Search Results (416)

Electrochromic devices (ECDs) in smart buildings have been advanced as a potential solution for improving energy savings and visual and thermal comfort. The current paper is a review of advanced control strategies for ECDs with respect to predictive, environmental, and adaptive strategies for improving building performance. One of the most frequently employed methods is rule-based control (RBC). RBC is being complemented by more sophisticated model predictive control (MPC) and machine learning (ML) procedures. By adjusting ECD behaviour in dynamic response to changing external circumstances, like daylight, glare, temperature, and solar radiation, these improved techniques ensure a major improvement in real-time adaptivity, energy savings, and occupant comfort. The paper systematically examines ECD control techniques available in the literature, detailing performance indicators, energy conservation, and comfort enhancement for various climatic conditions. It also examines hybrid techniques based on MPC and ML models that tackle the obstacles faced by conventional control systems. Furthermore, their compatibility with renewable energy sources such as PV and thermochromic systems is outlined in relation to net-zero energy buildings. The paper ends with a review of future directions that could lead towards standardization in the form of models, sensor networks and AI-based adaptive frameworks to increase the scale as well as the real-world relevance of ECDs in varying building contexts. Full article

Educational buildings account for approximately 17% of the building sector’s energy consumption, making them critical for zero-emission building (ZEB) strategies. With students spending over 50% of their time indoors, life-cycle carbon assessments and targeted retrofitting are essential for realizing UN sustainability goals on campuses. This study develops a comprehensive life-cycle carbon assessment framework using a Calibrated Building Energy Simulation to simultaneously evaluate embodied and operational carbon emissions in campus facilities. This research evaluates two distinct university buildings for analysis: a historical 1970 educational building in Cosenza, Italy (Mediterranean climate) and a modern 2009 building in Ankara, Türkiye (semi-arid/continental climate). To minimize the total carbon footprint, seven distinct retrofitting scenarios are systematically simulated and compared: adding photovoltaic (PV) panels, integrating solar films on windows, applying internal and external insulations, implementing green wall applications, applying psychological-adaptive HVAC control and decreasing the heating set-point temperature. Results indicate that psychological-adaptive HVAC control is the most effective, achieving approximately 19.3% operational carbon savings across both cases with a low embodied carbon penalty. Conversely, the green wall application was the least effective, with a carbon payback period of 53.55 years. Ultimately, this study provides actionable engineering pathways for transforming campus buildings into net-zero emission educational facilities. Full article

The building sector is one of the largest contributors to global energy demand and carbon emissions, making the transition to net-zero buildings (NZBs) a critical component of climate change mitigation strategies. However, the persistent building energy performance gap (BEPG), defined as the discrepancy between predicted and actual energy consumption, continues to hinder the achievement of net-zero operational performance. Accordingly, this review examines the role of artificial intelligence (AI) in enabling NZBs through the integration of energy-efficient building systems, renewable energy technologies, and intelligent operational control. A comprehensive review of the literature published between 2018 and 2025 was conducted, focusing on three complementary domains: heating, ventilation, and air conditioning (HVAC) system efficiency as the demand-side pillar, renewable energy integration as the supply-side pillar, and AI as the enabling layer connecting both domains. Synthesis of the reviewed literature reveals that demand-side HVAC technologies achieve energy savings ranging from 20% to 67%, while supply-side renewable energy integration increases photovoltaic (PV) self-consumption by 11–13%. Furthermore, AI-driven optimization, particularly through reinforcement learning (22.3% ± 8.4% energy savings) and digital twins (up to 70% renewable energy utilization), substantially enhances building performance within integrated energy management frameworks. The reviewed studies further demonstrate that AI techniques, including machine learning, deep learning, reinforcement learning, and digital twins, enable accurate energy forecasting (R² > 0.90), intelligent operational control, and effective coordination of integrated PV–battery energy storage system–electric vehicle systems, improving building energy flexibility and reducing grid fluctuations by up to 12.78%. Despite these advances, challenges related to data quality, interoperability, model explainability, cybersecurity, and limited large-scale real-world validation remain significant barriers to widespread adoption. Overall, the evidence indicates that AI serves as a key enabler for reducing the BEPG and improving the reliability, resilience, and operational efficiency of NZBs, thereby supporting the transition toward intelligent, low-carbon built environments. Full article

