articleTop 1% cited
Predicting residential energy consumption using CNN-LSTM neural networks
Energy · 2019 · Vol. 182 · pp. 72–81
Tae Young Kim(Yonsei University)Sung-Bae Cho✉(Yonsei University)
Energy Load and Power ForecastingAir Quality Monitoring and ForecastingSmart Grid Energy ManagementComputer scienceConvolutional neural networkEnergy consumptionArtificial neural networkConsumption (sociology)Artificial intelligenceDeep learningMean squared errorEnergy (signal processing)Electricity
Funding
- Korea Electric Power Corporation
Citations
1,390
FWCI
67.45
field-weighted impact
References
53
Percentile
100%
vs. same field & year
Citations per year
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Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks
Sensors · 2017 · 722 citations
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN
IEEE Transactions on Smart Grid · 2017 · 1,063 citations
Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network
IEEE Transactions on Smart Grid · 2017 · 2,423 citations
Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
IEEE Access · 2017 · 741 citations
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