Predicting residential energy consumption using CNN-LSTM neural networks

By T. Kim, Sung-Bae Cho. Energy, 2019

1,302 citations recorded by the source corpus.

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.

Subfields: Electrical and Electronic Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/fbb6c557c0f44701f510c70581f625a5d2743194

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Rahim Barzegar
2. Jan Adamowski
3. Deshi Li
4. Trond Kvamsdal
5. Omer San
6. Mohammad Taghi Aalami
7. Adil Rasheed

In Environmental Engineering

1. Zhidi Lin
2. Rahim Barzegar
3. Jan Adamowski
4. Yue Xu
5. Feng Yin
6. Deshi Li
7. Qinglei Kong
8. Mohammad Taghi Aalami

Citations in the corpus

• A survey on long short-term memory networks for time series prediction (2021, positive): “Approaches for the incorporation of convolution operations into LSTM network are presented by [17] or [18].”
• FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “Data-driven, learning model-based solutions have demonstrated great data representation and generalization capability [11, 12, 13, 14].”
• A review of the-state-of-the-art in data-driven approaches for building energy prediction (2020, neutral): “[147] 1844352 204928 3 1 Time index, historical data, sub-metering data CNNLSTM Residential Electricity energy consumption Building 1min 1hour 60 MAE, MAPE, MSE, RMSE”
• Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model (2020, methodology): “001 was used after the pooling layer in the CNN to adjust for overfitting; the pooling layer can also be helpful to overcome overfitting (Kim and Cho 2019).”
• Digital Twin: Values, Challenges and Enablers From a Modeling Perspective (2019, background): “in [132] demonstrated its value in making predictions of the energy consumption in residential areas.”