Integration of IoT Energy Management System with Appliance and Activity Recognition

Lai, Chin‐Feng; Lai, Ying-Xun; Yang, Laurence T.; Chao, Han‐Chieh

doi:10.1109/greencom.2012.20

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…The knowledge and control of these activities help solve some home automation problems [18], like health and monitoring elderly [19] [20] and energy saving [7]. Hereafter some works that involve energy saving solutions that uses recognition of activities with sensor networks data analysis are listed.…”

Section: B Activity Recognitionmentioning

confidence: 99%

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User activity recognition for energy saving in smart home environment

Lima

Souto

Rocha

et al. 2015

2015 IEEE Symposium on Computers and Communication (ISCC)

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In recent years, the consumption of electricity has increased considerably in the industrial, commercial and residential sectors. This has prompted a branch of research which attempts to overcome this problem by applying different information and communication technologies, turning houses and buildings into smart environments. In this paper, we propose and design an energy saving technique based on the relationship between the user's activities and electrical appliances in smart home environments. The proposed method utilizes machine learning techniques to automatically recognize the user's activities, and then a ranking algorithm is applied to relate activities and existing home appliances. Finally, the system gives recommendations to the user whenever it detects a waste of energy. Tests on a real database show that the proposed method can to save up to 35% of electricity in a smart home

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Section: B Activity Recognitionmentioning

confidence: 99%

“…C. F. Lai et al [7] claims that the energy consumption of electrical appliances are directly related to human activities and that a significant amount of energy could be saved if the appliances would fit according to the real needs of users.…”

Section: Introductionmentioning

confidence: 99%

User activity recognition for energy saving in smart home environment

Lima

Souto

Rocha

et al. 2015

2015 IEEE Symposium on Computers and Communication (ISCC)

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“…User activities have significant impacts on the amount of energy consumed in building/classroom. We had seen from Lai et al's work how energy is related with appliances and human activities and the amount of energy we can save if we connect appliances according to user needs [12].…”

Section: Introductionmentioning

confidence: 99%

A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

et al. 2020

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With the demand for clean energy increasing, novel research is presented in this paper on providing sustainable, clean energy for a university campus. The Internet of Things (IoT) is now a leading factor in saving energy. With added deep learning for action recognition, IoT sensors implemented in real-time appliances monitor and control the extra usage of energy in buildings. This gives an extra edge on digitizing energy usage and, ultimately, reducing the power load in the electric grid. Here, we present a novel proposal through context-aware architecture for energy saving in classrooms, combining Internet of Things (IoT) sensors and video action recognition. Using this method, we can save a significant amount of energy usage in buildings.

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“…Human activity is classified using Hidden Markov models (HMM). In [12] proposed a system that can automatically achieve energy management by controlling electric appliances.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach

et al. 2018

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Optimal power usage and consumption require continuous monitoring, forecasting electric energy consumption and renewable generation. To facilitate integration of renewable energies and optimize their resources, new communication and data processing technologies are used in new projects. This article shows the works and results obtained in the eoTICC project. The objective is to design and develop an intelligent energy manager using the Archimedes wind turbine and a solar generation system, both integrated in industrial and residential power facilities. Solutions based on Artificial Intelligence paradigms and Internet of Things protocols allow automatic decision making to optimize energy management. In a facility, the energy demand and weather forecasts can be known by an intelligent energy manager. With these conditions, the energy manager can develop rules based on decision trees to automate control actions aimed at optimizing the use of energy. This article shows the architecture of IoT infrastructure and the first rules designed in the project. The result obtained provides improvements in the use of renewable energy in current facilities that do not use this type of intelligent management. The improvements allow to use the energy at the time of generation, avoiding unnecessary storage.

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Integration of IoT Energy Management System with Appliance and Activity Recognition

Cited by 19 publications

References 16 publications

User activity recognition for energy saving in smart home environment

User activity recognition for energy saving in smart home environment

A Context-Aware IoT and Deep-Learning-Based Smart Classroom for Controlling Demand and Supply of Power Load

Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach

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