Building Energy Efficiency Research Articles

Multi-objective decision optimization design for building energy-saving retrofitting design based on improved grasshopper optimization algorithm

With the national emphasis on building energy efficiency planning, energy efficiency optimization in existing buildings requires renovation measures based on multi-objective factors. In order to get the optimal solution in the multi-objective decision-making of renovation, the study proposes a class of improved grasshopper optimization algorithms. The process employs a systematic methodology to identify an optimal energy renovation method, taking into account the specific characteristics of the building environment. It then classifies and formulates the energy reduction substitution items for building renovation, and finally, it synchronizes the cost of the renovation project as a measure for decision-making. The elite inverse strategy approach enhances the grasshopper optimization algorithm to facilitate the multi-objective decision-making process associated with building renovation measures. The results showed that the improved grasshopper optimization algorithm could achieve a decision accuracy of 98.8% for the test samples, which was 5.5% higher than the accuracy of the particle swarm optimization algorithm. Repeated run tests of the research algorithm for multi-objective decision making yielded a mean decision fitness value of 2.34×104 and a data extreme value of 0.38×104. Compared to other algorithms improved grasshopper optimization algorithm converged in a lower range of fitness values, which indicated that the algorithm worked well for multi-objective optimization and the model repeatability was good. The research algorithm was used to decide the energy efficient renovation planning of the building and the power consumption of the renovated power supply system was reduced by 23.7%-49.6%. This indicates that the renovated building has better energy efficiency and can provide a reliable technical direction for decision-making optimization of building energy efficiency renovation.

Development of novel multifunctional phase change microcapsules for improving thermal comfort and radiation properties

Microencapsulated phase change materials (MPCMs) have gained widespread application in energy-efficient buildings, aiming to improve thermal comfort and reduce energy consumption. To enhance the engineering feasibility of MPCMs, researchers are now focusing on developing MPCMs with multifunctional properties, beyond single thermal storage functionality. In this study, we proposed a novel and straightforward approach to fabricate multifunctional MPCMs with BaCO3 as shell and a core composed of binary phase change materials (PCMs) and lipophilic-modified graphite (MG) via self-assembly method. The incorporation of BaCO3 shell provides MPCMs with additional resistance to ionizing radiation pollution due to the high atomic number element Ba. Additionally, the incorporation of MG into PCMs enhances its temperature sensitivity. The thermal analysis indicates that the binary PCMs’ composition in MPCMs results in two phase transition temperatures at 3.3 °C and 26.4 °C, making them capable of thermal storage in line with comfortable residential temperatures across different seasons. Furthermore, MPCMs were introduced into cement paste (CPM) and used to construct cement chambers. Adding 10 wt% MPCMs in the CPM chambers placed outdoors in Wuhan (from June 7th to June 9th) led to a 23.8 % decrease in temperature fluctuations compared to the reference sample. Radiation shielding results indicated that the linear attenuation coefficient (μ) of CPM increased by 58.7 %, due to enhanced incoherent scattering and photoelectric effects between high atomic number elements and photons. Additionally, XCOM simulations predicted a strong correlation between MPCM content and μ values, with particularly high accuracy at low photon energies. Overall, the multifunctional MPCMs developed in this study not only offers a new route for reducing residential energy consumption and shielding ionizing pollution, but also broadens the applicability of traditional PCMs in the building and other fields.

Physics-informed ensemble learning with residual modeling for enhanced building energy prediction

Accurate modeling of building energy use is important to support a diverse spectrum of its downstream applications, such as building energy efficiency assessment, resilience analysis, smart control, etc. As mainstream approaches of building energy modeling, physics-based modeling builds on different fidelities of physics rules yet are usually compromised in modeling accuracy due to insufficiency of physics rules to capture real-world dynamics and incomplete input information. Data-driven approaches are computationally efficient, but black box (uninterpretable) in nature. For improved modeling of building energy use, this work proposes a physics-informed ensemble learning approach in building energy prediction through residual modeling. Specifically, we first analyze the components of building energy use data. Evidence suggests that the building energy use data can be decomposed into the physics-driven part, occupant-driven part, and white noise. Second, high-fidelity physics-based building models (EnergyPlus) and low-fidelity ones (RC models) are developed to capture the physics-driven part while time series methods are explored as the residual modeling approach to capture the occupant-driven discrepancies between physics-based simulation and measured building energy use (i.e., residuals). Finally, the physics-informed ensemble learning is proposed to integrate physics-based and data-driven models for enhanced accuracy of building energy modeling. Results demonstrate 40–90% decrease of discrepancies between modeling and field observations compared to traditional physics-based modeling methods. Moreover, when the training dataset size is small, the proposed ensemble model overperforms the pure data-driven models, demonstrating its higher robustness in extrapolation scenarios. This work makes fundamental contributions to the development of convergent modeling approaches in the building modeling field.

