Why heating systems are smarter than they feel

Your company focuses on urban intelligent heating. What are the biggest inefficiencies in traditional urban heating systems today, and how does Nuanliu Technology address them?

Prof. Liu Lanbin
Founder and CEO, Nuanliu Technology

Inefficiencies in traditional heating systems stem from various factors, but the primary operational pain points are threefold: equipment operating at sub-optimal efficiency, heat supply failing to adapt to weather changes, and uneven water distribution across the network. To address these, Nuanliu Technology employs a suite of digital solutions for precision management:

Equipment efficiency: By monitoring core nodes in real time and using algorithms for predictive maintenance, we ensure boilers and pumps consistently operate within their optimal efficiency ranges.

Temporal temperature control: Our proprietary AI model acts as a "brain," automatically adjusting supply temperatures in real time based on weather fluctuations. This eliminates the overheating issues caused by delayed or imprecise manual adjustments.

Spatial balancing: Using digital twin technology and hydraulic simulation algorithms, we tackle the root cause of uneven water distribution, maintaining dynamic balance across the entire heating network.

Modern buildings contain a great deal of sophisticated heating and energy technology, yet most of it is invisible to the people who live or work inside them. What might surprise people about how heating systems actually operate?

Many people assume that a heating system simply pumps heat into a room, but in reality, it’s a complex process involving constant dynamic adjustments. The system must continuously adapt to changes in outdoor temperatures, building occupancy, and pipe network conditions. Even when residents feel the indoor temperature is perfectly stable, the back-end infrastructure and control systems are working tirelessly in real time. Rooms on different floors or facing different directions require varying amounts of heat, dictated by both hydraulic distribution and building architecture. Furthermore, heating isn’t about "the hotter, the better"; it requires precise control within a comfortable range. Excessive heat supply often leads to wasted energy and a poorer experience for residents. It is precisely these "invisible adjustments" that define the true complexity of a modern heating system.

Your company has developed digital twin technology for heating networks. In simple terms, how does this help operators optimize energy use? Can you give a brief example?

A digital twin is essentially a virtual, computer-based replica that runs in sync with the physical heating system. This model mirrors the system’s real-time operating status, tracking key parameters like temperature, pressure, and flow rates. With this virtual environment, operators can safely simulate various control strategies without impacting actual users. For instance, before a cold snap hits, we can test different heating plans—such as increasing heat source capacity or adjusting valve settings at specific substations. By comparing the outcomes, we can pinpoint the optimal, most energy-efficient, and stable operational mode before applying it to the physical pipe network. This approach streamlines decision-making and drastically reduces operational risks, as well as the costs associated with trial and error.

Traditional heating systems often rely on fixed schedules or operator experience. How do AI and data help systems anticipate demand and adjust heating in real time? Can you give a simple real-world example?

Our AI models are trained on massive volumes of high-quality historical data, offering far greater adaptability and generalization than fixed schedules or manual guesswork. They synthesize multiple variables—such as shifting weather patterns, historical performance, and current system states—to make highly informed, predictive decisions. The ultimate proof of their effectiveness is their ability to consistently keep indoor temperatures within an optimal comfort zone. For a real-world example: we use AI to regulate secondary water supply temperatures. The model processes multidimensional inputs, including historical meteorological data, upcoming weather forecasts, and live operational metrics, to determine the exact optimal supply temperature for the next time step. This allows the system to preemptively respond to environmental shifts, ensuring smoother and highly energy-efficient operations.

Nuanliu Technology participated in the heating system construction for the Beijing 2022 Winter Olympics. What lessons did that project offer about managing energy systems at a large scale?

Supporting the heating infrastructure for the Beijing 2022 Winter Olympics profoundly deepened our expertise in managing large-scale energy systems. In such an extremely cold and high-stakes environment, the system had to deliver not just peak efficiency, but flawless reliability. Even the slightest fluctuation could impact event operations and the athlete experience, making stability requirements incredibly stringent. Throughout the project, we deployed AI to monitor the network in real time, identifying potential risks preemptively. This allowed us to resolve anomalies before they could affect the end-users. We also established a rapid-response protocol for unexpected scenarios. These experiences definitively proved the value of data-driven, intelligent management in massive systems—lessons we have since integrated into our everyday urban heating operations.

In large heating networks, problems such as leaks or uneven heating can be difficult to detect. How do modern systems identify and respond to these issues more quickly? What does this look like in practice?

In expansive heating networks, leaks are notoriously difficult to spot early and often go unnoticed until a major failure occurs. To combat this, we developed specialized, rugged sensors designed to continuously monitor temperature and pressure variations across complex environments. These sensors operate reliably long-term, even under high temperatures, extreme humidity, and without external power supplies. By analyzing this data in real time, our system detects abnormal signals early and provides an initial estimation of the fault's location. This has already drastically improved our leak detection efficiency and response times. However, pinpointing an exact location within minutes remains an industry challenge. Moving forward, we are optimizing our algorithms and sensing capabilities with the goal of reducing leak pinpointing time to just two to five minutes, thereby minimizing any broader impact.

Looking ahead, how do you see urban heating systems evolving as cities work to reduce carbon emissions? In practical terms, will buildings operate differently ten years from now?

As global carbon reduction goals advance, urban heating systems will accelerate their transition toward low-carbon and eventually zero-carbon operations. Over the next decade, technologies like industrial heat pumps will increasingly replace fossil-fuel-based methods. We will also see a surge in the recovery and efficient utilization of waste heat from industrial plants, data centers, and even nuclear facilities. By establishing waste heat sharing networks and seasonal thermal storage, overall energy efficiency will soar. Furthermore, heating networks will become deeply integrated with clean power grids to optimize city-wide energy operations. However, this convergence means system complexity will grow exponentially—far beyond human management capabilities. That is why AI is the only viable path forward. Within ten years, driven by deep AI integration, heating systems will become as autonomous, intelligent, and adaptable as self-driving cars, paving the way for true urban sustainability.