Search Results (594)

With the promotion of national fitness, the requirements for regulating indoor environments during non-competition periods are low and relatively flexible under the trend of composite sports buildings. To maximize the use of natural ventilation and lighting for energy savings, passive optimization design based on building ontology has emerged as an effective strategy. This paper focuses on the spatial prototype of large- and medium-sized gymnasiums, optimizing key geometric design parameters and envelope structure parameters that influence energy consumption. This optimization employs a combination of orthogonal experiments and performance simulations. This study identifies the degree to which each factor affects energy consumption in the competition hall and determines the optimal low-energy consumption gymnasium prototype. The results reveal that the skylight area ratio is the most significant factor impacting the energy consumption of large- and medium-sized gymnasiums. The optimized gymnasium prototype reduced energy consumption by 5.3%~50.9% compared to all experimental combinations. This study provides valuable references and insights for architects during the initial stages of designing sports buildings to achieve low energy consumption. Full article

Lightweight steel-framing (LSF) systems have become increasingly prominent in modern construction due to their structural efficiency, design flexibility, and sustainability. However, traditional facade materials such as stone are often cost-prohibitive, and brick veneers—despite their popularity—pose seismic performance concerns. This study introduces an innovative porcelain sheathing system for cold-formed steel (CFS) shear walls. Porcelain has no veins thus it offers integrated and reliable strength unlike granite. Four full-scale CFS shear walls incorporating screwed porcelain sheathing (SPS) were tested under combined cyclic lateral and constant gravity loading. The experimental program investigated key seismic characteristics, including lateral stiffness and strength, deformation capacity, failure modes, and energy dissipation, to calculate the system response modification factor (R). The test results showed that configurations with horizontal sheathing, double mid-studs, and three blocking rows improved performance, achieving up to 21.1 kN lateral resistance and 2.5% drift capacity. The average R-factor was 4.2, which exceeds the current design code values (AISI S213: R = 3; AS/NZS 4600: R = 2), suggesting the enhanced seismic resilience of the SPS-CFS system. This study also proposes design improvements to reduce the risk of brittle failure and enhance inelastic behavior. In addition, the results inform discussions on permissible building heights and contribute to the advancement of CFS design codes for seismic regions. Full article

An efficient integrated energy system (IES) can enhance the potential of building energy conservation and carbon mitigation. However, imbalances between user-side demand and supply side output present formidable challenges to the operational dispatch of building energy systems. To mitigate heat rejection and improve dispatch optimization, an integrated building energy system incorporating waste heat recovery via an absorption heat pump based on the flow temperature model is adopted. A comprehensive analysis was conducted to investigate the correlation among heat pump operational strategies, thermal comfort, and the dynamic thermal storage capacity of piping network systems. The optimization calculations and comparative analyses were conducted across five cases on typical season days via the CPLEX solver with MATLAB R2018a. The simulation results indicate that the operational modes of absorption heat pump reduced the costs by 4.4–8.5%, while the absorption rate of waste heat increased from 37.02% to 51.46%. Additionally, the utilization ratio of battery and thermal storage units decreased by up to 69.82% at most after considering the pipeline thermal inertia and thermal comfort, thus increasing the system’s energy-saving ability and reducing the pressure of energy storage equipment, ultimately increasing the scheduling flexibility of the integrated building energy system. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination $R^2$. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an $R^2$ of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article

Pilot production is a critical transitional phase in the process of new product development or manufacturing, aiming at ensuring that products are thoroughly validated and optimized before entering full-scale production. During this stage, a key challenge is how to leverage limited resources to build data infrastructure and conduct data analysis to establish and verify quality control. This paper presents the implementation of a cyber–physical system (CPS) for a lithium battery pilot assembly line. A machine learning-based predictive model was employed to establish quality control mechanisms. Process knowledge-guided data analysis was utilized to build a quality prediction model based on the collected battery data. The model-centric concept of ‘virtual quality’ enables early quality judgment during production, which allows for flexible quality control and the determination of optimal process parameters, thereby reducing production costs and minimizing energy consumption during manufacturing. Full article

Hydrogen is being increasingly integrated into the international trade system as a clean and flexible energy carrier, motivated by the global energy transition and carbon neutrality objectives. The rapid expansion of the global hydrogen trade network has simultaneously exposed several sustainability challenges, including a centralized structure, overdependence on key countries, and limited resilience to external disruptions. Based on this, we develop a risk propagation model that incorporates the absorption capacity of nodes to simulate the propagation of supply shortage risks within the global hydrogen trade network. Furthermore, we propose a composite sustainability index constructed from structural, economic, and environmental resilience indicators, enabling a systematic assessment of the network’s sustainable development capacity under external shock scenarios. Findings indicate the following: (1) The global hydrogen trade network is undergoing a structural shift from a Western Europe-dominated unipolar configuration to a more polycentric pattern. Countries such as China and Singapore are emerging as key hubs linking Eurasian regions, with trade relationships among nations becoming increasingly dense and diversified. (2) Although supply shortage shocks trigger structural disturbances, economic losses, and risks of carbon rebound, their impacts are largely concentrated in a limited number of hub countries, with relatively limited disruption to the overall sustainability of the system. (3) Countries exhibit significant heterogeneity in structural, economic, and environmental resilience. Risk propagation demonstrates an uneven pattern characterized by hub-induced disruptions, chain-like transmission, and localized clustering. Accordingly, policy recommendations are proposed, including the establishment of a polycentric coordination mechanism, the enhancement of regional emergency coordination mechanisms, and the advancement of differentiated capacity-building efforts. Full article

