This Catalyst is helping energy companies reduce their carbon footprints. The project shows the latest privacy-first architecture that enables CSPs and utility companies to achieve a sustainable greener grid.
CSPs use privacy computing to support green energy grid
Introducing new services and technologies with sustainability requirements in mind is a crucial element of digital transformation. CSPs are thankfully well-placed to do this, with increasingly sophisticated network services and advanced big data capabilities.
A key reason for this is the vastness of user data on which they can base business calculations – the challenge they face is how to use this data for cross-industry applications while maintaining user privacy. This has been the aim of the Trusted Privacy Computing Empowers Sustainable Green Grid Catalyst, which is focused on using CSP big data and privacy computing capabilities to improve power load forecasting methods in power transmission and distribution and to help combat illicit energy consumption.
Privacy computing applications in the energy sector
With global and national privacy compliance requirements often stipulating that CSP user data and energy provider data cannot be directly interoperable, the Catalyst privacy computing platform was designed to aggregate and calculate user power consumption to determine accurate power load forecasting and environmental protection monitoring models. The project sought to use this to service three key applications in the energy sector in three key applications. Firstly, to provide more accurate forecasts for production, and to calculate regional load forecasting, helping to providing more effective and efficient production. This enhanced load forecasting can also help energy sales teams, by giving them access to more accurate residential user electricity forecasting, a more reasonable sales strategy can be formulated. Lastly, this Catalyst was applied to environmental protection departments to improve monitoring of smart meters and detection of illegal energy consumption, such as through illicit mining.
How the privacy computing platform works
This platform is made possible by, and based on, China Mobile's privacy computing platform. CSPs have used this to develop a versatile privacy computing platform based on the ‘1+X’ technical framework, which calls the core functional algorithm components through an API interface, and solve the intercommunication problem of multi-party privacy computing algorithms under compliance requirements – greatly improving the openness, flexibility, and compatibility of the system, to meet the needs of multiple industry applications.
Using privacy computing to create a green grid
The platform has been successful in realizing data modelling and analysis from data both CSPs and China’s national power grid, helping to achieve a load forecast error rate reduction from 3.23% to 0.87%. Moreover, it has successfully detected thousands of illegal mines, and has an identification success rate over 90%. The platform has also enabled Catalyst participants in redeeming their smart meter business guarantee which aims to mitigate the "running, emitting, dripping and seepage" of smart meters, which has been reduced by 5%. Power consumption has been reduced by 3%, overall helping energy companies fine-tune meter management and help meet energy and emission targets.
Future applications of the green grid
The success of this Catalyst, and the privacy platform it is anchored around, have important consequences for other applications as well. By demonstrating how anonymous user data can be shared and combined with CSP big data capabilities, a solid foundation has been laid for industry-wide standards for privacy computing and a path has been created for other sectors to engage in privacy computing projects.