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Open Access

Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes

Department of Electronics and Telecommunications, Politecnico di Torino, Turin 10129, Italy
Department of Energy Engineering, Zarqa University, Zarqa 13110, Jordan
Shandong Provincial University Laboratory for Protected Horticulture, Weifang University of Science and Technology, Weifang 262799, China
Department of Electrical and Electronics Engineering, Faculty of Engineering and Architecture, Nişantaşı University, Istanbul 25370, The Republic of Türkiye
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Abstract

Energy management in smart homes is one of the most critical problems for the Quality of Life (QoL) and preserving energy resources. One of the relevant issues in this subject is environmental contamination, which threatens the world’s future. Green computing-enabled Artificial Intelligence (AI) algorithms can provide impactful solutions to this topic. This research proposes using one of the Recurrent Neural Network (RNN) algorithms known as Long Short-Term Memory (LSTM) to comprehend how it is feasible to perform the cloud/fog/edge-enabled prediction of the building’s energy. Four parameters of power electricity, power heating, power cooling, and total power in an office/home in cold-climate cities are considered as our features in the study. Based on the collected data, we evaluate the LSTM approach for forecasting parameters for the next year to predict energy consumption and online monitoring of the model’s performance under various conditions. Towards implementing the AI predictive algorithm, several existing tools are studied. The results have been generated through simulations, and we find them promising for future applications.

Keywords

design-builderBesossmart citiessmart buildingneural networkLong Short-Term Memory (LSTM)

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Tsinghua Science and Technology
Volume 29 Issue 1,
February 2024
Pages 143-157
DOI: 10.26599/TST.2023.9010008
Cite this article:
Akbarzadeh O, Hamzehei S, Attar H, et al. Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes. Tsinghua Science and Technology, 2024, 29(1): 143-157. https://doi.org/10.26599/TST.2023.9010008

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Received: 27 November 2022
Revised: 02 February 2023
Accepted: 13 February 2023
Published: 03 August 2023
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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