Artificial Intelligence and Building Energy Efficiency: Promises and Concrete Applications

Buildings are at the heart of the climate challenge. They account for about 40% of global carbon dioxide emissions and consume a large share of produced energy, mainly for heating, ventilation, and air conditioning. This finding, highlighted in a recent European study [6], underlines the importance of energy renovation, even though it remains costly and complex.

In this context, artificial intelligence and machine learning are emerging as powerful levers. A review published in 2022 shows that these technologies make it possible to predict consumption, optimize equipment performance, and improve operational management. Researchers also point out that hybrid models and deep learning techniques often deliver the highest accuracy [1]. Reducing the energy footprint of buildings is therefore increasingly based on algorithms capable of learning from data to guide decisions.

When Artificial Intelligence Becomes the Architect of Energy

A bibliometric analysis published in 2025 highlights three major functions of machine learning in this field [4]:

• forecasting energy consumption, for example based on historical and weather data;

• intelligent control, which consists in real-time management of heating, ventilation, and air conditioning systems;

• design optimization, which integrates energy performance as early as the construction or renovation stage.

To address these challenges, researchers rely on artificial neural networks and support vector machines. However, there is a growing trend toward hybrid models, which combine different approaches to achieve greater accuracy while reducing the risk of errors [1].

Beyond academic work, concrete initiatives are emerging. One of the most notable is the Artificial Intelligence for Energy Efficiency (AI4EF) platform, developed within the framework of the European Enershare project and tested in Latvia on real-world data [6]. This modular solution offers several services: recommendations for energy renovation, calculations of the economic and environmental impact of photovoltaic installations, and even a space where experts can train their own models.

Its major advantage is that it can provide useful results even with limited initial data. Unlike other more demanding solutions, it allows building managers or local authorities to quickly obtain a diagnosis without requiring advanced technical expertise [6].

Buildings That Learn

Several studies confirm the potential of these approaches. Applying machine learning to heating, ventilation, and air conditioning systems has been shown to significantly reduce energy consumption while improving occupant comfort, thanks to automatic adjustments of temperature and humidity [3].

Research has also focused on predicting building energy performance. Since 2019, numerous models have been developed to estimate future consumption by factoring in occupancy, weather conditions, and building condition [4]. These predictions provide a solid basis for planning more targeted renovations.

Finally, artificial intelligence is not limited to energy consumption. It also contributes to managing indoor air quality, lighting, and thermal comfort. It can even detect anomalies, such as an impending breakdown in an air conditioning system, thereby preventing unnecessary overconsumption [5]. A building equipped with sensors and algorithms thus becomes an adaptive organism, capable of learning and optimizing itself continuously.

Challenges to Overcome

These promising advances nevertheless face several obstacles:

• Limited access to data: many studies are still based on prototypes or simulations that are difficult to replicate in real-world conditions [5];

• Lack of transparency: the most accurate models, such as deep learning, often function as “black boxes.” Their opacity undermines user trust, which is why some researchers propose explainable artificial intelligence approaches [2];

• Limited adoption: despite the surge in scientific publications, real-world deployments remain rare, constrained by costs, lack of expertise, and absence of common standards [3][4].

Towards Shared Energy Intelligence

One particularly promising avenue is the secure sharing of data. The AI4EF platform illustrates this orientation by relying on the Enershare Data Space, a European framework that enables the pooling of data from different countries and buildings [6]. This cooperation makes it possible to train more robust and reliable models.

Beyond this collaborative dimension, the future is shaped by two main directions: making models more explainable and data more interoperable [2], and exploring new techniques such as transfer learning, which adapts a model trained on one building to other contexts with little additional data [4], or hybrid approaches, which combine several methods to maximize both accuracy and reliability [1].

​​Beyond Design: AI for Energy Retrofit of Existing Buildings

While many applications of artificial intelligence focus on planning or new construction, AI can play an equally decisive role in the renovation of existing buildings. In retrofit contexts, AI can automatically recommend improvement measures (insulation, window replacement, HVAC optimization, heat recovery), relying on available data (historical consumption, sensors, building characteristics). For example, a recent publication proposes an approach combining generative networks (CTGAN) to enrich data with explainable models (SHAP) to identify the critical variables influencing retrofit decisions, a way to overcome the limitations of scarce data while ensuring transparency in recommendations [7].

Furthermore, a review dedicated to retrofit projects in the construction sector highlights that AI is already widely used to support post-audit energy assessment, simulate expected energy savings, optimize the selection of renovation packages, and manage trade-offs between costs and performance [8].

By combining these applications, AI turns existing buildings into adaptive systems: it can prioritize the most cost-effective interventions, guide managers in tracking results after renovation, and adjust future scenarios according to constraints (budgets, comfort, emissions).

Conclusion: Artificial Intelligence as a Climate Ally

Research converges on the same conclusion: artificial intelligence and machine learning provide powerful tools to make buildings more energy efficient. From consumption forecasting to smart management of indoor comfort, and through concrete platforms such as AI4EF, applications are multiplying.

Yet for these solutions to reach their full potential, it will be necessary to ensure access to representative data, develop transparent models, and create the conditions for large-scale adoption.

If these obstacles are overcome, artificial intelligence could become a central pillar of the energy transition, transforming buildings from simple consumers into intelligent actors in the fight against climate change.

Sources

[1] Ardabili, S., et al. (2022). Systematic review of deep learning and machine learning for building energy. arXiv. 

[2] Chen, Z. (2023). Interpretable machine learning for building energy. Patterns, 4(2), 100694. Elsevier. 

[3] Das, H. P., et al. (2024). Machine learning for smart and energy-efficient buildings. Environmental Data Science, 3, e25. Cambridge University Press.

[4] Liu, J., et al. (2025). Applications and trends of machine learning in building performance prediction: A bibliometric review. Buildings, 15(7), 994. MDPI. 

[5] Ogundiran, J., et al. (2024). Artificial intelligence for energy efficiency and indoor environmental quality: A systematic review. Sustainability, 16(9), 3627. MDPI.

[6] Tzortzis, A. M., Kormpakis, G., Pelekis, S., Michalitsi-Psarrou, A., Karakolis, E., Ntanos, C., & Askounis, D. (2024). AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector. arXiv. 

[7] Voulgaris, Z., Ntanos, C., Tzortzis, A., & Askounis, D. (2025). Data-driven building retrofit decision-making: Combining CTGAN and SHAP for transparent recommendations.

[8] Mugahed Amran, Y. H., Al-Sakkaf, A., & Fediuk, R. (2024). Application of artificial intelligence in retrofitting building construction projects: A systematic review. Archives of Computational Methods in Engineering. Springer

Would you like to learn more about the practical side of smart buildings ? Feel free to check out our article ' Smart Building Technology: Monitoring and Automation for Real-Time Efficiency ' to explore the topic further.