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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Regulatory Analysis of Machine Learning in Smart Meter Gateway: Legal Metrology Perspective

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DOI: http://dx.doi.org/10.15439/2026F4395

Citation: Mahbuba Moni, , ,

Full text

Abstract. The integration of machine learning-based algorithms into regulated measuring instruments raises questions for approval frameworks that govern software-controlled functions and assume stable, fully specified states. In Europe, legally regulated smart metering is shaped by a layered framework combining international legal metrology concepts, EU-level harmonization, and Member-State-specific implementation and certification rules. This paper develops a 13-topic cross-standard assessment for integrating machine-learning-based algorithms into such instruments and applies it to the Smart Meter Gateway, a particularly stringent national case within the European smart-metering landscape. The results show that fixed-state deployment is largely compatible with the current framework, whereas evolving-state deployment produces ten conflicts. The analysis, therefore, clarifies the conditions under which fixed-state deployment can be accommodated within the current architecture and the regulatory extensions required for evolving-state deployment. The resulting structure provides a basis for future comparative analysis across other national smart-metering regimes and comparable regulated measurement domains.

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