The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment. Full article

Autoclaved Aerated Concrete (AAC) is a lightweight construction material with strong relevance for energy-efficient and modular building systems, but its production remains constrained by steam-curing energy demand and carbon-intensive binders. This challenge is increasingly important as the AAC sector targets net-zero pathways and as cement and lime remain major contributors to life-cycle emissions in AAC products. This study develops an optimization framework for sustainable AAC production that leverages machine-learning surrogates for discrete-event simulations. We first construct a discrete-event factory model that represents mix preparation, mould pouring and rising, cutting, autoclaving, unloading, and product handling. We then couple the simulation to a ${\mathrm{CO}}_2$e and cost model and generate 116,640 production scenarios. Machine-learning surrogate models are trained to predict total ${\mathrm{CO}}_2$e emissions, cost, and production time, and a gradient-based optimization procedure is used to identify operating strategies under different carbon, cost, time, and balanced priorities. The results show that, autoclaving time, electricity carbon intensity, and cement use are the two most important environmental levers. The carbon–cost and carbon-priority strategies produced the lowest predicted emissions, approximately 1292 kg CO₂e, and selected the lowest electricity emission factor and cement mass considered in the design space, 0.05 kg CO₂e/kWh and 400 kg, respectively. The time-priority strategy produced the shortest predicted production time but the highest predicted emissions and cost, demonstrating a clear carbon–cost–time trade-off under the model assumptions. The proposed framework provides a practical decision-support tool for AAC manufacturers to compare production strategies, quantify trade-offs, and identify lower-carbon operating regimes before implementation. Full article

The transition toward a net-zero economy requires buildings to evolve from passive consumers into Grid-Interactive Efficient Buildings (GEBs). Traditional Building Energy Management Systems (BEMSs) lack the dynamic intelligence needed to control stochastic energy flows and solve multi-objective optimization problems. To systematically map this technological shift, this study conducts a Systematic Literature Review (SLR) following PRISMA guidelines, analyzing a curated corpus of 144 studies (135 primary technical papers and 9 review articles). Due to the significant diversity in methodological approaches within cyber-physical testbeds and IoT architectures discovered through the literature review process, a qualitative narrative and architectural synthesis was conducted rather than a quantitative meta-analysis. Based on this framework, this review examines emerging paradigms for Cognitive Buildings based on Artificial Intelligence of Things (AIoT), edge computing, and semantic interoperability. This review discusses the evolution of algorithms from predictive Deep Learning (DL) and Deep Reinforcement Learning (DRL) to newer approaches such as Agentic AI and Physics-Informed Neural Networks (PINNs). These new methods address the fundamental “sim-to-real” gap while ensuring thermodynamic consistency and safety in physical actuation. It also presents strategic applications in multi-objective optimization of HVAC systems, demand response, energy arbitrage, and predictive maintenance. Moreover, this review tackles major real-world deployment issues by introducing Federated Learning for data privacy, Transfer Learning for portfolio scaling, and TinyML for overcoming the computational carbon paradox of “Green AI.” By quantifying this paradox, the review contrasts the massive computational carbon footprint of cloud-based model training against the milliwatt-class efficiency of localized edge deployments. Overall, this review outlines potential research directions toward the development of autonomous Cognitive Digital Twins (CDTs) and Human-Centric Personal Comfort Models (PCMs). Full article

