Trend AnalysisOther Engineering
Building Energy Efficiency and Smart HVAC: Machine Learning Meets Phase Change Materials
Buildings consume 40% of global energy, and HVAC systems account for roughly half of that. Machine learning-driven optimization of heating, cooling, and thermal storage is emerging as the fastest path to significant energy savings without sacrificing occupant comfort.
By Sean K.S. Shin
This blog summarizes research trends based on published paper abstracts. Specific numbers or findings may contain inaccuracies. For scholarly rigor, always consult the original papers cited in each post.
Buildings are responsible for approximately 40% of global energy consumption and 36% of CO2 emissions. Within buildings, heating, ventilation, and air conditioning (HVAC) systems are the dominant energy consumers, accounting for 40-60% of total building energy use. The potential for efficiency gains is enormous: most HVAC systems operate on fixed schedules or simple thermostatic control, ignoring occupancy patterns, weather forecasts, thermal mass dynamics, and electricity price signals.
The convergence of IoT sensors, machine learning algorithms, and advanced thermal storage materials is creating a new generation of buildings that actively optimize their own energy consumption in real time.
Why It Matters
Decarbonizing the building sector is essential for meeting Paris Agreement targets. While new construction can incorporate passive design from the start, the vast majority of 2050's building stock already exists today. Retrofit solutions---smart HVAC controls, thermal storage, and AI-based energy management---are therefore the highest-impact interventions available.
The Research Landscape
Phase Change Materials with ML Optimization
Mikhailovna and Abdullaev (2024), with 5 citations, combine phase change materials (PCMs) with hybrid machine learning optimization for smart building energy storage. PCMs absorb and release heat during phase transitions (solid to liquid), effectively smoothing temperature fluctuations. Their ML-optimized approach determines the optimal placement, quantity, and melting point of PCMs within building envelopes, achieving 15-25% energy savings over conventional insulation.
Autonomous Energy Auditing
Ashraf and Abdellatif (2025) present a fully automated energy management system using ML across three phases: load classification, benchmarking, and smart monitoring. Their system categorizes electrical loads, identifies inefficient equipment, and recommends interventions---replacing the costly and intermittent process of manual energy audits with continuous, data-driven optimization.
Intelligent HVAC Control
Cengiz (2024) investigates neural network and genetic algorithm approaches for HVAC optimization in smart buildings. The key finding: model predictive control using neural network building models can reduce HVAC energy consumption by 20-30% while maintaining or improving thermal comfort, primarily by pre-cooling or pre-heating based on predicted occupancy and weather.
Woldegiyorgis and Asmare (2025) review the broader landscape of AI applications in building energy performance, identifying the critical barriers to adoption: data quality and availability, model interpretability for building managers, and integration with legacy building management systems.
Smart Building Technology Stack
<
| Layer | Technology | Function | Maturity |
|---|
| Sensing | IoT temperature, occupancy, CO2 | Real-time building state | Mature |
| Storage | Phase change materials | Thermal energy buffering | Growing |
| Control | ML-based predictive control | Optimal HVAC scheduling | Emerging |
| Management | Autonomous energy auditing | Continuous optimization | Early stage |
What To Watch
The integration of building energy systems with grid-level demand response is the next frontier. Buildings that can shift their energy consumption to periods of renewable energy surplus---using thermal storage as a buffer---become active participants in grid stability rather than passive loads. This building-to-grid intelligence requires the ML and PCM technologies described here to work in concert.
Buildings are responsible for approximately 40% of global energy consumption and 36% of CO2 emissions. Within buildings, heating, ventilation, and air conditioning (HVAC) systems are the dominant energy consumers, accounting for 40-60% of total building energy use. The potential for efficiency gains is enormous: most HVAC systems operate on fixed schedules or simple thermostatic control, ignoring occupancy patterns, weather forecasts, thermal mass dynamics, and electricity price signals.
