Abstract
Appliance load monitoring in smart homes has been gaining importance due to its significant advantages in achieving an energy efficient smart grid. The methods to manage such processes can be classified into hardware-based methods, including intrusive load monitoring (ILM) and software-based methods referring to non-intrusive load monitoring (NILM). ILM is based on low-end meter devices attached to home appliances in opposition to NILM techniques, where only a single point of sensing is needed. Although ILM solutions can be relatively expensive, they provide higher efficiency and reliability than NILMs. Moreover, future solutions are expected to be hybrid, combining the benefits of NILM along with individual power measurement by smart plugs and smart appliances. This paper proposes a novel ILM approach for load monitoring that aims to develop an activity recognition system based on IoT architecture. The proposed IoT architecture consists of the appliances layer, perception layer, communication network layer, middleware layer, and application layer. The main function of the appliance recognition module is to label sensor data and allow the implementation of different home applications. Three different classifier models are tested using real data from the UK-DALE dataset: feed-forward neural network (FFNN), long short-term memory (LSTM), and support vector machine (SVM). The developed activities of daily living (ADL) algorithm maps each ADL to a set of criteria depending on the appliance used. The features are extracted according to the consumption in Watt-hours and the times where they are switched on. In the FFNN and the LSTM networks, the accuracy is above 0.9 while around 0.8 for the SVM network. Other experiments are performed to evaluate the classifier model using a new test set. A sensitivity analysis is also carried out to study the impact of the group size on the classifier accuracy.
Highlights
Nowadays, the applications of smart home concepts and home energy management systems (HEMS) have been gaining increasing attention in the research community due to many advantages they offer
To the best of our knowledge, our work proposes a novel solution based on intrusive load monitoring (ILM) for activities of daily living (ADL) classification, that allows identifying the daily activities of house occupants in a simple manner, which can be useful for a series of applications
This work presented a framework for an internet of things (IoT) approach able to support distributed sensing and ADL classification system based on ILM in smart homes
Summary
The applications of smart home concepts and home energy management systems (HEMS) have been gaining increasing attention in the research community due to many advantages they offer. P. Franco et al.: IoT Based Approach for Load Monitoring and Activity Recognition in Smart Homes useful information could be inferred from appliance data such as consumers’ behavior patterns, including occupation, sleep patterns, and other activities. Software-based methods include measurements from only a single point of sensing (smart meter device) These methods, commonly known as non-intrusive load monitoring (NILM), offer an attractive solution, essentially due to their low-cost implementation since they only need a single point of detection. Techniques based on hardware include methods for intrusive load monitoring (ILM), known as distributed sensing This technique can be divided into two sub-categories: one that refers to a model in which energy consumption profiles are obtained at device level using submeasurement sensors attached to appliances. A discussion of the results and the conclusions are provided in sections six and seven
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