Restaurants combine long opening hours, catering demand, kitchen ventilation, DHW, and mixed-fuel cooking loads, making their decarbonisation different from generic commercial retrofit. For small- and medium-sized enterprise (SME) hospitality premises, this makes the transition to net-zero operation a distinct sustainability challenge because a large, process-driven share of demand lies outside conventional building-fabric and building-services retrofit. This single-case study develops a whole-building utility-calibrated OpenStudio/EnergyPlus model for Beit El Zaytoun, a 655.82 m² restaurant in Park Royal, London. Monthly electricity and gas data for June 2024–May 2025 were used to calibrate the baseline at whole-building level. Standalone and cumulative scenarios tested insulation, low-emissivity double glazing, LED lighting and controls, ASHP service scenarios, and an 11 kWp PV array. Baseline demand was 413,895 kWh/yr, equivalent to 631.1 kWh/m²·yr and 75,020 kgCO₂e/yr. The lowest-net-energy analytical package reduced net imported energy to 314,734 kWh/yr and operational carbon to 56,700 kgCO₂e/yr, a retained 24.0% reduction on the source reporting basis; this package is treated as an analytical bound rather than as a final design recommendation because it excludes cooling. The model-derived residual process load, kitchen and catering gas plus kitchen, and back-of-house electricity remained 233,920 kWh/yr across building-focused scenarios. The Residual-Load Index (RLI) rose from 0.57 to 0.74; with ±15% process-load allocation uncertainty, the optimised RLI range was 0.63–0.85, so the post-retrofit balance remained process-load dominated. The case demonstrates a practical decarbonisation ceiling likely to recur in similar high-process-load hospitality premises: fabric, lighting, heat electrification, and PV are necessary but insufficient without catering-equipment, cooking-fuel, kitchen-ventilation, refrigeration-control, sub-metering, and demand-response strategies. The paper contributes whole-building utility-calibrated quantitative evidence and a transferable RLI metric for sub-sector-specific sustainable retrofit policy, and the net-zero transition of SME food-service premises. Full article

Traditional air conditioning consumes substantial electricity, exacerbates the urban heat island effect, and creates a maladaptive feedback loop, necessitating a shift toward passive-first net-zero pathways. This study takes a typical six-story residential building in Nanjing’s hot summer and cold winter climate zone as a case study. Using EnergyPlus hourly simulations, three progressive passive strategy packages are designed to quantify the impact of building envelope heat exchange on cooling loads, grid stress, and heat resilience. Package A includes external shading and natural ventilation. Package B adds reflective coating and a green roof. Package C further adds night ventilation precooling and high-performance windows. The results show that Package C achieves a 62.5% reduction in peak cooling load and a 63.0% reduction in seasonal cooling load. Daytime peak inward heat gain decreases from 68 W/m² to 22 W/m², while nighttime outward heat dissipation increases from 12 W/m² to 38 W/m². Under an extreme heat day of 41.2 °C with no active cooling, indoor peak temperature drops from 36.8 °C to 29.4 °C, and heat risk hours decrease by 73.6%. Peak-hour power demand is reduced by 70.4%, with a systemic leverage factor of 1.08. Innovations include achieving over 60% load reduction using only mature passive strategies, introducing the systemic leverage factor to quantify urban heat island mitigation benefits, and establishing a vulnerability-to-resilience transformation framework. The passive-first pathway validates building envelope as the first line of defense for net-zero futures. However, the findings are based on a typical six-story residential building in Nanjing and require validation through field measurements or broader application across different climate zones and building typologies before generalization. Full article

Buildings account for 40% of global energy consumption and one-third of greenhouse gas emissions. Renewable energy systems (RESs), such as solar photovoltaic (PV) and geothermal heat pumps, are critical technological solutions for decarbonization. Despite the growing literature, existing reviews lack a comprehensive synthesis integrating machine learning (ML), Internet of Things (IoT), and Building Information Modeling (BIM). Following the PRISMA protocol, this paper presents a systematic review of 41 studies published between 2012 and 2025. The review evaluates four primary domains: RES performance, building energy prediction, HVAC optimization, and occupancy-aware management. Quantitative findings reveal that solar PV-integrated buildings achieve electricity cost reductions of 35–64%, while ML-enhanced energy prediction models attain accuracies up to R² = 0.989. Critical research gaps are identified, including the scarcity of real-time sensor integration and geographically inclusive multi-climate datasets. Ultimately, this review contributes a structured synthesis of effective technologies, a comparative analysis of methodological approaches (ML, simulation, hybrid), and actionable future directions. It provides practical guidance for researchers and policymakers toward achieving net-zero energy buildings. This study serves as a definitive reference for the development of sustainable, low-energy built environments. Full article