The convergence of IoT sensors, machine learning algorithms, and advanced thermal storage materials is creating a new generation of buildings that actively optimize their own energy consumption in real time.
Why It Matters
Decarbonizing the building sector is essential for meeting Paris Agreement targets. While new construction can incorporate passive design from the start, the vast majority of 2050's building stock already exists today. Retrofit solutions---smart HVAC controls, thermal storage, and AI-based energy management---are therefore the highest-impact interventions available.
The Research Landscape
Phase Change Materials with ML Optimization
Mikhailovna and Abdullaev (2024), with 5 citations, combine phase change materials (PCMs) with hybrid machine learning optimization for smart building energy storage. PCMs absorb and release heat during phase transitions (solid to liquid), effectively smoothing temperature fluctuations. Their ML-optimized approach determines the optimal placement, quantity, and melting point of PCMs within building envelopes, achieving 15-25% energy savings over conventional insulation.
Autonomous Energy Auditing
Ashraf and Abdellatif (2025) present a fully automated energy management system using ML across three phases: load classification, benchmarking, and smart monitoring. Their system categorizes electrical loads, identifies inefficient equipment, and recommends interventions---replacing the costly and intermittent process of manual energy audits with continuous, data-driven optimization.
Intelligent HVAC Control
Cengiz (2024) investigates neural network and genetic algorithm approaches for HVAC optimization in smart buildings. The key finding: model predictive control using neural network building models can reduce HVAC energy consumption by 20-30% while maintaining or improving thermal comfort, primarily by pre-cooling or pre-heating based on predicted occupancy and weather.
AI for Building Performance
Woldegiyorgis and Asmare (2025) review the broader landscape of AI applications in building energy performance, identifying the critical barriers to adoption: data quality and availability, model interpretability for building managers, and integration with legacy building management systems.
Smart Building Technology Stack
<
| Layer | Technology | Function | Maturity |
|---|
| Sensing | IoT temperature, occupancy, CO2 | Real-time building state | Mature |
| Storage | Phase change materials | Thermal energy buffering | Growing |
| Control | ML-based predictive control | Optimal HVAC scheduling | Emerging |
| Management | Autonomous energy auditing | Continuous optimization | Early stage |
What To Watch
The integration of building energy systems with grid-level demand response is the next frontier. Buildings that can shift their energy consumption to periods of renewable energy surplus---using thermal storage as a buffer---become active participants in grid stability rather than passive loads. This building-to-grid intelligence requires the ML and PCM technologies described here to work in concert.
References (7)
[1] Mikhailovna, R. T., Nasrabadi, M., & Abdullaev, S. (2024). A novel hybrid optimization and ML technique for energy storage in smart buildings using PCMs. International Journal of Low-Carbon Technologies.
[2] Ashraf, S., Zarie, M. M., & Abdellatif, S. O. (2025). Towards autonomous energy management: ML for effective auditing. Scientific Reports.
[3] Cengiz, K. (2024). Optimizing Energy Efficiency in Smart Buildings Through Intelligent HVAC Control. IEEE ISMSIT.
[4] Woldegiyorgis, T. A., Li, H. X., & Asmare, E. (2025). Harnessing AI to improve building performance and energy use. Energy Informatics.
Mikhailovna, R. T., Nasrabadi, M., Abdullaev, S., Pourasad, Y., Alviz-Meza, A., & Benti, N. E. (2024). A novel hybrid optimization and machine learning technique to energy storage in smart buildings using phase change materials. International Journal of Low-Carbon Technologies, 19, 1477-1490.
Ashraf, S., Zarie, M. M., & Abdellatif, S. O. (2025). Towards autonomous energy management: machine learning for effective auditing and optimization. Scientific Reports, 15(1).
Woldegiyorgis, T. A., Li, H. X., Asmare, E., Assamnew, A. D., Admassu, F. C., Desalegn, G. A., et al. (2025). Harnessing Artificial Intelligence to improve building performance and energy use: innovations, challenges, and future perspectives. Energy Informatics, 8(1).