Integrating Machine Learning and Genetic Algorithms to Optimize Building Energy and Thermal Efficiency Under Historical and Future Climate Scenarios

As the global energy demand rises and climate change creates more challenges, optimizing the performance of non-residential buildings becomes essential. Traditional simulation-based optimization methods often fall short due to computational inefficiency and their time-consuming nature, limiting their practical application. This study introduces a new optimization framework that integrates Bayesian optimization, XGBoost algorithms, and multi-objective genetic algorithms (GA) to enhance building performance metrics—total energy (TE), indoor overheating degree (IOD), and predicted percentage dissatisfied (PPD)—for historical (2020), mid-future (2050), and future (2080) scenarios. The framework employs IOD as a key performance indicator (KPI) to optimize building design and operation. While traditional indices such as the predicted mean vote (PMV) and the thermal sensation vote (TSV) are widely used, they often fail to capture individual comfort variations and the dynamic nature of thermal conditions. IOD addresses these gaps by providing a comprehensive and objective measure of thermal discomfort, quantifying both the frequency and severity of overheating events. Alongside IOD, the energy use intensity (EUI) index is used to assess energy consumption per unit area, providing critical insights into energy efficiency. The integration of IOD with EUI and PPD enhances the overall assessment of building performance, creating a more precise and holistic framework. This combination ensures that energy efficiency, thermal comfort, and occupant well-being are optimized in tandem. By addressing a significant gap in existing methodologies, the current approach combines advanced optimization techniques with modern simulation tools such as EnergyPlus, resulting in a more efficient and accurate model to optimize building performance. This framework reduces computational time and enhances practical application. Utilizing SHAP (SHapley Additive Explanations) analysis, this research identified key design factors that influence performance metrics. Specifically, the window-to-wall ratio (WWR) impacts TE by increasing energy consumption through higher heat gain and cooling demand. Outdoor temperature (Tout) has a complex effect on TE depending on seasonal conditions, while indoor temperature (Tin) has a minor impact on TE. For PPD, Tout is a major negative factor, indicating that improved natural ventilation can reduce thermal discomfort, whereas higher Tin and larger open areas exacerbate it. Regarding IOD, both WWR and Tin significantly affect internal heat gains, with larger windows and higher indoor temperatures contributing to increased heat and reduced thermal comfort. Tout also has a positive impact on IOD, with its effect varying over time. This study demonstrates that as climate conditions evolve, the effects of WWR and open areas on TE become more pronounced, highlighting the need for effective management of building envelopes and HVAC systems.

A Review on the Effective Utilization of Organic Phase Change Materials for Energy Efficiency in Buildings

Energy efficiency is critical for achieving building sustainability because it means that fewer resources are consumed. In this context, the advancement of phase-changing materials has attracted attention with regard to the integration and management of energy efficiency in construction projects. Buildings consume 40% of the global energy output annually, accounting for one-third of the global greenhouse gas emissions. For hot weather-prone construction, PCMs should have a melting temperature of 25–50 °C. For more than 30 years, researchers worldwide have experimented with PCMs at various temperatures, but few studies have been conducted in hot or harsh environments. According to recent studies, the amount of PCMs in construction materials has been limited to 20%, and exceeding this ratio was shown to significantly affect the compressive strength of concrete specimens. In this study, various phase-changing concrete materials were investigated to reduce the thermal energy consumption of buildings. This paper aims to provide an overview of the current state-of-the-art phase change materials for constructing thermal energy storage building materials. It also includes a brief review of the most recent developments in phase change technologies and their encapsulation techniques based on thermophysical properties. Implementing PCM technology in buildings will also maintain good indoor air quality. These materials are widely used in various real-time applications to significantly enhance thermal comfort in buildings.

