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 threate...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 promisingforfutureapplications.展开更多
文摘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 promisingforfutureapplications.