Buildings are the primary energy consumption layer of Renewable Energy Communities (RECs) and a key target for net-zero policy under the EPBD recast. This scoping review applies the PRISMA-ScR framework to map Digital Twin (DT) architectures for building-scale and community-scale energy management in REC configurations. A Scopus search yielded a final analytical corpus of 102 studies, coded through an eight-dimensional thematic matrix covering lifecycle phases, digitalization objectives, enabling technologies, DT capability dimensions, and data realism. DT is the dominant enabling technology (55.9%), followed by IoT (23.5%) and machine learning (22.5%). Research is concentrated in the Planning and Design phase (77.5%) and markedly underrepresented in Implementation and Commissioning (16.7%). Notably, only 10.8% of studies integrate real-time operational data, exposing a significant gap between simulation-based research and the deployment conditions required under current EPBD mandates. The evidence base supports building energy monitoring, demand forecasting, and flexible grid operation but remains limited for retrofit verification, standardized net-zero KPIs, and operational workflows in existing stock. Critical DT capability gaps persist in Data Services (7.8%) and User Experience (18.6%). Overall, DT architectures show genuine potential for grid-interactive, net-zero building management, yet the field presents unresolved structural challenges for large-scale real-world deployment. Full article

Wind turbine blade (WTB) end-of-life waste is projected to increase significantly, yet no sustainable recycling solution with a clear energy, economic, and environmental (3E) assessment exists. This paper presents a validated 3E model of a WTB thermal recycling pilot (1 t/day) to benchmark recycled glass fibre (rGF) against virgin glass fibre (vGF) and identifies the throughput at which rGF becomes competitive. This subsequently leads to a projection of 3E performance at 5000 t/y plant capacity, at which rGF achieves approximately 46% lower specific primary thermal energy, 92% of the CO₂ emissions of vGF, and a selling price of 80% of vGF for a financial break-even. Building on this baseline, a novel combined material, heat, and power system is proposed and simulated, integrating the WTB recycling pilot with a 20 kWₑₗ/130 kWₜₕ organic Rankine cycle to serve residential buildings. Results show that coupling the pilot with 3000 m² of apartments yields a near net-zero CO₂ and energy-cost residential complex, with overall CO₂ emissions falling below those of standalone residential buildings combined with vGF production when more than 25 apartments are integrated. Full article

Achieving deep decarbonization of the residential building sector is essential for meeting Canada’s climate commitments and Net Zero targets. However, large-scale residential retrofit planning is often constrained by the time, cost, and expertise required for detailed building energy modelling. This study evaluates the applicability of a typology-based retrofit planning tool developed by Homes to Zero (HTZ) as a simplified alternative to conventional simulation-based analysis. Two representative Canadian residential archetypes—a detached bungalow and a two-storey semi-detached home located in Toronto—were analyzed using both the HTZ platform and detailed hourly energy simulations conducted in eQuest (DOE-2.2 engine). Baseline energy consumption and greenhouse gas (GHG) emissions were first compared across the two modelling approaches. Results show strong agreement for the bungalow case, with differences of less than 1% for electricity and natural gas consumption and approximately 4% for total emissions. For the two-storey dwelling, baseline electricity estimates were identical while natural gas consumption differed by approximately 17%, highlighting the sensitivity of physics-based simulations to envelope and operational assumptions. Retrofit scenarios were then compared using single-measure GHG reductions derived from HTZ and incremental simulation results from eQuest. While both tools identified electrification through air-source heat pumps as the dominant emission-reduction strategy, differences were observed in the magnitude of savings for envelope upgrades and secondary measures. The HTZ platform also provides approximate retrofit cost estimates, enabling order-of-magnitude budgeting, whereas eQuest requires separate costing analysis. This study is framed as a screening-level benchmark rather than a full validation exercise, highlighting the trade-off between scalability and modelling fidelity in residential retrofit planning. The results suggest that typology-based tools can provide credible screening-level guidance for residential retrofit planning and large-scale policy analysis, while detailed simulation remains valuable for evaluating integrated retrofit packages and design-level decisions. Full article