Web Server Based Electrical Control System Analysis for Smart Buildings

Energy management in smart buildings still faces challenges in optimizing energy use, particularly for high-load devices, such as HVAC systems and lighting. Conventional control systems are often inadequate for optimizing energy usage based on the operational needs of the building. This study aims to develop and analyze a web server-based electrical energy control system that can be accessed in real time to improve the energy efficiency of smart buildings. This study employed a quasi-experimental method by implementing a web server-based control system in a smart building and comparing the energy consumption before and after the application of the system. The results show that the system reduces the energy consumption by up to 25%, particularly for HVAC systems and lighting. The most significant energy savings occurred during off-peak hours, when the system automatically reduced power for unnecessary devices. The implications of this research suggest that a web server-based control system not only enhances energy efficiency and reduces operational costs, but also provides greater flexibility in energy management through more adaptive and responsive remote control. This research contributes to the development of more sustainable energy management technologies for smart buildings, with wide potential applications in commercial and institutional building scenarios

Transparent and Thermally Stable Rare-Earth-Doped Luminescent Gallate Glass toward Passive Daytime Radiative Cooling Applications.

Currently, the implementation of passive daytime radiative cooling based on zero-energy cooling methodologies primarily focuses on polymers and composite materials, whereas the available literature on all-inorganic materials is relatively few. Here, we present a novel microcrystalline glass material CaGa0.5Al1.5O4 (CGAO), doped with rare-earth elements and prepared by the high-temperature melting method. This material exhibits long-term stability at 200 °C, coupled with an effective infrared radiation cooling function, demonstrating a 4.9 °C temperature reduction at solar noon. The energy transfer and luminescence mechanisms of Tb3+ and Sm3+ doped CGAO glass have been thoroughly investigated, along with thorough assessments of its thermal stability and hardness. The glass exhibits ultrahigh light transmission in the ultraviolet to near-infrared range, with the transmittance reaching 98% in specific spectral bands. Furthermore, it demonstrates superior luminescent thermal stability, retaining 85.6% and 71.2% of its initial luminescence intensity at 423 and 523 K, respectively. The high-temperature resistance and stability and long-term cooling properties render CGAO glass as an optimal candidate for integration into future energy-efficient and sustainable building designs.

The Regulation of Temperature Fluctuations and Energy Consumption in Buildings Using Phase Change Material–Gypsum Boards in Summer

This study examined the thermal performance of Comfortboard23, a commercial gypsum board from Knauf infused with phase change material (PCM). Structural characterization using XRD and SEM confirmed the presence of microencapsulated PCM within the gypsum matrix. The study does not provide a direct comparison between Comfortboard23 and other PCM-integrated gypsum boards on the market. This is a limitation of the research. A comprehensive comparison would involve testing multiple products under identical conditions, and evaluating factors such as thermal performance, cost-effectiveness, durability, and ease of installation. Thermal characterization involved a novel low-scale thermal chamber to measure U-value, thermal conductivity, heat storage capacity, and dynamic thermal response. Results showed incorporating PCM decreased the U-value by 2% compared to standard gypsum boards. Additionally, PCM inclusion increased heat storage capacity by around 45% and improved dynamic thermal characteristics by decreasing thermal stability coefficient from 0.92 to 0.76 and increasing thermal lag from 0.27 to 0.49 h. The 45% increase in heat storage capacity of Comfortboard23 could lead to a 10–20% reduction in heating and cooling energy consumption, improved thermal comfort, and potential HVAC downsizing. Exact benefits depend on climate, building design, and occupancy patterns, necessitating further research in diverse real-world settings. The findings demonstrate Comfortboard23’s potential for enhancing thermal energy storage in buildings, contributing to energy savings, improved thermal comfort, and reduced temperature fluctuations across varying daily temperatures. Overall, the study highlights the promise of Comfortboard23 as an energy-efficient PCM-integrated building material.

Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process

This paper proposes design guidelines to enhance energy efficiency and energy generation potential in active solar buildings. Additionally, it presents a variety of optimized urban forms characterized by attributes such as shape, layout, and number of buildings on the plot. These urban configurations are classified into solar species, each associated with a distinct range of high passive and active solar potential. These results were achieved by developing and applying a simulation-driven, multi-objective optimization technique for the early-stage design of a residential building cluster in a temperate climate. This method leverages both passive and active energy indicators, employing a genetic algorithm to identify optimal forms that maximize active solar potential while also minimizing operational energy demand. The approach utilizes a parametric modelling routine that relies on vertical cores and horizontal connections to produce design iterations featuring irregular geometry, while ensuring structural continuity and means of egress. The findings reveal a significant variability in onsite energy generation, with optimized solutions differing by a factor of 2.5 solely based on shape, underscoring the critical role of active solar potential. Taken together, these results hint at the descriptive and predictive capabilities of these solar species, making them a promising heuristic model for characterizing urban form in relation to energy performance.

