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

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

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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(Al)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 Al predictive algorithm,several existing tools are studied.The results have been generated through simulations,and we find them promising for future applications.

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

Omid Akbarzadeh、Sahand Hamzehei、Hani Attar、Ayman Amer、Nazanin Fasihihour、Mohammad R.Khosravi、Ahmed A.Solyman

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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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2024

10.26599/TST.2023.9010008

清华大学学报自然科学版(英文版)
清华大学

清华大学学报自然科学版(英文版)

CSTPCDEI
影响因子：0.474
ISSN：1007-0214
年,卷(期)：2024.29(1)
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