Interpretable Deep Learning with Hybrid Autoencoders to Predict Electric Energy Consumption.
Jin-Young KimSung-Bae ChoPublished in: SOCO (2020)
Keyphrases
- energy consumption
- deep learning
- restricted boltzmann machine
- deep belief networks
- energy saving
- wireless sensor networks
- sensor networks
- energy efficient
- unsupervised learning
- energy efficiency
- energy conservation
- data transmission
- machine learning
- unsupervised feature learning
- data center
- sensor nodes
- base station
- power management
- denoising
- routing algorithm
- routing protocol
- save energy
- mental models
- energy aware
- total energy
- environmental impact
- residual energy
- multi hop
- electricity consumption
- weakly supervised
- probabilistic model
- reinforcement learning
- multiscale
- sensor data
- stability margin
- data mining