The low-carbon transition of the global energy system is an urgent necessity to address climate change and meet growing energy demand. As a major source of energy consumption and emissions, buildings play a key role in this transition. This study systematically analyzes the flexible resources of building energy systems and vehicle-to-grid (V2G) interaction technologies, and mainly focuses on the regulation characteristics and coordination mechanisms of distributed energy supply (renewable energy and multi-energy cogeneration), energy storage (electric/thermal/cooling), and flexible loads (air conditioning and electric vehicles) within regional energy systems. The study reveals that distributed renewable energy and multi-energy cogeneration technologies form an integrated architecture through a complementary “output fluctuation mitigation–cascade energy supply” mechanism, enabling the coordinated optimization of building energy efficiency and grid regulation. Electricity and thermal energy storage serve as dual pillars of flexibility along the “fast response–economic storage” dimension. Air conditioning loads and electric vehicles (EVs) complement each other via thermodynamic regulation and Vehicle-to-Everything (V2X) technologies, constructing a dual-dimensional regulation mode in terms of both power and time. Ultimately, a dynamic balance system integrating sources, loads, and storage is established, driven by the spatiotemporal complementarity of multi-energy flows. This paper proposes an innovative framework that optimizes energy consumption and enhances grid stability by coordinating distributed renewable energy, energy storage, and flexible loads across multiple time scales. This approach offers a new perspective for achieving sustainable and flexible building energy systems. In addition, this paper explores the application of demand response policies in building energy systems, analyzing the role of policy incentives and market mechanisms in promoting building energy flexibility. Full article

Under the guidance of carbon peaking and carbon neutrality goals, the power industry is transitioning toward environmentally friendly practices. With the increasing integration of intermittent renewable energy sources (RES) and the enhanced self-regulation capabilities of grids, traditional distribution networks (DNs) are transitioning into active distribution networks (ADNs). To fully exploit the synergistic optimization potential of the “source-grid-load-storage” system in electricity-carbon coupling scenarios, leverage user-side flexibility resources, and facilitate low-carbon DN development, this paper proposes a low-carbon optimal scheduling strategy for ADN incorporating demand response (DR) priority. Building upon a bi-directional feedback mechanism between carbon potential and load, a two-layer distributed robust scheduling model for DN is introduced, which is solved through hierarchical iteration using column and constraint generation (C&CG) algorithm. Case study demonstrates that the model proposed in this paper can effectively measure the priority of demand response for different loads. Under the proposed strategy, the photovoltaic (PV) consumption rate reaches 99.76%. Demand response costs were reduced by 6.57%, and system carbon emissions were further reduced by 8.93%. While accounting for PV uncertainty, it balances the economic efficiency and robustness of DN, thereby effectively improving system operational safety and reliability, and promoting the smooth evolution of DN toward a low-carbon and efficient operational mode. Full article

The photovoltaic, energy storage, direct current, and flexibility (PEDF) system represents a crucial innovation for transforming buildings into low-carbon energy sources. Although it is still in the early stages of scalable demonstration, current research and practice related to PEDF lack comprehensive studies on optimizing and evaluating system capacity configuration across various scenarios. Capacity configuration and energy scheduling are crucial components that are often treated separately, leading to a missing opportunity to leverage the synergy among key interactive devices. To address this issue, this paper proposes an optimization and evaluation framework for the PEDF system that employs a dual-layer model for planning and operating. This framework precisely configures the PEDF topology, load, photovoltaic, energy storage, and critical interactive devices, while integrating economic, environmental, and reliability objectives. The effectiveness of the proposed model has been validated in optimizing capacity configurations for newly built office buildings and existing commercial settings. The results indicate that for new office buildings, schemes that prioritize low-carbon initiatives are more effective than those that focus on reliability and economy. In existing commercial buildings, reliability-focused schemes outperform those that prioritize economy and low carbon, and all three are significantly better than pre-configuration schemes. The proposed framework enhances the theoretical understanding of PEDF system planning and evaluation, thereby promoting broader adoption of sustainable energy technologies. Full article

