A new smart controller helps heat pump systems run more efficiently and reliably. It uses two advanced tools in a small, easy-to-use device. It consists of a model predictive control (MPC) algorithm that looks at weather forecasts, building use, and energy prices to run heating and cooling at the best times to reduce energy costs. A fault detection and diagnosis (FDD) algorithm also watches how well the system is working, spotting problems early and helping fix them before they get worse. Both tools are built into a simple platform—like a Raspberry Pi—making the system flexible and easy to test.

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Smart building energy systems help save energy and reduce costs. They do this by storing extra solar power in batteries and using that stored energy when electricity from the grid is most expensive. These systems also run heat pumps and store heat when there’s plenty of solar power. They charge or discharge the battery at the best times, based on changing electricity prices throughout the day.

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Use of digital technologies, such as satellite images, street-level photos and 3D scans, to automatically analyse buildings and identify those with similar characteristics. By looking at features such as roof shapes, façade designs and construction materials, the system creates a kind of “building DNA” that groups buildings into common types. This makes it easier to plan large-scale renovations, identify suitable retrofit solutions and accelerate the delivery of energy-efficient upgrades across entire neighbourhoods.

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Danfoss Leanheat Building is an AI-based software and control system used to manage heating at building level. It replaces the traditional approach based on a fixed relationship between heating supply temperature and outdoor temperature, known as weather compensation. Instead, the solution combines an adaptive dynamic building model with weather forecasts and measured indoor temperatures from apartments. This allows the system to estimate the optimal heating supply temperature at any given time while maintaining indoor comfort, taking into account the building’s passive thermal storage capacity as well as solar radiation, wind, and outdoor temperature predictions.

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BIM and Digital Twins are used to optimise building performance from environmental and economic perspectives. The approach enables a comprehensive evaluation across the building lifecycle, including environmental impact, costs, and energy use, as well as aspects such as community inclusivity, use of indoor and outdoor spaces, water management, noise and pollution, and architectural quality.

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