
- Consumption (sociology)
- Deep learning
- Affordable and clean energy