Journal Home > Volume 29 , Issue 1
Tsinghua Science and Technology 2024, 29(1): 143-157 https://doi.org/10.26599/TST.2023.9010008
Open Access | Issue | Published: 03 August 2023

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

Show Author's Information Omid Akbarzadeh1( ),  Sahand Hamzehei1,  Hani Attar2,  Ayman Amer2,  Nazanin Fasihihour1,  Mohammad R. Khosravi3,  Ahmed A. Solyman4
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
Keywords:
Long Short-Term Memory (LSTM), smart cities, neural network, smart building, design-builder, Besos
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
Download citation
EndNote(RIS)
BibTeX

736

Views

221

Downloads

Citations

2

Crossref

0

WoS

1

Scopus

0

CSCD

Abstract Full text About this article

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.


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Omid Akbarzadeh1( ), Sahand Hamzehei1, Hani Attar2, Ayman Amer2, Nazanin Fasihihour1, Mohammad R. Khosravi3, Ahmed A. Solyman4
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

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: Long Short-Term Memory (LSTM), smart cities, neural network, smart building, design-builder, Besos

References(48)

[1]
K. R. Systems, What will happen if the level of air pollution continues to increase? https://www.kent.co.in/blog/what-will-happen-if-thelevel-of-air-pollution-continues-to-increase, 2020.
[2]
O. Akbarzadeh, Evaluating latency in a 5G infrastructure for ultralow latency applications, http://webthesis.biblio.polito.it/id/eprint/22652, 2021.
[3]
Enel X, Do electric cars cause pollution? How do they reduce it? — Enel X, https://corporate.enelx.com/en/question-and-answers/how-much-does-an-electric-car-pollute, 2022.
[4]
Business Watch, What are the benefits of smart buildings? https://www.businesswatchgroup.co.uk/what-are-the-benefits-of-smart-buildings/, 2021.
[5]
O. Akbarzadeh, M. Baradaran, and M. R. Khosravi, IoT-based smart management of healthcare services in hospital buildings during COVID-19 and future pandemics, Wireless Commun. Mobile Comput., vol. 2021, p. 5533161, 2021.
DOI Google Scholar
[6]
Is machine learning the next big thing in smart buildings?—Intelligent building Europe, www.intelligentbuildingeurope.com, 2022.
[7]
Altensis, DesignBuilder Software, https://www.altensis.com/en/services/designbuildersoftware, 2015.
[8]
AccuWeather, 5 of the coldest cities in the world, (in Chinese), https://www.accuweather.com/en/weather-news/5-of-thecoldest-cities-in-the-world-2/434260, 2022.
[9]
A. W. Khan, M. U. Khan, J. A. Khan, A. Ahmad, K. Khan, M. Zamir, W. Kim, and M. F. Ijaz, Analyzing and evaluating critical challenges and practices for software vendor organizations to secure big data on cloud computing: An AHP-based systematic approach, IEEE Access, vol. 9, pp. 107309–107332, 2021.
DOI Google Scholar
[10]
BESOS, BESOS: Building and energy systems optimization and surrogate-modelling, https://pypi.org/project/besos/, 2022.
[11]
L. Troup, R. Phillips, M. J. Eckelman, and D. Fannon, Effect of window-to-wall ratio on measured energy consumption in US office buildings, Energy Build., vol. 203, p. 109434, 2019.
DOI Google Scholar
[12]
Calculator Academy, Window to wall ratio calculator- calculator academy, calculator. academy, https://calculator.academy/window-to-wall-ratiocalculator/, 2022.
[13]
CIOB, Glazing-designing buildings, https://www.designingbuildings.co.uk/wiki/Glazing, 2022.
[14]
