Abstract
The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh.
Highlights
The use of information technology in the smart building environment for connectivity has increased during recent years
This paper focuses on overcoming some of these issues to reduce energy consumption and increase the comfort index
PROPOSED METHODOLOGY In this paper, we have proposed a general model to address the issue of static user parameters along with energy consumption optimization and comfort index management
Summary
The use of information technology in the smart building environment for connectivity has increased during recent years. It is believed that in the future, more devices will be connected in the smart home networks. The internet of things (IoT) has changed the living styles of people. The ordinary cities are being converted to smart cities, with facilities like automated parking, smart lights, smart cars, automated trains, and so forth [1, 2]. There are security issues regarding the confidentiality and misuse of technology [3]. Electronic devices operate using the electricity due to smart homes' compatibility with grids; internal power units need to VOLUME XX, 2017
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