The built environment in Saudi Arabia accounts for approximately 78% of the country’s total electricity consumption, positioning building energy performance as one of the most consequential levers available to policymakers pursuing the kingdom’s net-zero greenhouse gas emissions target for 2060 and Vision 2030’s sustainability agenda. Despite the progressive introduction of the Saudi Building Code (SBC) energy chapters SBC 601, SBC 602, and the Saudi Green Building Code (SgBC 1001), a persistent gap remains between regulatory intent and measurable outcomes across Saudi Arabia’s five distinct climatic zones. Building codes are, by design, generic policy instruments encompassing structural, fire, accessibility, and energy provisions; this paper focuses specifically on the energy and sustainability dimensions and critically examines how the SBC’s update cycle and prescriptive compliance architecture shape actual performance outcomes. This study presents three explicit research questions: (RQ1) What zone-differentiated energy savings does SBC implementation deliver across residential typologies? (RQ2) How does the Mostadam national rating system compare with international benchmarks in the Saudi context, and what caveats govern that comparison? (RQ3) What evidence-based policy interventions are needed to transition from compliance-led to performance-led building energy governance? Drawing on a systematic synthesis of 53 building energy simulation models (2018–2025), official programme data, and a structured comparative analysis of Mostadam against LEED v4.1 and BREEAM, the study finds EUI reductions of 5–25% from SBC compliance, with the largest savings in the hot–humid coastal zone. Seven prioritised policy recommendations are proposed, addressing code revision, financial incentives, digital monitoring, renewable energy thresholds, and capacity building. Full article

Improving window performance is a key strategy for reducing heating energy demand in residential buildings; however, the economic feasibility of upgrading to higher performance grades remains uncertain, particularly for single-detached houses. This study quantitatively evaluates the energy-saving performance and economic feasibility of window performance grade improvements in single-detached houses in South Korea. Heating energy demand was estimated using ECO2-OD (Energy Conservation Optimization Tool for One-zone Dwelling), the national standard simulation tool adopted for the Building Energy Efficiency Certification (BEEC) and Zero Energy Building (ZEB) certification systems. Representative residential prototypes constructed in 1980, 1987, and 2001 were analyzed to reflect differences in envelope performance associated with construction vintage. Window upgrades from the baseline grade to Grades 3, 2, and 1 using polyvinyl chloride (PVC) windows were simulated under consistent building geometry and operating conditions. Heating energy demand reductions were converted into annual energy cost savings using market-based natural gas prices. Economic feasibility was assessed using annual indicator-based metrics—unit cost of energy saving (UCES), payback period (PBP), and return on investment (ROI)—as well as discounted cash flow-based life-cycle metrics, including net present value (NPV) and discounted payback period (DPB). The results show that heating energy demand decreases consistently with improved window performance across all construction years; however, marginal energy savings diminish at higher performance grades, while investment costs increase. Upgrading from the baseline to intermediate performance grades yields the most favorable economic outcomes, particularly for houses constructed in 2001, whereas upgrades to the highest performance grade often fail to achieve economic feasibility when time value is considered. These findings indicate that uniform application of the highest-grade windows may not be economically optimal. Unlike previous studies that mainly focused on thermal performance or case-specific retrofit outcomes, this study compares multiple window performance grades across construction-year-specific single-detached house prototypes under a unified economic evaluation framework. This study highlights the importance of construction year-specific and performance-tiered retrofit strategies and provides quantitative evidence to support cost-effective window retrofit policies for single-detached residential buildings. Full article

This paper presents a comprehensive review of artificial intelligence (AI) applications in photovoltaic-integrated buildings, focusing on energy forecasting, advanced control strategies, and pathways toward net-zero energy performance. Net-zero energy buildings are defined as systems that balance annual energy consumption with on-site renewable generation, requiring efficient and adaptive energy management. The review analyzes state-of-the-art AI-based forecasting methods for photovoltaic power generation and building energy demand, demonstrating the superior performance of machine learning and deep learning models in capturing nonlinear and time-dependent patterns. In parallel, advanced control strategies, including model predictive control (MPC), reinforcement learning (RL), and hybrid approaches, are evaluated in terms of their performance, limitations, and practical applicability. The results show that accurate forecasting alone is insufficient, and its integration with control strategies is essential for optimal system operation. Hybrid approaches combining model-based and data-driven methods emerge as the most effective solution for complex and dynamic environments. The role of real-time energy management systems in enabling adaptive and coordinated operation is also highlighted. Finally, key challenges related to data quality, model interpretability, and system integration are identified, along with future research directions. Overall, AI-driven energy management systems have strong potential to transform photovoltaic-integrated buildings into intelligent, flexible, and sustainable energy systems. Full article