A semantic data framework to support data-driven demand forecasting

Ali Hainoun, James Allan, Francesca Mangili, Marco Derboni, Luis Gisler, Andrea Rizzoli, Luca Ventriglia, Matthias Sulzer

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung


This paper presents a prototype semantic data framework for integrating heterogeneous data inputs for data-driven demand forecasting. This framework will be a core feature of a data exchange platform to improve the access and exchange of data between stakeholders involved in the operation and planning of energy systems. Surveys revealed that these stakeholders require reliable data on expected energy production and consumption for strategic and real-time decision-making. A core feature of the framework is the application of semantic technologies for comprehending spatial and temporal data requirements of energy demand forecasting. This paper demonstrates an approach to meeting these semantic requirements through established data standards and models. The conceptual design process followed the following stages: surveying stakeholders, researching digital technologies’ capability, and systematically evaluating the available data. In this paper, we present a prototype based on simulated data. Inputs and results from the simulation model, extracted from open datasets, were structured and stored in a knowledge graph comprised of virtual entities of buildings and geospatial regions. Multiple virtual entities can be linked to a single real-world entity to provide a flexible and adaptable approach to data-driven demand forecasting
TitelCISBAT 2023
UntertitelJournal of Physics: Conference Series
PublikationsstatusVeröffentlicht - 1 Nov. 2023


NameJournal of Physics: Conference Series
Herausgeber (Verlag)IOP Publishing Ltd.
ISSN (Print)1742-6588

Research Field

  • Ehemaliges Research Field - Smart and Carbon Neutral Urban Developments


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