Y. Q. Xiao, B. H. Qi, and W. Dong, Research on the layout of external shading components and the design of microstructure in combined-façade of subtropical architecture, in Proc. 2011 Int. Conf. on Remote Sensing, Environment and Transportation Engineering, Nanjing, China, 2011, pp. 1519–1524.
DOI Google Scholar
[15]
YourHome, Shading, https://www.yourhome.gov.au/passive-design/shading, 2022.
[16]
Energlaze, U-values explained, https://www.energlaze.ie/u-values-explained/, 2022.
[17]
TheGreenAge, Getting to grips with U-values, https://www.tegreenage.co.uk/getting-to-grips-with-u-values/, 2022.
[18]
EnergyPlus, https://energyplus.net/, 2022.
[19]
C. Symonds, How to detect a performance gap (+5 of the most common causes)? https://factorialhr.com/blog/performance-gap/, 2022.
[20]
C. Robinson, B. Dilkina, J. Hubbs, W. W. Zhang, S. Guhathakurta, M. A. Brown, and R. M. Pendyala, Machine learning approaches for estimating commercial building energy consumption, Appl. Energy, vol. 208, pp. 889–904, 2017.
DOI Google Scholar
[21]
M. Bourdeau, X. Q. Zhai, E. Nefzaoui, X. F. Guo, and P. Chatellier, Modeling and forecasting building energy consumption: A review of data-driven techniques, Sustainable Cities Soc., vol. 48, p. 101533, 2019.
DOI Google Scholar
[22]
S. Seyedzadeh, F. P. Rahimian, I. Glesk, and M. Roper, Machine learning for estimation of building energy consumption and performance: A review, Visualization Eng., vol. 6, no. 1, p. 5, 2018.
DOI Google Scholar
[23]
Investopedia, Understanding time series, https://www.investopedia.com/terms/t/timeseries.asp, 2022.
[24]
Key Differences, Difference between forecasting and prediction, https://keydifferences.com/difference-between-forecasting-and-prediction.html, 2022.
[25]
R. Gandhi, Support vector machine—Introduction to machine learning algorithms, https://towardsdatascience.com/supportvector-machine-introduction-to-machine-learningalgorithms-934a444fca47, 2022.
[26]
Analytics Vidhya, SVM—Support vector machine algorithm in machine learning, https://www.analyticsvidhya.com/blog/2017/09/understaingsupport-vector-machine-example-code/, 2022.
[27]
Q. Li, Q. L. Meng, J. J. Cai, H. Yoshino, and A. Mochida, Applying support vector machine to predict hourly cooling load in the building, Appl. Energy, vol. 86, no. 10, pp. 2249–2256, 2009.
DOI Google Scholar
[28]
Q. Li, Q. L. Meng, J. J. Cai, H. Yoshino, and A. Mochida, Predicting hourly cooling load in the building: A comparison of support vector machine and different artificial neural networks, Energy Convers. Manage., vol. 50, no. 1, pp. 90–96, 2009.
DOI Google Scholar
[29]
H. X. Zhao and F. Magoulès, Parallel support vector machines applied to the prediction of multiple buildings energy consumption, J. Algorithms Comput. Technol., vol. 4, no. 2, pp. 231–249, 2010.
DOI Google Scholar
[30]
J. Brownlee, Gaussian processes for classification with python, https://machinelearningmastery.com/gaussian-processes-for-classification-with-python/, 2020.
[31]
Y. Heo, R. Choudhary, and G. A. Augenbroe, Calibration of building energy models for retrofit analysis under uncertainty, Energy Build., vol. 47, pp. 550–560, 2012.
DOI Google Scholar
[32]
Y. Heo and V. M. Zavala, Gaussian process modeling for measurement and verification of building energy savings, Energy and Build., vol. 53, pp. 7–18, 2012.
DOI Google Scholar
[33]
javaTpoint, Artificial neural network tutorial, https://www.javatpoint.com/artificial-neural-network, 2022.
[34]
M. A. Azadeh and S. Sohrabkhani, Annual electricity consumption forecasting with neural network in high energy consuming industrial sectors of Iran, in Proc. 2006 IEEE Int. Conf. on Industrial Technology, Mumbai, India, 2006, pp. 2166–2171.
