Smart home automation system for energy consumption using tensorflow-based deep ensemble learning
Umamageswari, S.
Smart home automation system for energy consumption using tensorflow-based deep ensemble learning - Vol.14(3), Jan - Chennai ICT Academy 2024 - 3282-3292p.
Over the past decades, the evolution of new wireless technology has led
to increased attention toward Smart Home Automation Systems
(SHAS). In the smart home, numerous smart devices are
interconnected with the proliferation of the Internet of Things (IoT)
technology to provide users with a more comfortable lifestyle. Prior
research on the smart home system has enacted machine learning and
deep learning techniques to forecast the consecutive activities in the
smart home. This research paper aims to enhance the future decision-
making of energy consumption with the assistance of environmental
factors and home appliances by exploiting the Tensorflow-based deep
ensemble learning technique. The enhancement of future decision-
making in smart home automation systems primarily involves the
classification of energy consumption levels from the knowledge of
external environmental factors and energy consumption levels of home
appliances through the phases of data preprocessing, feature selection,
fuzzy logic-based data labeling, and finally, classification of energy
consumption using TensorFlow based deep ensemble learning
technique. The data obtained from the effective feature selection
technique is subjected to labeling via the fuzzy logic system to classify
the energy consumption of smart home appliances. Finally, this work
classifies the level of energy consumption based on the labeled
knowledge of smart home data using a tensorflow-based deep ensemble
learning model. The experimental model implements the proposed deep
ensemble learning model in the tensorflow framework, which improves
the decision-making performance of energy utilization in the smart
home system. Experimental results illustrate that the proposed deep
ensemble learning model yields superior classification performance
than the other baseline classifiers, such as Artificial Neural Network
(ANN), Convolutional Neural Network (CNN), and Long Short-Term
Memory (LSTM) on the smart home dataset.
Computer Engineering
Smart home automation system for energy consumption using tensorflow-based deep ensemble learning - Vol.14(3), Jan - Chennai ICT Academy 2024 - 3282-3292p.
Over the past decades, the evolution of new wireless technology has led
to increased attention toward Smart Home Automation Systems
(SHAS). In the smart home, numerous smart devices are
interconnected with the proliferation of the Internet of Things (IoT)
technology to provide users with a more comfortable lifestyle. Prior
research on the smart home system has enacted machine learning and
deep learning techniques to forecast the consecutive activities in the
smart home. This research paper aims to enhance the future decision-
making of energy consumption with the assistance of environmental
factors and home appliances by exploiting the Tensorflow-based deep
ensemble learning technique. The enhancement of future decision-
making in smart home automation systems primarily involves the
classification of energy consumption levels from the knowledge of
external environmental factors and energy consumption levels of home
appliances through the phases of data preprocessing, feature selection,
fuzzy logic-based data labeling, and finally, classification of energy
consumption using TensorFlow based deep ensemble learning
technique. The data obtained from the effective feature selection
technique is subjected to labeling via the fuzzy logic system to classify
the energy consumption of smart home appliances. Finally, this work
classifies the level of energy consumption based on the labeled
knowledge of smart home data using a tensorflow-based deep ensemble
learning model. The experimental model implements the proposed deep
ensemble learning model in the tensorflow framework, which improves
the decision-making performance of energy utilization in the smart
home system. Experimental results illustrate that the proposed deep
ensemble learning model yields superior classification performance
than the other baseline classifiers, such as Artificial Neural Network
(ANN), Convolutional Neural Network (CNN), and Long Short-Term
Memory (LSTM) on the smart home dataset.
Computer Engineering