Implement Heizungsmonitoring: How to Roll Out Across Portfolios
The cleanest way to bring heating monitoring into an existing portfolio is to treat it as one connected operational project: a clear target picture, prioritised properties, a verified data base, named owners, a fixed set of KPIs and a binding response process. This chain is what turns raw readings into prioritised actions, clean responses and progress you can actually prove.
The pressure is coming from several directions at once. Since 1 January 2022, buildings with remotely readable meters must provide monthly consumption information, and from 2027 this applies across the board. On top of that come the GEG inspection obligations for heat pumps and older heating systems. And the national CO₂ price sits at 55 to 65 euros per tonne in 2026, with the landlord's share tied directly to the specific emissions per square metre. Transparency in the boiler room has long since become a commercial question.
Before you move into the rollout, it pays to look at the levers that decide whether any of this delivers real value:
- Prioritise before you scale: start with clusters grouped by savings potential, system technology and deadline, not with a big-bang across the whole portfolio.
- Settle data and KPIs first: a lean core of flow, return, heat quantity and heating curve beats an unverified flood of sensors.
- A response needs an owner: every anomaly belongs to a named person, otherwise the data has no effect.
- Keep monitoring and control apart: KUGU VIS creates transparency, and automated optimisation through KUGU EOS is the next step.
How does heating monitoring start in a residential portfolio?
The start is a project decision, not a tool selection. Before the first sensor goes in, you clarify your goals, the building selection, the available data and interfaces, the responsibilities, the KPI set and the response process as connected building blocks. This is exactly the sequence the current KEDi guidance recommends. Before any rollout, it poses seven concrete planning questions: which buildings, which goals, what monitoring scope, which data, which existing interfaces, what qualifications and what effort.
The goals set the direction. Do you want to produce regulatory proof under GEG, cut operating costs or prepare modernisations? For older water-based systems in buildings with six or more residential units, inspection and optimisation is on the agenda anyway, specifically for systems installed before October 2009 and due by 30 September 2027 at the latest. New heat pumps in buildings of this size are checked after the first full heating season. These deadlines effectively set the pace for the order in which you tackle your properties.
The most common mistake rarely lies in the technology, it lies in the organisation. A monitoring system only delivers something once the operator uses it to spot inefficiencies, set target values and make sure they are met. A dashboard flagging an excessive flow temperature that nobody feels responsible for simply produces data with no effect. The question of who reacts to what, and within which deadline, belongs in the start phase, not in later operations. What such an entry into digitalised heating portfolios feels like in practice becomes clear on the path from the first connected system to steady, day-to-day operation.
Which properties enter monitoring first?
The first properties to go in are the ones with the best ratio of impact to feasibility, not the ones that are technically most convenient. Impact is measured by energy, cost and CO₂ risk, feasibility by data access and system technology. The national CO₂ price of 55 to 65 euros per tonne in 2026 hits the landlord's share directly in emission-heavy buildings. That is precisely why inefficient stock makes the most rewarding starting candidate.
For prioritisation, these axes have proven their worth:
- Savings and CO₂ risk: buildings with high specific emissions per square metre hit the landlord's share hardest.
- System technology: gas, district heating and heat pumps each call for different monitoring strategies and metrics.
- Regulatory deadline: older systems with a § 60b date by 2027 belong early in the wave.
- Data access: existing remotely readable metering and open interfaces shorten the build considerably.
- Usage homogeneity: similar building types can be rolled out in a standardised way as a cluster.
What emerges from these axes is a staggered sequence, not a rigid universal order. A manageable pilot cluster of a few similar buildings delivers the experience the faster rollout waves build on for the comparable rest. Special cases with mixed technology or unclear data access are deliberately pushed to the back. A big-bang across the whole portfolio, without any prior check of data and processes, produces mainly one thing: mountains of data that nobody feels responsible for.
Which data and KPIs does the boiler room need?
The minimum data set is defined by operational value, not by the number of sensors on offer. A simple monitoring setup that is workable according to KEDi consists of temperature sensors, a heat meter, a controller and, where needed, a radio module, supplemented by electricity or fuel meters. In residential buildings, retrofitting usually works with minimal intervention in the existing system technology.
These measuring points become the metrics you anchor your operational decisions to:
- Flow and return temperature: reveals excessive system temperatures that drive efficiency and costs directly.
- Heating curve and heating limit temperature: show whether the control matches actual demand.
- Night and summer setback: confirms whether setback periods actually work or sit unused.
- Heat quantity and energy consumption: form the basis for comparison, reporting and CO₂ metrics.
- Seasonal performance factor for heat pumps: the central efficiency indicator for the § 60a proof.
There are no hard standard values for "sufficient data quality", so you set testable project criteria yourself: plausible readings, complete series, correct time reference, unambiguous system assignment and secured data access. This is exactly where KUGU VIS comes in, the Visuelles-Informationssystem (VIS) for real-time transparency on system states, consumption, anomalies and concrete recommendations. The VIS Anlagendiagnose checks a system after installation and commissioning against more than 20 criteria. This layer assesses and recommends, but it does not yet intervene in the control itself.
Who responds to monitoring anomalies?
