Machine Learning
Energy Distribution

Residential Electricity Consumption Forecasting

Hourly D+1 demand curves
Zone-level granularity
Dynamic flow optimisation

The Challenge

Estimation based on static consumption profiles and historical averages prevented optimal distribution. Without a forecast adjusted to real demand, injection into the grid caused high energy losses — and sudden thermal changes or atypical events could not be absorbed.

What We Built

An ML pipeline combining smart meter consumption history, weather forecasts and temporal variables generates hourly demand curves for D+1, segmented by zone. Distribution flows are dynamically adjusted from generation centres to anticipated demand nodes in real time.

Operational Impact

Direct reduction in transport losses, improved grid balancing and full utilisation of energy produced — every megawatt reaching its useful destination.

Technology Used

Prophet
Power BI
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Standards

Secure, accountable delivery.

Recognised standards for information security, quality management and responsible data protection.

ENS Alto certificationENS Alto
ISO/IEC 27001 certificationISO 27001
ISO 9001 certificationISO 9001
GDPR complianceGDPR
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