Abstract
Maintaining a fair use of energy consumption in smart homes with many household appliances requires sophisticated algorithms working together in real time. Similarly, choosing a proper schedule for appliances operation can be used to reduce inappropriate energy consumption. However, scheduling appliances always depend on the behavior of a smart home user. Thus, modeling human interaction with appliances is needed to design an efficient scheduling algorithm with real-time support. In this regard, we propose a scheduling algorithm based on human appliances interaction in smart homes using reinforcement learning (RL). The proposed scheduling algorithm divides the entire day into various states. In each state, the agents attached to household appliances perform various actions to obtain the highest reward. To adjust the discomfort which arises due to performing inappropriate action, the household appliances are categorized into three groups i.e., (1) adoptable, (2) un-adoptable, (3) manageable. Finally, the proposed system is tested for the energy consumption and discomfort level of the home user against our previous scheduling algorithm based on least slack time phenomenon. The proposed scheme outperforms the Least Slack Time (LST) based scheduling in context of energy consumption and discomfort level of the home user.
Highlights
The requisition for electrical energy, smart grid paradigm, and renewable energy extend to new space for the home energy management system (HEMS) in such a way that can mitigate the consumption of smart home energy
Human-appliances interaction data consist of (a) Time horizon data, weather data, electricity price data, zone wise ambient temperature, and user up-to-date location-based data and actions performed by user contained data, (b) control signal data related to appliances, and (c) indoor and outdoor temperature data, and user comfort preference data about services provided by household appliances [18,19]
The proposed system is compared with the scheduling algorithm based on the Least Slack Time (LST)
Summary
The requisition for electrical energy, smart grid paradigm, and renewable energy extend to new space for the home energy management system (HEMS) in such a way that can mitigate the consumption of smart home energy. Artificial intelligence (AI)-based HEMS has received much attention in the past decade, with most of the systems implementing a household appliance scheduler and controller for consumers in smart homes to reduce the energy cost. These systems are based on the adaptive neural fuzzy inference system (ANDIS), fuzzy logic control (FLC), and artificial neural network (ANN) [1]. Human-appliances interaction data consist of (a) Time horizon data, weather data, electricity price data, zone wise ambient temperature, and user up-to-date location-based data and actions performed by user contained data, (b) control signal data related to appliances, and (c) indoor and outdoor temperature data, and user comfort preference data about services provided by household appliances [18,19]. The mathematical equations and objective function of the load management, together with the numerous constrained appliances operation for said categories of appliances, are explained
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