Highly accurate energy consumption forecasting model based on parallel LSTM neural networks.
Ning JinFan YangYuchang MoYongkang ZengXiaokang ZhouKe YanXiang MaPublished in: Adv. Eng. Informatics (2022)
Keyphrases
- highly accurate
- energy consumption
- neural network
- electricity consumption
- wireless sensor networks
- recurrent neural networks
- energy saving
- energy efficient
- energy efficiency
- sensor networks
- energy conservation
- data transmission
- high quality
- routing protocol
- base station
- high accuracy
- data center
- capable of producing
- save energy
- accurate models
- sensor nodes
- load distribution
- back propagation
- artificial neural networks
- routing algorithm
- resource limitations
- total energy
- power management
- network architecture
- data gathering
- data aggregation
- multi hop
- parallel computing
- feed forward
- massively parallel
- data sets
- residual energy
- ambient intelligence
- active learning
- environmental impact
- stability margin