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Working Group 3: Implementation Guidelines of AI and Emerging Technologies for Environmental Efficiency

​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​This Working Group describes methodologies for implementing emerging technologies, including guidelines on technical solutions, use cases and best practices.​

​​​​No

​​​Provisional number
​Title
Category
Scope
​Priority

​Leader

​1D.WG3-1
Guidelines on the implementation of eco-friendly criterias for AI and other emerging technologies

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Technical Report
This document proposes a set of guidelines for organizations to review  their implementation and build process to assess the technologic​al impact to environmental factors like: 
Completed

Download the report here 
​​[Word | PDF]​
Bosen Liu,
Ladder Education Group​ 

2​D.WG3-2Smart Energy Saving of 5G Base Station: Based on AI and other emerging technologies to forecast and optimize the management of 5G wireless network energy consumptionTechnical ReportThis document focuses on energy saving technology of 5G base stations. It explores ​​how 4G network energy saving technologies, such as carrier shutdown, channel shutdown, symbol shutdown, etc., can be leveraged to mitigate 5G energy consumption. It also analyses the development of enhanced technologies like deep sleep, symbol aggregation shutdown, etc., in the 5G era. It covers how AI and big data technology are introduced in response to the requirement of an intelligent and selfadaptive energy saving solution. This report also covers intelligent technical guidance for smart energy saving of 5G base stations.
​Completed

Download the report here
[Word | PDF]
​Rumeng Tan,
China Telecom
​​3

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​​D.W​​G3-3
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Data center energy saving: Application of Al technology in improving energy efficiency of telecom equipmen​t rooms and internet data center infrastructure (IDC)Technical Report​​Most of the telecom equipment rooms  rooms do not have the full ability to identify indoor temperature distribution.  Therefore, it is difficult to analyze power consumption in real-time and make appropriate and timely adjustments. As a result, it causes energy to be wasted. This document covers how AI-based power management capabilities can:
Completed

Download the report here
[Word PDF]
​Ying Shi,
China Telecom


​5D.WG3-5​Best practice catalogue on environmentally efficient artificial intelligence and blockchain application​Technical Report​​This document will contain a list of best practices on artificial intelligence and blockchain applications that have taken environmental efficiency into full consideration. The growing energy demands of AI and blockchain is directly contributing to carbon emissions. The best practices contained in this specification will support relevant stakeholders in making better environmental decisions and reduce the environmental footprint of these technologies. The best practices also act as benchmarking tools that allow operators and service providers to assess their own operation, improve process management and learn from the industry leaders.Completed

Download the report here
[Word​ | PDF]​​​​​​​
Mattia Santoro & Enrico Boldrini, Consiglio Nazionale delle Ricerche (CNR)​
​6D.WG3-6​​Guidelines on the Environmental Efficiency of 5G Usage in Smart Water ManagementTechnical Report​​This guidance document is intended to support researchers and practitioners in measuring and improving the environmental efficiency of IoT technologies, in particular 5G connectivity in water management systems. The requirements, recommended processes, best practices and other considerations regarding the measurement and verification of environmental impact/efficiency contained in this document are developed based on inputs from leading academic experts and industry leaders. These requirements provide general guidelines applicable to the use of IoT connectivity of 5G. Other stakeholders may also utilize this guidance to gain new understanding on the environmental impacts from the use of Internet of Things (IoT) and 5G to connect and enable further networked sensors and applications to manage water supplies and reduce water loss.Draft Technical report has been sent to FG-AI4A, for finalization.
Ramy Ahmed Fathy, Co-Chairman, Focus Group on AI and IoT for Digital Agriculture (FG-AI4A) 



​7D.WG3-7​​Guidelines on the Environmental Efficiency of Machine Learning Processes in Supply Chain Management
​Technical Report​This guidance document is intended to support machine learning (ML) researchers and operators to measure and improve the environmental efficiency of ML, artificial intelligence (AI) and other emerging technologies used in supply chain management. The requirements, recommended processes, best practices and other considerations regarding the measurement and verification of environmental impact/efficiency contained in this document are developed based on inputs from leading academic experts and industry leaders. These requirements provide general guidelines applicable to the use of ML, AI and other emerging technologies in supply chain management.
Other stakeholders may also use this guidance document to gain a new understanding of the environmental impacts of ML, AI and other emerging technologies used in supply chain management.​​​
Completed

Download the report here
[Word | PDF]
​Claudio Bianco,
Telecom Italia S.p.A.