Improving the Energy Efficiency of the Modular Buildings Drying Room: A Case Study of Construction Site Cabins

Improving the Energy Efficiency of the Modular Buildings Drying Room: A Case Study of Construction Site Cabins

Experimental study and simulation of a solid desiccant cooling system installed in northern Algeria

An experimental investigation of a solid desiccant cooling system for a research laboratory on the Algerian coast has been carried out. The system utilized flat-plate solar collectors for regeneration and was evaluated through a combination of individual component characterization, system modeling (using TRNSYS), and experimental validation. The results obtained have been validated and show that the efficiency of the rotary heat exchanger reached 65 %, the evaporative humidifier efficiency reached 94 %, and the desiccant wheel achieved efficiencies of 73 % on the process side and 66 % on the regeneration side. This translates to a significant dehumidification effect, reducing absolute humidity in the treated air by 9 g/kg, for the supply air temperature effectively maintained at a comfortable 24 °C. The close agreement between the simulated and experimental COP values (1.13 and 1.08, respectively) validates the chosen approach and confirms the potential of this technology for energy-efficient building cooling in the hot and humid Mediterranean climate of northern Algeria.

Radon Concentration Assessment in Urban Romanian Buildings: A Multistory Analysis

Radon (Rn 222) is a significant contributor to natural radiation exposure in residential environments such as single-family houses and multistory buildings. This study monitored radon activity concentration (RAC) in 455 apartments in 30 multistory buildings in Buzău, Romania. Integrated measurements of the RAC using CR-39 nuclear track detectors were conducted for a period of 3 to 4 months. The results revealed that the RAC varies between buildings, with an annual average between 33 and 77 Bq/m3. This variation may be attributed to poor ventilation and the chimney effect in common ventilation ducts, which may facilitate radon displacement vertically. Also, apartments with low occupancy or inadequate ventilation showed higher radon levels of up to 285 Bq/m3. The study highlights the potential risk of increased radon exposure in energy-efficient buildings due to poor ventilation, emphasizing the need for special attention to radon mitigation measures in building design. The results emphasize that the RAC is influenced by building characteristics, room use, and ventilation, with significant implications for health risks in urban residential environments.

Navigating the Nexus of Artificial Intelligence and Renewable Energy for the Advancement of Sustainable Development Goals

The integration of artificial intelligence (AI) into renewable energy and sustainability represents a transformative approach toward achieving sustainable development goals (SDGs), especially SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). This study utilized the PRISMA framework to conduct a systematic review, focusing on the role of AI in renewable energy and sustainable development. This research utilized Scopus’s curated AI research area, which employs text mining to refine AI concepts into unique keywords. Further refinement via the All Science Journals Classification system and SDG-mapping filters narrowed the focus to publications relevant to renewable energy and SDGs. By employing the BERTopic modeling approach, our study identifies major topics, such as enhancing wind speed forecasts, performance analysis of fuel cells, energy management in elective vehicles, solar irradiance prediction, optimizing biofuel production, and improving energy efficiency in buildings. AI-driven models offer promising solutions to address the dynamic challenges of sustainable energy. Insights from academia-industry collaborations indicate that such partnerships significantly accelerate sustainable-energy transitions, with a focus on AI-driven energy storage, grid management, and renewable-energy forecasting. A global consensus on the critical role of investing in technology-driven solutions for energy sustainability was underscored by the relationship between funding data and global R&D spending patterns. This study serves as a resource for practitioners to harness AI technologies for renewable energy, where for example, AI’s accurate wind speed predictions can increase wind farm efficiency, highlighting the necessity of innovation and collaboration for sustainable development.

Intelligent Monitoring and Visualization System for High Building Nighttime Utilization Based on Image Processing

The rise of complex high-rise buildings has made building management increasingly challenging, especially the nighttime supervision of university laboratories. Idle occupation increases the risk of accidents and undermines campus sustainability. Effective occupancy detection is essential for optimizing campus building safety and energy efficiency. Environmental sensors for occupancy detection offer limited coverage and are costly, making them unsuitable for campuses. Surveillance cameras, as part of campus infrastructure, provide wide coverage. On this basis, we designed a detection algorithm that uses light brightness to assess nighttime building use. Experimental results showed that the algorithm achieves an average accuracy of 98.67%, enabling large-scale nighttime occupancy detection without the need for installing additional sensors, significantly improving the efficiency of campus building management. In addition, to address the limitations of indoor space representation in geographic information system (GIS) management models, this paper developed a comprehensive 3D GIS model based on a “building–floor–room” hierarchical structure, utilizing oblique photogrammetry and laser scanning technology. This study combined the detection results with real-world 3D data for visualization, providing a new perspective for the 3D spatiotemporal refinement of complex high-rise buildings, and providing a reference framework for the detection and analysis of other types of building environments.

