This research addresses the intersection of low-power microcontroller technology and binary classification of events in the context of carbon-emission reduction. The study introduces an innovative approach leveraging microcontrollers for real-time event detection in a homogeneous hardware/firmware manner and faced with limited resources. This showcases their efficiency in processing sensor data and reducing power consumption without the need for extensive training sets. Two case studies focusing on landfill CO (Formula presented.) emissions and home energy usage demonstrate the feasibility and effectiveness of this approach. The findings highlight significant power savings achieved by minimizing data transmission during non-event periods (94.8–99.8%), in addition to presenting a sustainable alternative to traditional resource-intensive AI/ML platforms that comparatively draw and produce 20,000 times the amount of power and carbon emissions, respectively.
India Business News: Genus Power Infrastructures has secured a Rs 2,259.94 crore order for a smart meter project, bringing its total order book to over Rs 19,000 crore. Th
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A successful smart meter rollout starts by developing an understanding of the structure of each nation’s data legislation and energy market. To use the data effectively, policies and systems must be adapted, and the public must be provided with comprehensive information. Factors such as political willingness, market readiness, metering infrastructure, and the level of
An overview of legal and practical considerations surrounding renewable energy project development in Romania, including project finance transaction structures and distributed and residential renewable energy.