Turning sunlight into heat

Prof. Wang Dengjia
Xi’an University of Architecture and Technology

Your research focuses on solar energy integration in buildings. On the Qinghai-Tibet Plateau, where temperatures can swing from warm days to near-freezing nights, what challenges do buildings face in heating?

The main challenge is the climate. Temperatures are very low, with big swings between day and night, and access to conventional energy sources is limited. Although solar radiation is abundant, it’s intermittent, and there’s a mismatch between the time when solar energy is available and when buildings need heat.

How has your team used near-zero energy building and solar heating technologies to overcome these challenges?

No single technology can solve these challenges, so we’ve shifted from a “solar heating” approach toward a “zero-carbon building” paradigm. On the demand side, we reduce energy use through better architectural design, high-performance building envelopes [the physical separator between a building’s heated interior and its outer walls], and passive solar design strategies that use a building’s structure and materials to heat and cool living spaces using solar energy. On the supply side, solar is the main source, using solar panels for electricity and solar collectors for heat, combined with batteries and heat storage to keep energy available when it’s needed. This integrated system lets us balance supply and demand, while optimizing the full process, from energy capture to end use. That forms a zero-carbon building technology framework specifically tailored to plateau regions.

Solar Building and Environment Team conducting research on the Qinghai-Tibet Plateau

How can AI, sensors, and IoT systems help buildings use energy more efficiently and automatically regulate indoor environments?

AI-driven digital technologies help predict, control, and optimize complex building energy systems. On the plateau, conditions such as low atmospheric pressure, high solar radiation, and large temperature swings can affect the ability of sensors to provide accurate measurements. Systems therefore must be robust and well-calibrated. By combining sensor data with digital twins and predictive models, buildings can better match energy supply with demand. This enhances system reliability, operational stability, and energy efficiency, particularly in remote areas where maintenance is limited.

Alongside colleagues at the Xi'an University of Architecture and Technology, you’ve been working and helping in the Tibetan Plateau since 2007. How do you assess the impact your work has had on local communities?

Since 2007, we have worked on renewable energy heating projects across western China. What began with individual buildings has expanded to megawatt-scale district heating systems, along with the development of technical standards and engineering guidelines. These approaches have been implemented in multiple regions, providing models for zero-carbon development that can be replicated at the building, community, and town levels, and supporting more sustainable local development.

Solar district heating project in Qusong County, Tibetan Plateau

You’ve been quoted as saying the driving forces behind the research are: “How to promote technological optimization? How to achieve multi-energy complementarity? How to achieve precise control? How to achieve near-zero energy consumption operation?” How close are you to success on all four fronts?

These goals reflect a transition from optimizing individual components to designing integrated systems. At the design level, we now have relatively mature approaches. Thermal storage efficiency now exceeds 90%, significantly improving overall system performance.

We have also developed solar-dominant hybrid systems, where solar provides most of the energy, supplemented by auxiliary energy. For system control, operations are shifting from empirical approaches to data-driven strategies, improving our ability to optimize operations and diagnose faults. For near-zero energy operation, solar now provides almost 90% of the heating while maintaining stable indoor comfort. Overall, these advances indicate a transition toward integrated systems; however, further work remains in the areas of long-term reliability, climate adaptability, and scalability.

Solar heating virtual simulation experiment platform software

Many cities have large numbers of older buildings. How feasible is it to retrofit existing buildings with intelligent energy systems and digital infrastructure?

Integrating advanced energy systems into existing buildings is technically feasible but more complex than in new construction, and often depends on policy and economic support. A phased retrofit strategy is more practical: first improving building envelope performance and baseline heating systems, then gradually integrating renewable energy systems, intelligent control, and digital infrastructure. This incremental approach strikes a better balance between feasibility and cost.

What kinds of collaborations between universities and tech companies are most valuable for turning research into real-world applications?

Effective collaboration between academia and industry is essential for technology transfer. A complete innovation chain should encompass basic research, technology development, engineering demonstration, and large-scale deployment. In our practice, collaboration with local governments and energy enterprises has enabled a relatively complete pathway from pilot projects to standard development and commercialization. A key challenge, however, remains the misalignment between research outputs and practical demand. Strengthening the role of academia in leading innovation and aligning research with real-world needs will be critical.

What role can large AI models play in university research? What gaps might they fill? What new potential could they unlock?

Large-scale AI models offer new tools for complex system modeling, combining data from multiple sources, and optimizing operations. For now, they should be seen as supporting engineering work rather than replacing it. Looking ahead, the biggest opportunity is to combine data-driven approaches with physical models and real-world engineering practice.

Will the future of smart buildings depend more on digital intelligence, or on better architectural and environmental design?

The future of smart buildings lies in the integration of intelligent technologies with high-performance architectural design. Passive design strategies—such as orientation, insulation, and shading—form the foundation of near-zero energy buildings, while digital technologies enhance system efficiency and operation. Both are indispensable and mutually reinforcing.

What is the single most important technological change needed to make smart, low-carbon buildings a reality, at scale? How can companies like Huawei contribute to that change?

The key challenge is coordinating renewable energy generation, storage, and system control. To use energy efficiently, we must better align supply and demand over time, storing or shifting energy so it’s available when needed. Companies such as Huawei can play a critical role by collaborating with academia and the engineering industry to implement these systems at scale.

As AI, the IoT, and digital infrastructure continue to develop rapidly, what will the truly “intelligent” building of the future look like?

In the long term, intelligent buildings will become integrated into broader energy and information networks. They will be able to sense their environment, respond adaptively, and operate as part of multi-energy systems.

In solar-rich regions, this will extend beyond individual buildings to encompass entire communities and districts, forming integrated, zero-carbon, and resilient built environments at a regional scale.