Predicting residential energy consumption using CNN-LSTM neural networks

Tae Young Kim, Sung Bae Cho

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

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
JournalEnergy
Volume182
DOIs
Publication statusPublished - 2019 Sep 1

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Building and Construction
  • Pollution
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Predicting residential energy consumption using CNN-LSTM neural networks'. Together they form a unique fingerprint.

  • Cite this