Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/56542
Title: Explainable AI-powered Edge Computing Solution for Smart Building Energy Management in Green IoT
Authors: Kök, I.
Ergun, Y.
Uğur, N.
Keywords: Building Energy Management
Edge Computing
Explainable Artificial Intelligence (XAI)
Green Internet of Things
Architecture
Artificial intelligence
Computer architecture
Costs
Edge computing
Energy efficiency
Energy management
Energy utilization
Environmental impact
Global warming
Green computing
Building energy managements
Buildings sector
Computing solutions
Edge computing
Energy
Energy-consumption
Explainable artificial intelligence (XAI)
Green internet of thing
Green internets
Low-costs
Internet of things
Publisher: Institution of Engineering and Technology
Abstract: Today, climate change and global warming are among the most serious problems of humanity. In combating these problems, urgent and serious actions are needed especially in energy preferences, utilization, and management. Especially in the building sector, energy consumption has increased rapidly and today it has reached 40% of total global energy consumption. Therefore, the use of low-cost, eco-friendly, and sustainable green technologies is critical to mitigate the negative environmental impacts of carbon emissions and the depletion of the world's energy resources. In this context, emerging technologies such as Artificial Intelligence (AI), the Internet of Things (IoT), and edge computing are both necessary and promising in terms of ensuring energy efficiency in buildings, grid security, and supply-demand balance. In this paper, we propose a low-cost end-to-end IoT system architecture for energy monitoring and management in smart buildings. In this architecture, we develop explainable ML models that predict building energy consumption based on edge computing. We also develop a mobile application with video call and instant messaging features for monitoring and managing energy consumption for expert users. Experimental and test results show that the proposed system can be used in building energy management in a fast, effective and interpretable way with the support of AI and IoT. With the developed prototype architecture, we present a future projection that the energy management of buildings in Green IoT can be low-cost, transparent, and understandable. © 2023 IET Conference Proceedings. All rights reserved.
Description: 2023 Low-Cost Digital Solutions for Industrial Automation, LoDiSA 2023 -- 25 September 2023 through 26 September 2023 -- 194731
URI: https://doi.org/10.1049/icp.2023.1747
https://hdl.handle.net/11499/56542
ISSN: 2732-4494
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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