Forecasting and control of LowEx system

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Products/Technical solution, Technologies and Building systems

DESCRIPTION:

This solution explores the integration of a low-exergy ground-source heat pump with Thermally Activated Building Systems (TABS) to deliver efficient heating and cooling in buildings, focusing on a pilot site in Oslo. While TABS enhance energy efficiency by leveraging the thermal inertia of concrete structures, their slow response time currently leads to poor indoor comfort due to inadequate rule-based controls. To address this, a room-level Model Predictive Control (MPC) system is proposed to improve temperature regulation using data-driven models continuously trained during building operation. Additionally, a building-level MPC will coordinate various systems to optimize energy costs, implement demand response strategies, enable fuel switching, and maximize on-site solar PV self-consumption. These smart controls use forecasts of weather, occupancy, energy prices, and PV output.

BUSINESS ROUTE

Target Users

Local authorities, Service providers

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Oslo, Norway

Nordic

Efficient Operation & Flexibility

VALUE

Buildings are dynamic systems that interact with their environment, occupants, and energy grids. While those with high thermal inertia and energy storage can be challenging to manage, smart model predictive control (MPC) offers a powerful solution. In this case, predictive control will optimize PV self-consumption, reduce energy use and costs, enhance indoor comfort and occupant well-being, lower grid peak demand, and enable demand response. Results will be documented in technical reports and scientific publications.

APPLICATION

The Oslo demo site tested both room- and building-level MPCs, evaluating their ability to improve comfort, energy flexibility, and system performance, with results to be compared with similar studies and documented in future reports. The lessons learned will be instrumental in identifying the essential features required in building management software to support MPC and demand response control. Results from the demand flexibility tests will be compared with other Norwegian case studies to assess the broader potential of the national building stock to provide grid services. Additionally, data-driven methods for evaluating energy flexibility and calibrating MPC models will support the future deployment of smart predictive controls in grid-interactive buildings across Norway.