Machine learning algorithms for electrical appliances monitoring system using open-source systems

Abstract

Two main methods to minimize the impact of electricity generation on the environment are to exploit clean fuel resources and use electricity more effectively. In this paper, we aim to change the user's electricity usage by providing feedback about the electrical energy consumed by each device. The authors introduced two devices, load monitoring device (LMD) and activity monitoring device (AMD). The function of the LMD is to provide feedback on the operating status and energy consumption of electrical appliances in a home, which will help people consume electrical energy more efficiently. The parameters of LMD are used to predict the on/off state of each electrical appliance thanks to machine learning algorithms. AMD with audio sensors can assist LMD to distinguish electrical devices with the same or varying power over time. The system was tested for three weeks and achieved a state prediction accuracy of 93.60%.

Machine Learning Algorithms for Predicting Energy Consumption in Educational Buildings

“…The performance evaluation of the model demonstrates improved accuracy and computational efficiency compared to traditional methods. Also, Duong and Nam [27] developed a machine learning system that monitors electrical appliances to improve electricity usage behavior and reduce environmental impact. The system utilizes load and activity sensors to track energy consumption and operating status.…”

Information system based on multi-value classification of fully connected neural network for construction management

“…Sarumi et al [19] developed an efficient intrusion model for preventing network intrusion attacks in real-life application scenarios using data analytics and machine intelligence. Few researchers attempted optimizing machine learning algorithms in [20]- [22] using open-source systems. Research papers [20] and [23] suggest to solving the problem to determine the professional adaptive capabilities of construction management based on a multidimensional data model.…”

“…Few researchers attempted optimizing machine learning algorithms in [20]- [22] using open-source systems. Research papers [20] and [23] suggest to solving the problem to determine the professional adaptive capabilities of construction management based on a multidimensional data model. The studies [24] and [25] are devoted to the use of fuzzy logic methods to assess the impact of adaptability factors on the effectiveness of change management at construction industry enterprises.…”