DOI Google Scholar
[35]
R. Olu-Ajayi, H. Alaka, I. Sulaimon, F. Sunmola, and S. Ajayi, Building energy consumption prediction for residential buildings using deep learning and other machine learning techniques, J. Build. Eng., vol. 45, p. 103406, 2022.
DOI Google Scholar
[36]
J. Brownlee, Difference between a batch and an epoch in a neural network, https://machinelearningmastery.com/difference-between-a-batch-and-an-epoch/, 2022.
[37]
S. Hamzehei, Gateways and wearable tools for monitoring patient movements in a hospital environment, https://webthesis.biblio.polito.it/22711/, 2022.
[38]
K. Rezaee, G. Jeon, M. R. Khosravi, H. H. Attar, and A. Sabzevari, Deep learning-based microarray cancer classification and ensemble gene selection approach, IET Syst. Biol., vol. 16, nos. 3&4, pp. 120–131, 2022.
DOI Google Scholar
[39]
L. Z. Kong, G. S. Li, W. Rafique, S. G. Shen, Q. He, M. R. Khosravi, R. L. Wang, and L. Y. Qi, Time-aware missing healthcare data prediction based on ARIMA model, IEEE/ACM Trans. Comput. Biol. Bioinf., .
DOI Google Scholar
[40]
F. Wang, L. N. Wang, G. S. Li, Y. L. Wang, C. Lv, and L. Y. Qi, Edge-cloud-enabled matrix factorization for diversified APIs recommendation in mashup creation, World Wide Web, vol. 25, no. 5, pp. 1809–1829, 2022.
DOI Google Scholar
[41]
L. Z. Kong, L. N. Wang, W. W. Gong, C. Yan, Y. C. Duan, and L. Y. Qi, LSH-aware multitype health data prediction with privacy preservation in edge environment, World Wide Web, vol. 25, no. 5, pp. 1793–1808, 2022.
DOI Google Scholar
[42]
F. Wang, H. B. Zhu, G. Srivastava, S. C. Li, M. R. Khosravi, and L. Y. Qi, Robust collaborative filtering recommendation with user-item-trust records, IEEE Trans. Comput. Soc. Syst., vol. 9, no. 4, pp. 986–996, 2022.
DOI Google Scholar
[43]
L. Y. Qi, W. M. Lin, X. Y. Zhang, W. C. Dou, X. L. Xu, and J. J. Chen, A correlation graph based approach for personalized and compatible web APIs recommendation in mobile APP development, IEEE Trans. Knowl. Data Eng., vol. 35, no. 6, pp. 5444–5457, 2023.
Google Scholar
[44]
C. M. L. Etoundi, J. De Dieu Nkapkop, N. Tsafack, J. M. Ngono, P. Ele, M. Wozniak, J. Shafi, and M. F. Ijaz, A novel compound-coupled hyperchaotic map for image encryption, Symmetry, vol. 14, no. 3, p. 493, 2022.
DOI Google Scholar
[45]
C. Chen, C. Y. Wang, B. Liu, C. He, L. Cong, and S. Wan, Edge intelligence empowered vehicle detection and image segmentation for autonomous vehicles, IEEE Trans. Intell. Transp. Syst., .
DOI Google Scholar
[46]
Y. R. Wu, H. F. Guo, C. Chakraborty, M. Khosravi, S. Berretti, and S. H. Wan, Edge computing driven low-light image dynamic enhancement for object detection, IEEE Trans. Netw. Sci. Eng., .
DOI Google Scholar
[47]
C. Chen, H. F. Li, H. Li, R. F. Fu, Y. Y. Liu, and S. H. Wan, Efficiency and fairness oriented dynamic task offloading in internet of vehicles, IEEE Trans. Green Commun. Netw., vol. 6, no. 3, pp. 1481–1493, 2022.
DOI Google Scholar
[48]
Y. Xia, S. R. Qu, and S. H. Wan, Scene guided colorization using neural networks, Neural Comput. Appl., vol. 34, no. 13, pp. 11083–11096, 2022.
DOI Google Scholar
Publication history
Copyright
Rights and permissions

Publication history

Received: 27 November 2022
Revised: 02 February 2023
Accepted: 13 February 2023
Published: 03 August 2023
Issue date: February 2024

Copyright

© The author(s) 2024.

Rights and permissions

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/).

Return