Using CNN-LSTM and KNN to Analyze Energy Consumption Patterns of Smarthome Devices

In recent years, smart home devices have been adopted at an accelerating rate due to their automation, security, and convenience for users. Although these devices are known to be energy efficient, it is important to know their energy consumption patterns, an important factor for planning that is currently faced with challenges. This study looks at how well CNN-LSTM and KNN work at analyzing how much energy smart home devices use. To do this, we use an exploratory research design and the cross-sectional time horizon method to collect data about how much energy smart home devices use. The CNN-LSTM and KNN machine learning algorithms were used to conduct an analysis on data pertaining to the energy consumption of smart home devices. The performance of the machine learning algorithms was measured using carefully selected evaluation metrics. Based on the results we obtained, CNN-LSTM and KNN provided some promising capabilities in predicting smart home devices' energy consumption. Apart from the energy consumption analysis, our research provides some insights on the factors that influence the energy consumption of smart homes and recommendations on steps that can be applied to promote optimization of energy management in homes with smart home devices.