Success factor intelligent energy management: When AI controls electricity, heat, and cooling

Energy management was long a secondary task: recording consumption, checking bills, occasionally swapping a system. Those days are over. Volatile electricity prices, network charges, peak loads, self-generation, heat pumps, cooling generation, storage, and charging infrastructure transform buildings and properties into a system with many degrees of freedom - and thus into an optimization problem.

The regulatory framework has caught up. § 71a of the Building Energy Act obligates operators of non-residential buildings with heating or air conditioning systems over 290 kilowatts of nominal output to equip them with building automation and digital energy monitoring technology; for existing buildings, the deadline expired at the end of 2024, in new construction, at least automation level B according to DIN V 18599-11 is required. At the company level, the Energy Efficiency Act requires energy or environmental management systems and the publication of implementation plans; the amendment draft approved by the Cabinet in June 2026, currently in the parliamentary process, significantly raises the thresholds and shifts deadlines. It thus shifts the focus more on the actual impact - and less on formal concern.

Thus, the initial situation is clear: The obligation to technology is largely fulfilled or scheduled. The question is what it achieves.

In practice, the benefits often fall short of expectations – and for recurring reasons:

• Control loops operate reactively and in silos: heating, cooling, ventilation, photovoltaic, storage, and charging infrastructure are optimized separately.

• Setpoints and time schedules come from commissioning and were never adjusted to actual use.

• Forecasts of weather, occupancy, and prices do not flow into the control.

• Malfunctions go unnoticed because they do not impair comfort – such as simultaneous heating and cooling.

• Measurement concepts are incomplete, data points inconsistently named, systems non-interoperable.

The pattern is always the same: The systems function, but they do not work together. And no one notices because the deviation in consumption goes unnoticed.

AI does not replace building automation. It builds on it – and shifts regulation from reactive to predictive. Four effects are relevant in practice.

Predictive regulation. Instead of reacting to deviations, weather, occupancy, the building's thermal inertia, and price signals are forecasted, and operation is aligned accordingly. Storage masses and buffer storage are then loaded when it is economical – not just when the need arises.

Fault detection in ongoing operations. Models learn the normal behavior of a system and report deviations long before they become noticeable in consumption. This is where many quickly realizable savings lie.

Cross-sector optimization. Electricity, heating, and cooling are considered together. Only this coupling makes heat pumps, storage, and self-generation economically manageable.

Management of flexibility. Load shifting, peak load capping, and the use of self-generated power become actively controlled variables instead of by-products.

The crucial limitation here: The bottleneck is rarely the procedure. It is the data basis. Without a reliable measurement concept, consistent data point naming, and interoperable systems, any model remains an estimate on uncertain ground.

Additionally, an aspect that is regularly considered too late in efficiency projects: Building automation is operational technology. Remote access, cloud connections, and interfaces to office IT create attack surfaces. Network separation, access concepts, and logging should, therefore, be part of planning from the start – for many operators now also with regulatory reference.

Intelligent energy management is not a technology issue but a discipline of operation. The regulatory requirements set the framework, economics determine the pace – but the benefit arises only where data, regulation, and responsibility converge. AI is an effective lever but no substitute for transparency. Organizations that first invest in their data basis and then in optimization achieve more than those who start with the algorithm.