This jupyter notebook is how a prediction system for cold energy demand was implemented in context of I-NERGY project. The overall vision of I-NERGY is to promote AI in the energy sector by delivering:

• An open modular framework for supporting AI-on-Demand in the energy sector by capitalising on state-of-the-art AI, IoT, semantics, federated learning, analytics tools.
• Financing support through Open Calls to third party SMEs for new energy use cases and technology building blocks validation, as well as for new AI-based energy services development, fully aligning to AIoD requirements.

This is a prediction service for predict the demand (cold energy) in a Spanish Hospital in hourly basis. The data was provided by VEOLIA, from the hospital complex in Córdoba (Spain). The hospital complex have a district heating network. The layout of this district heating network is a ring system composed by two independent rings for heating and cooling. This ring just provides energy for heating and Domestic Hot Water (DHW).

Apart from being a district heating network, this system is complex due to the different production sources used for heating and cooling. In this facility heat, cold and steam are produced by using different sources.

For more information on how to use the service, please see Documents section.

The project leading to this service has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101016508