In the context of the intensifying global climate crisis, the power industry, as a significant carbon emitter, urgently needs to promote low-carbon transformation using market mechanisms. In this paper, a multi-objective stochastic optimization scheduling framework for regional power grids integrating carbon trading (CET) and green certificate trading (GCT) is proposed to coordinate the conflict between economic benefits and environmental objectives. By building a deterministic optimization model, the goal of maximizing power generation profit and minimizing carbon emissions is combined in a weighted form, and the power balance, carbon quota constraint, and the proportion of renewable energy are introduced. To deal with the uncertainty of power demand, carbon baseline, and the green certificate ratio, Monte Carlo simulation was further used to generate random parameter scenarios, and the CPLEX solver was used to optimize scheduling schemes iteratively. The simulation results show that when the proportion of green certificates increases from 0.35 to 0.45, the proportion of renewable energy generation increases by 4%, the output of coal power decreases by 12–15%, and the carbon emission decreases by 3–4.5%. At the same time, the tightening of carbon quotas (coefficient increased from 0.78 to 0.84) promoted the output of gas units to increase by 70 MWh, verifying the synergistic emission reduction effect of the “total control + market incentive” policy. Economic–environmental tradeoff analysis shows that high-cost inputs are positively correlated with the proportion of renewable energy, and carbon emissions are significantly negatively correlated with the proportion of green certificates (correlation coefficient −0.79). This study emphasizes that dynamic adjustments of carbon quota and green certificate targets can avoid diminishing marginal emission reduction efficiency, while the independent carbon price mechanism needs to enhance its linkage with economic targets through policy design. This framework provides theoretical support and a practical path for decision-makers to design a flexible market mechanism and build a multi-energy complementary system of “coal power base load protection, gas peak regulation, and renewable energy supplement”. Full article

This article analyzes the revitalization process of the Kraków Fortress in the context of the amendment to the Revitalization Act of 29 July 2024, focusing on the legal, financial, social, and environmental effects of these changes. The aim of the work is to assess how the new regulations have affected the effectiveness of the revitalization of historic military facilities and the financial and participatory mechanisms that have enabled their effective implementation. The authors adopted an interdisciplinary approach, combining legal, urban, conservation, and social analysis, and applied the case study method of five forts: 52 “Borek”, 52a “Jugowice”, 2 “Kościuszko”, 49 “Krzesławice”, and 31 “Św. Benedict”. The selection of cases was based on different stages of implementation, financing models, and social functions. The research showed that the amendment to the Act accelerated decision-making processes and enabled more flexible management of space and better acquisition of financial resources, including from the EU and SKOZK. The use of a mixed financing model (local, European, private funds) and strong social participation contributed to the durability and acceptance of the projects. The effects of revitalization include, among others, an increase in the number of visitors (from 20,000 to 75,000 per year), the creation of approx. 120 jobs, and a reduction of energy consumption by over 30%. Revitalized facilities today perform cultural, educational, and recreational functions, supporting social integration and the development of a local identity. The article indicates that the Kraków model can be a model for other cities with military heritage. It also draws attention to the need to develop nationwide standards for the adaptation of historic buildings and recommends further research on the socio-economic durability of revitalization projects. Full article

Simulation-based development is a structured approach that uses formal models to design and test system behavior before building the actual system. The Internet of Things (IoT) connects physical devices equipped with sensors and software to collect and exchange data. Cyber-Physical Systems (CPSs) integrate computing directly into physical processes to enable real-time control. This paper reviews the Discrete-Event System Specification (DEVS) formalism and explores how it can serve as a unified framework for designing, simulating, and implementing systems that combine IoT and CPS—referred to as the Internet of Cyber-Things (IoCT). Through case studies that include home automation, solar energy monitoring, conflict management, and swarm robotics, the paper reviews how DEVS enables construction of modular, scalable, and reusable models. The role of the System Entity Structure (SES) is also discussed, highlighting its contribution in organizing models and generating alternative system configurations. With this background as basis, the paper evaluates whether DEVS provides the necessary modeling power and continuity across stages to support the development of complex IoCT systems. The paper concludes that DEVS offers a robust and flexible foundation for developing IoCT systems, supporting both expressiveness and seamless transition from design to real-world deployment. Full article

Improving the energy efficiency of buildings is a critical pathway in mitigating greenhouse gas emissions and fostering sustainable urban development. This study introduces a simulation-based multi-objective optimization framework designed to enhance both the thermal and economic performance of residential buildings. A representative single-family dwelling located in Patras, Greece, served as a case study to demonstrate the application and scalability of the proposed methodology. The optimization simultaneously minimized two conflicting objectives: the building’s annual thermal energy demand and the cost of construction materials. The computational process was implemented using MATLAB’s Multi-Objective Genetic Algorithm, supported by a modular Excel interface that enables the dynamic customization of design parameters and climatic inputs. A parametric analysis across four optimization scenarios was conducted by systematically varying the key algorithmic hyperparameters—population size, mutation rate, and number of generations—to assess their impact on convergence behavior, Pareto front resolution, and solution diversity. The results confirmed the algorithm’s robustness in producing technically feasible and non-dominated solutions, while also highlighting the sensitivity of optimization outcomes to hyperparameter tuning. The proposed framework is a flexible, reproducible, and computationally tractable approach to supporting early-stage, performance-driven building design under realistic constraints. Full article

With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km². The results show that the method has a high accuracy for the identification of the roof area, with a maximum maxF_β of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. Full article