A CNN-LSTM Model Trained with Grey Wolf Optimizer for Prediction of Household Power Consumption
- Resource Type
- Conference
- Authors
- Gottam, Shilpa; Nanda, Satyasai Jagannath; Maddila, Ravi Kumar
- Source
- 2021 IEEE International Symposium on Smart Electronic Systems (iSES) ISES Smart Electronic Systems (iSES), 2021 IEEE International Symposium on. :355-360 Dec, 2021
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Computing and Processing
Signal Processing and Analysis
Transportation
Training
Power demand
Computational modeling
Simulation
Predictive models
Hybrid power systems
Convolutional neural networks
Grey wolf optimization
Convolutional neural network
Long short-term memory
Deep learning
- Language
Recent trends in research reveal evolution of hybrid machine learning models based on deep neural networks and nature inspired computing. In this paper, a combined model of convolutional neural network (CNN) and long-short term memory (LSTM) termed as CNN-LSTM network has been used for modelling. A popular swarm intelligence technique Grey Wolf optimizer (GWO) is used to compute the meaningful and best hyper-parameters of the CNN-LSTM network. The GWO algorithm has become popular due to its ability of fast convergence and determining accurate solutions among other meta-heuristic techniques. The proposed hybrid model has been suitably applied to predict the household power consumption. Simulation results reveal the superior accuracy achieved by the proposed model compared to the same CNN-LSTM model trained with particle swarm optimization, artificial bee colony and social spider optimization.