Predicting residential energy consumption using CNN-LSTM neural networks

Tae Young Kim, Sung Bae Cho

Research output: Contribution to journalArticlepeer-review

663 Citations (Scopus)


The rapid increase in human population and development in technology have sharply raised power consumption in today's world. Since electricity is consumed simultaneously as it is generated at the power plant, it is important to accurately predict the energy consumption in advance for stable power supply. In this paper, we propose a CNN-LSTM neural network that can extract spatial and temporal features to effectively predict the housing energy consumption. Experiments have shown that the CNN-LSTM neural network, which combines convolutional neural network (CNN) and long short-term memory (LSTM), can extract complex features of energy consumption. The CNN layer can extract the features between several variables affecting energy consumption, and the LSTM layer is appropriate for modeling temporal information of irregular trends in time series components. The proposed CNN-LSTM method achieves almost perfect prediction performance for electric energy consumption that was previously difficult to predict. Also, it records the smallest value of root mean square error compared to the conventional forecasting methods for the dataset on individual household power consumption. The empirical analysis of the variables confirms what affects to forecast the power consumption most.

Original languageEnglish
Pages (from-to)72-81
Number of pages10
Publication statusPublished - 2019 Sept 1

Bibliographical note

Funding Information:
This research was supported by Korea Electric Power Corporation (Grant number: R18XA05 ).

Publisher Copyright:
© 2019 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Pollution
  • Energy Engineering and Power Technology
  • Energy(all)
  • Electrical and Electronic Engineering
  • Management, Monitoring, Policy and Law
  • Industrial and Manufacturing Engineering
  • Building and Construction
  • Fuel Technology
  • Renewable Energy, Sustainability and the Environment
  • Civil and Structural Engineering
  • Modelling and Simulation


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