Predicting residential energy consumption using CNN-LSTM neural networks

Tae-Young Kim and Sung-Bae Cho

Energy, 2019, vol. 182, issue C, 72-81

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.

Keywords: Electric energy consumption; Deep learning; Convolutional neural network; Long short-term memory (search for similar items in EconPapers)
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (162)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544219311223
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:182:y:2019:i:c:p:72-81

DOI: 10.1016/j.energy.2019.05.230

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().