Period: 01. 01. 2018 – 31. 12. 2020

Funding SOURCE:

TOTAL BUDGET:

GEMMA COORDINATOR: prof. dr. Domen Mongus

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IoT based machine learning model for predicting electricity consumption

Abstract:

This project focused on the analysis and machine learning architectures for predicting electricity consumption from real-time internet of things (IoT) data. For this purpose, extensive study of the existing technology for predicting electrical energy networks’ workloads was conducted and key strongpoint and good practices were identified. Based on a technological solution for the use of machine learning methods in predicting predicted electricity consumption and / or load on the electricity grid was design at a conceptual level and tested in as a proof-of-concept.