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AI for energy demand prediction to optimise District Heating Network (DHN) operation

Demonstrating how AI analytics can contribute to achieving an economic benefit optimisation as well as the reduction of energy consumption in a DHN.


Business Category

What is the challenge that is being addressed?

This pilot involves a DHN providing heating and domestic hot water to 398 households with 20 distribution substations, in Valladolid, as well as a DHN providing heating and domestic hot water to 1,500 households with 20 distribution substations, in Laguna de Duero. Such facilities need information coming from individual apartments (energy demand) from a baseline (static data) as well as in operation (real data). The operation of the generation and distribution, how to match the demand to the generation, the prediction taking into account weather conditions or other kind of social event can contribute to economic benefit. Additionally, this pilot will be in charge of applying the I-NERGY framework to design, in Laguna de Duero, a new DH brand to connect the remaining 11 building blocks to the station of production.


What is the AI solution the project plans to implement?

Information and data from the current DHN (Veolia HubGrade) will calibrate new models and simulations of alternative scenarios, which will be created using new components and equipment extracted from e-catalogues. Optimisation processes helps decide the best design for the new facility.


Who will help implement this solution?

This pilot is implemented within the framework of the “I-NΕRGΥ: Artificial Intelligence for Next Generation Energy” Project. The I-NERGY Project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No. 101016508.


The responsible partner for this Use Case is VEOLIA (


Learn more about I-NERGY pilots here: