Achieving climate neutrality requires reducing energy consumption and CO₂ emissions in the building sector, which has prompted increasing attention towards nearly zero energy, zero energy, and positive energy communities of buildings; there is a need to determine how individual buildings up to communities of buildings can become more energy efficient. This study addresses the scientific problem of optimizing energy efficiency strategies in building areas and identifies gaps in existing theories related to passive design strategies, active energy systems, and renewable energy integration. This study delineates boundaries at the building and community scales to examine the challenges of attaining energy efficiency goals and to emphasize the intricate processes of selecting, integrating, and optimizing energy systems in buildings. The four boundaries describe: (B1) energy flows through the building envelope; (B2) energy flows through heating, ventilation, air conditioning and energy systems; (B3) energy flows through individual buildings; (B4) energy flows through a community of buildings. Current theories often treat these elements in isolation, and significant gaps exist in interdisciplinary integration, scalable frameworks, and the consideration of behavioral and socioeconomic factors. Achieving nearly zero energy, zero energy, and positive energy communities requires seamless integration of renewable energy sources, energy storage systems, and energy management systems. The proposed boundaries B1–B4 can help not only in analyzing the various challenges for achieving high energy efficiency in building communities but also in defining and evaluating these communities and establishing fair methods for energy distribution within them. The results demonstrate that these boundaries provide a comprehensive framework for energy-efficient designs, constructions, and operational practices across multiple buildings, ensuring equitable energy distribution and optimized performance. In addition, the definition of boundaries as B1-B4 contributes to providing an interface for energy-efficient designs, constructions and operational practices across multiple buildings.

Managing Heating, Ventilation and Air-Conditioning (HVAC) optimally with respect to energy efficiency and adaptively with respect to uncertainties is a recognized bottleneck for multi-zone buildings. This work proposes a management approach where HVAC operation is approximated using averaged thermal dynamics. By doing this, energy management becomes effective due to the linear form of the averaged dynamics, making it possible to use optimal and adaptive control tools. The proposed approach exhibits favorable features for implementation in energy management programs: minimal parameters to be estimated (only those directly related to the thermal coefficients); preservation of the distributed multi-zone topology (only the thermal coefficients with neighboring rooms need to be estimated); simplicity of imposing positivity and controllability properties. Optimality and adaptation are studied analytically in the framework of adaptive linear quadratic control. Numerical validations show that the proposed approach strikes optimal trade-offs between energy consumption and thermal comfort, while handling unknown and possibly time-varying parameters. Improved trade-offs span 3-12% with respect to PID control, 14-47% with respect to non-adaptive control, and 1-20% with respect to alternative adaptation methods proposed in the literature.

Shaping the power consumption of districts via thermostatically controlled loads (TCLs) requires demand response (DR) programs to face the inevitable heterogeneity of the TCLs and buildings populating the district. The presence of heterogeneity makes district power shaping challenging because all buildings receive by the DR program the same thermostatic shift for each type of TCL, irrespective of their heterogeneity. This work studies the feasibility of DR with heterogeneous types of TCLs and buildings. To study the effect of heterogeneity, we create a district simulator benchmark with buildings belonging to different classes [lightweight (LW), nearly-zero energy (NZ), and massive passive (MP)] and equipped with different TCLs (heating units, air conditioner units, and boiler units). To shape the power consumption of the district, we propose a DR program studied analytically in the framework of adaptive linear quadratic control. Not only the proposed DR program is shown capable of regulating the district power consumption despite the large heterogeneity, but comparisons with a learning-based program reveal favorable performance in terms of power tracking error, size of the thermostatic shifts, and robustness to different district scenarios. Interestingly, the proposed solution takes advantage of heterogeneity by making different types of TCLs serve different tasks, for example, some units serve coarse regulation of the power, while others serve finer regulation.