Quantitative assessment of PM2.5-related human health impacts at the provincial level in China and analysis of its heterogeneity affected by economic structural transformation

Rapid economic development has led to massive fossil energy consumption and emissions of air pollutants such as PM2.5, which have severely impacted human health and the environment. By uncovering the primary regions and pivotal sectors of PM2.5-related human health impacts (PM2.5-HHI) and evaluating the influence of economic structural factors on them, we can facilitate a more targeted strategy for managing PM2.5 pollution sources. This study employs a structural decomposition analysis method based on input–output analysis to evaluate the impact of China’s provincial economic structural transformation and changes in final demand on PM2.5-HHI in the years 2012, 2015, and 2017. Results indicated that PM2.5-HHI is primarily concentrated in economically developed provinces (e.g., Shandong and Guangdong), which is compared to Shanghai, Heilongjiang, Liaoning, and Hebei experienced negative growth in PM2.5-HHI during 2007–2017. The production-based PM2.5-HHI is primarily driven by energy-intensive sectors such as the production and distribution of electric power and heat power. By contrast, the building sector is key to driving consumption-based PM2.5-HHI. An increasing number of regions are reducing PM2.5-HHI by implementing production structure changes. Moreover, the driving effect of production structure changes on PM2.5-HHI growth is strengthening in Beijing and Tianjin. Changes in the final demand structure mainly led to the growth of PM2.5-HHI in areas with higher economic development levels, such as Beijing and Shandong, but this driving effect is weakening. The final demand–driven PM2.5-HHI shows an evolutionary trend of an increasing share driven by fixed capital formation and exports and a decreasing share driven by household consumption. Changes in emission intensity play a key role in decreasing PM2.5-HHI in each region. Alternatively, changes in the structure of emission sources have a relatively minor impact on PM2.5-HHI. To mitigate PM2.5-HHI, regional economic and resource endowment advantages should be used to promote regional coordinated development and strengthen green production-process innovation in energy-intensive industries. Meanwhcile, it is necessary to optimize urban construction planning and improve the energy efficiency of buildings.

City energy planning: Modeling long-term strategies under system uncertainties

Cities are facing the dilemma of fulfilling energy service demands for their rapidly growing population while also working towards climate mitigation efforts. City energy planning is essential to deal with this dilemma. Thus, this study explores the role of city energy plans on future cost-efficient energy systems. A technology-rich cost-optimization model was developed using TIMES with intra-sectoral and inter-sectoral interactions and applied to the Gothenburg energy system. The model outcomes are investigated with the application of policy-driven scenarios and further tested under system uncertainties and price sensitivities identified using a participatory approach. Heating sector developments include DH production based on biomass CHP and heat pumps combined with improved building energy efficiency. Cost-efficient transport sector developments show a rapid deployment of biofuel vehicles, followed by increasing electrification later in the time horizon. With the applied uncertainties, different cost-optimal district heat production mixes and heating supply solutions are observed, providing alternative planning strategies. The model results also signify competition of biomass and electricity among sectors in their development pathways. The modeling outcomes enable decision-makers to assess various energy systems transition strategies from a long-term perspective.

City Logistics Solutions for CO2 Emission Reduction and Energy Efficiency: A Comparative Study of Vitoria-Gasteiz, Tartu, and Sønderborg

Modern cities face the challenge of reconciling dynamic development with environmental protection, as well as ensuring a high quality of life for their residents. In a time of continuous urbanization, with more than half of the world’s population living in cities, city logistics plays a crucial role in managing complex urban environments. As part of the concept of sustainable development, city logistics aims to minimize the negative impact of transport on the environment, while increasing operational efficiency and improving the comfort of life of residents in urban agglomerations. This article presents the results of a comparative analysis of the intensity of sustainable urban logistics development in selected cities of the European Union. It examines various strategies that reduce CO2 emissions, improve air quality, and increase building energy efficiency. The analysis demonstrates that cities investing in sustainable urban logistics not only improve their environmental performance but also increase their attractiveness and competitiveness as desirable places to live and work.