Every anomaly needs a named owner, a technical assessment, an escalation path, a defined handover to service providers and documentation. Without this chain, the value of monitoring drains away exactly where it is supposed to arise. Asset management prioritises by commercial risk, the technical function assesses the system parameters, property management coordinates tenant communication and access, and the specialist service providers carry out the actual repair work on site.
When it comes to the data base, you have to keep a clean separation. According to the guidance from the German data protection conference, § 6b HeizkostenV covers the processing of data from radio-based meters for cost allocation, billing and the monthly consumption information. For anything beyond that, such as deeper optimisation analysis, you need an additional legal basis or voluntary consent. As a rule, the building owner stays responsible, while reading and metering service providers act as processors. So you settle the billing purpose, the monitoring purpose and the optimisation purpose before scaling, not afterwards.
Worth knowing: The current KEDi interpretation of § 60b opens a way to drop the manual heating inspection when continuous monitoring runs with standardised building automation, an ongoing comparison of target and measured values, and a named energy management contact. Response process and ownership are then not only good operation, they also connect to the regulatory side.
In everyday operation, the practical effect is what counts. Anyone keeping the boiler room in digital view spots anomalies earlier and saves unnecessary call-outs. Ongoing assessment and clear recommendations noticeably ease the load on maintenance and operations.
Which rollout models scale all the way to control?
The models that scale best are cluster-based waves that start as a service model and later move into automated control. KEDi distinguishes here between buying the technology including software and monitoring as a service. The service model lowers the initial investment and training effort, but it brings contractual commitment and questions of data control. Which model holds up depends in the end on the portfolio.
| Model | How it works | Strength | Limit |
|---|---|---|---|
| Technology purchase with in-house operation | Hardware and software owned outright, run by your own staff | Full data control | Higher initial investment and training effort |
| Monitoring as a service | Sensors and analysis via a provider, ongoing service fee | Low start-up cost, fast scaling | Contractual commitment, data control to be clarified |
| Cluster-based waves | Rollout in groups of similar buildings after a pilot | Standardisable and able to learn | Requires clean portfolio segmentation |
| Extension to control | Optimisation built on top of existing monitoring | Active adjustment instead of pure display | Needs verified data quality and controller compatibility |
Practice supports the step-by-step path. In documented KEDi cases, sensors were retrofitted without touching existing components, for example in a residential building from 1959, while a district heating station in a new build showed an expected saving of around 8 percent. The move from transparency to control is the real scaling question, and this is where the two KUGU layers separate cleanly: KUGU VIS delivers the real-time transparency, while KUGU EOS as the Energie-Optimierungssystem (EOS) takes over fully automated control of the heating system based on a digital building twin, in 15-minute intervals. KUGU cites more than 20 percent energy savings across the average of its connected systems, and individual older buildings in Leipzig reached 33 and 39 percent over a short spring window. These figures are company data and individual cases, not a general market benchmark.
From heating monitoring to operational control
The sheer volume of measurement data decides nothing on its own, as long as it is not tied to a portfolio decision and a binding operational response. Only when prioritised properties, named responsibilities and a clear KPI set work together does the boiler room become a controllable operational process. Transparency is the beginning, automated optimisation the reliable target state.
The next step is manageable. Start a limited pilot cluster with a defined target picture, a data audit against testable quality criteria, clarified roles and a fixed set of metrics. The experience from this cluster feeds the next wave, and the monitoring transparency from KUGU VIS becomes the entry into active adjustment with KUGU EOS as a possible follow-up step.
Anyone who works this way is not buying a product, they are building a development path that keeps regulation, economics and operations together for the long term.
Frequently asked questions (FAQ)
Is heating monitoring mandatory under GEG § 60b?
No. § 60b does not require permanent monitoring, but a one-off heating inspection and optimisation for older water-based systems in buildings with six or more residential units, with systems installed before October 2009 due by 30 September 2027. Continuous monitoring with standardised building automation and a named energy management contact can, however, make the manual inspection unnecessary under the current KEDi interpretation.
Which measuring points matter most in a multi-family building?
The priorities are flow and return temperature, heat quantity and energy input, along with the central control parameters of heating curve, night setback and heating limit temperature. This combination reliably flags excessive system temperatures and unused setback potential. For heat pumps, the seasonal performance factor is added as an efficiency indicator. The specific selection depends on the system technology in place.
May heating cost and consumption data be used for monitoring?
Only to a limited extent. Data from radio-based meters is covered by § 6b HeizkostenV for cost allocation, billing and the monthly consumption information. For monitoring and optimisation purposes beyond that, you need an additional legal basis or voluntary consent. So clarify the data purpose, the legal basis and the role of the appointed service provider before you scale.
When is monitoring enough, and when do you need automatic optimisation?
Monitoring is enough when you are after transparency, proof and manual parameter setting. As soon as you want to adjust continuously and on demand across many buildings, automatic optimisation makes sense. The strategies range from remote surveillance through automatic parameter setting to automatic control. KUGU VIS covers the transparency layer, while fully automated control is handled by KUGU EOS.
How do housing companies document progress after the rollout?
Operationally, through the measures carried out, the deviations identified, the consumption trend and the CO₂ metrics derived from it. The GEG inspections require written results and evidence of optimisation measures anyway. Monitoring data provides the ongoing basis for this, eases operations through earlier anomaly detection, and at the same time supports the reporting obligations around consumption and CO₂ costs.
