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• W4386041566 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." @default.
• W4386041566 created "2023-08-22" @default.
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• W4386041566 date "2024-02-01" @default.
• W4386041566 modified "2023-10-06" @default.
• W4386041566 title "Heating-Cooling Monitoring and Power Consumption Forecasting Using LSTM for Energy-Efficient Smart Management of Buildings: A Computational Intelligence Solution for Smart Homes" @default.
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• W4386041566 doi "https://doi.org/10.26599/tst.2023.9010008" @default.
• W4386041566 hasPublicationYear "2024" @default.
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