Predicting residential energy consumption using CNN-LSTM neural networks

Tae Young Kim; Sung-Bae Cho

doi:10.1016/j.energy.2019.05.230

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Predicting residential energy consumption using CNN-LSTM neural networks

Tae Young Kim Sung-Bae Cho

Year: 2019 Journal: Energy Vol: 182 Pages: 72-81 Publisher: Elsevier BV

DOI: 10.1016/j.energy.2019.05.230

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Keywords:

Computer science Convolutional neural network Energy consumption Artificial neural network Consumption (sociology) Artificial intelligence Deep learning Mean squared error Energy (signal processing) Electricity Power (physics) Population Pattern recognition (psychology) Statistics Engineering Mathematics

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Topics

Energy Load and Power Forecasting

Physical Sciences → Engineering → Electrical and Electronic Engineering

Air Quality Monitoring and Forecasting

Physical Sciences → Environmental Science → Environmental Engineering

Smart Grid Energy Management

Physical Sciences → Engineering → Electrical and Electronic Engineering

Predicting residential energy consumption using CNN-LSTM neural networks

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