Synthesis of highly transparent and fast-responding photochromic coating by template method with space-limited domains

The persistent global high-temperature weather warns us that improving energy efficiency and reducing CO2 emissions are urgent. Controlling the solar energy input in buildings using photochromic smart coating can effectively reduce the energy consumption required for cooling. However, it remains a challenge to achieve high transparency and fast photoresponse in organic–inorganic hybrid photochromic smart coating. Herein, we utilize space-limited domains to design a template polymer that facilitates the formation of amorphous WO3 nanodots, thereby enhancing the polymer network structure through hydrogen bonding and dipole–dipole interactions, which endow the smart coating with excellent adhesion strength up to 13 MPa. Notably, the photochromic smart coating with amorphous WO3 nanodots exhibits higher transparency (96.8 %) than conventional glass (91 %), and a wide visible light modulation range of 96.8 %-4.8 %. Based on this, the cooling efficacy of the smart photochromic coating was assessed within a model residence under actual environmental conditions. The inner temperature of the model house was reduced by up to 13 °C, which decreased indoor heat absorption by 25.8 %, significantly improving energy efficiency. This work provides an important and attractive strategy for achieving energy-efficient building materials and addressing global climate warming.

Multi-pronged strategies for enhancing building envelopes toward nearly-zero energy in hot climatic regions

Abstract Growing concerns about climate change and rising energy demands necessitate advancements in building energy efficiency. This study investigates the effectiveness of radiative coatings and thermal insulation, both individually and combined, in reducing energy consumption and carbon footprint for buildings in hot and humid climates. This research contributes to a growing body of knowledge by comprehensively evaluating the combined effects of these strategies. A comparative analysis was conducted using data on energy usage and carbon emissions.
The research highlights the effectiveness of envelope-enhancing techniques in reducing energy consumption and carbon emissions. The application of radiative coating led to a significant 13.1% decrease in energy usage, totaling 681.95 MWh, and corresponding emissions of 482.14 tons of CO2. Radiative coating offers the most cost-effective solution with an LCOS of $0.045/kWh. When integrating thermal insulation with radiative coating, there was a substantial 12.0% reduction in energy consumption, amounting to 690.39 MWh, and emissions of 488.11 tons of CO2. The integrated model provides significant energy savings at a slightly higher LCOS of $0.052/kWh, making it a balanced choice between efficiency and cost-effectiveness compared to using thermal insulation alone. Moreover, the study emphasizes that the combination of Glazing Integrated Photovoltaic (GIPV) with radiative coating can lead to the creation of nearly zero-energy buildings, resulting in a significant energy savings of 34.9%. These results underscore the efficacy of these technologies in achieving significant energy savings and environmental benefits.
This study demonstrates that radiative coatings significantly reduce energy consumption and carbon footprints. The combined method with thermal insulation reduces energy, suggesting further optimization strategies in hot and humid conditions. The results of this investigation recommend utilizing Glazing Integrated Photovoltaic (GIPV) to achieve nearly zero-energy buildings. Such integrated solutions not only improve energy efficiency but also make a substantial contribution to environmental sustainability in the building sector.

A data-driven framework for occupancy optimisation strategies towards energy demand reduction in non-domestic buildings

Proactive strategies for data-driven operational schedules based on monitored occupancy patters can enable energy demand reduction and optimal resource utilisation. A replicable framework that enables strategic closure of specific thermal zones is introduced and design and operational considerations are discussed. The potential of the framework is illustrated through a case study building, where up to 6% annual energy savings were estimated, highlighting the effectiveness of zone closures. Further findings indicated that total energy savings from the simultaneous closure of multiple zones were marginally larger compared to savings from closing off zones individually, depending on zone capacity and use. Therefore, balanced considerations should take place prior to selecting which zones to close off, also taking into account user acceptability, capacity of systems and controls as well as the internal layout of the building. The research enhances the understanding of the relationship between occupancy and energy demand, while offering recommendations for more energy efficient and sustainable building design and operations that require minimal capital cost. Practical Application This paper provides design and operational considerations based on a robust and replicable framework for energy savings by optimising operational building schedules based on monitored occupancy patterns. This allows a proactive implementation of data-driven strategies that maximise resource utilisation, such as closure of specific zones during periods of low occupancy, without requiring any physical intervention. The findings on a case study building demonstrate that annual energy savings can be achieved, underscoring the potential for substantial cost reductions and improved energy efficiency. Applications can also extend to optimising energy demand for energy flexibility.