Towards Efficient Electricity Forecasting in Residential and Commercial Buildings: A Novel Hybrid CNN with a LSTM-AE based Framework

Sensors
Zulfiqar Ahmad KhanSung Wook Baik

Abstract

Due to industrialization and the rising demand for energy, global energy consumption has been rapidly increasing. Recent studies show that the biggest portion of energy is consumed in residential buildings, i.e., in European Union countries up to 40% of the total energy is consumed by households. Most residential buildings and industrial zones are equipped with smart sensors such as metering electric sensors, that are inadequately utilized for better energy management. In this paper, we develop a hybrid convolutional neural network (CNN) with an long short-term memory autoencoder (LSTM-AE) model for future energy prediction in residential and commercial buildings. The central focus of this research work is to utilize the smart meters' data for energy forecasting in order to enable appropriate energy management in buildings. We performed extensive research using several deep learning-based forecasting models and proposed an optimal hybrid CNN with the LSTM-AE model. To the best of our knowledge, we are the first to incorporate the aforementioned models under the umbrella of a unified framework with some utility preprocessing. Initially, the CNN model extracts features from the input data, which are then fed to the LSTM-encoder t...Continue Reading

References

Oct 23, 1997·Neural Computation·S Hochreiter, J Schmidhuber
Jun 16, 2018·Computers in Biology and Medicine·Shu Lih OhU Rajendra Acharya

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Citations

Aug 1, 2020·Sensors·Calvin Janitra Halim, Kazuhiko Kawamoto
Nov 19, 2020·Sensors·Eva Lucas SegarraCarlos Fernández Bandera
Jun 3, 2021·Sensors·Eva Lucas SegarraCarlos Fernández Bandera

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Software Mentioned

AE
Keras
TensorFlow
LSTM

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