Wireless Water Consumption Sensing System for Building Energy Efficiency: A Visual-based Approach with Self-Powered Operation

• Published in: Energy and buildings 2023-12, Vol.301, p.113584, Article 113584
• Main Authors: Liang, Rui, AL-Huqail, Arwa Abdulkreem, Elhosiny Ali, H., Ponnore, Joffin Jose, Alkhalifah, Tamim, Alturise, Fahad, Assilzadeh, Hamid
• Format: Article
• Language: English
• Subjects: Advanced Visual Sensing Model (AVSM); Building Energy Efficiency; Carbon Emissions Reduction; Lightweight AVSM Model; Water Energy Collection; Wireless Water Consumption Sensing System
• ISSN: 0378-7788
• DOI: 10.1016/j.enbuild.2023.113584

In recent years, the adoption of smart wireless remote water meters has gained popularity due to their potential for automating meter reading and reducing manual labor. The current implementation of these measurement instruments often requires expensive external power sources and separate data aggregators, posing challenges for circuit safety and the goal of reducing carbon emissions in aging communities. In response, a novel wireless system has been developed to accurately measure water consumption in buildings. This system incorporates advanced machine learning techniques and utilizes the potential of water-based energy in a seamless manner. This novel methodology utilizes optical sensors to acquire visual data of water gauges and exploits machine learning techniques, specifically the AVSM model also called Advanced Visual Sensing Model (AVSM). This methodology employs a rigorous approach to achieve accurate metric determinations, thereby eliminating the necessity for additional hardware or excessive power configurations. Moreover, an integrated micro water generator has been seamlessly implemented, demonstrating the capability to effectively transfer energy to a lithium battery for the purpose of storage. This implementation guarantees consistent functionality and a significant decrease in reliance on external power supplies. The data augmentation procedure involves the stochastic manipulation of image luminance, resulting in encouraging results when used in conjunction with the AVSM model. Significantly, the dimensions of the model have been significantly decreased, while maintaining a commendable mean average precision (mAP). By leveraging thismethod and visual-based features, the system can contribute to overall building energy efficiency and reduce the burdens of manual meter reading while supporting CO2 emissions reduction as a feasible and cost-effective solution for building management, promoting sustainability, and advancing energy-efficient practices in old communities. This proposed wireless water consumption sensing system offers a feasible and cost-effective solution for building energy efficiency, reducing manual meter reading burdens, and contributing to overall carbon emissions reduction targets.