Smart Ready Building Control

Today, there is a good understanding of how to realize low-cost energy savings in connection with energy optimization of buildings.

This project will go a step further and automate the opti-mizations that today are usually too resource-intensive to realize.

In the project, a self-learning predictive control is developed and demonstrated that can de-liver the best possible indoor climate at minimal energy consumption. This is achieved with a digital twin based on data from BMS-systems and ventilation components, supplemented with data from weather forecasts, user behavior and energy market.

The digital twin will be able to continuously optimize the indoor climate according to use, energy market and weather. In ad-dition, KPIs (Key Performance Indicators) will be set up which can help identify when energy consumption deviates from normal, and thus perform predictive maintenance.

The project builds on a number of previously developed products / technologies.The digital twin is built on Neogrid's PreHEAT platform. PreHEAT is an intelligent superstructure that in-teracts with the existing CTS solution and provides optimized control and monitoring of the installation. In this project, the digital twin will control heating installations, ventilation, cooling and solar shading.

The self-learning predictive control is demonstrated at the IDA engineering association in their building on Kalvebod pier. Here there are both office space and a meeting center where there are challenges with the indoor climate.

The project is primarily aimed at larger buildings with central ventilation systems, as well as BMS control. Both new and existing buildings.