Search Results (5,959)

Aware of the nefarious effects of excessive exploitation of natural resources and the greenhouse gases emissions linked to building sector, the concept of smart buildings emerged, referring to a building that uses clean energy efficiently. This requires intelligent control systems to manage the use of residential energy consuming devices, namely the HVAC (Heating, Ventilation, Air-conditioning) system. This system consumes up to 50% of the total energy used by a building. In this paper, we introduce a RL (Reinforcement Learning) and MPC-LSTM (Model Predictive Control-Long-Short Term Memory) hybrid control system that combines DNNs (Deep Neural Networks), through RL, with LSTM’s long-short memory technique and MPC’s control characteristics. The goal of our model is to maintain thermal comfort of residents while optimizing energy consumption. Consequently, to train and test our model, we generate our own dataset using a building model of a corporate building in Casablanca, Morocco, combined with weather data of the same city. Simulations confirm the robustness of our model as it outperforms basic control methods in terms of thermal comfort and energy consumption especially during summer. Compared to conventional methods, our approach resulted in a 45.4% and 70.9% reduction in energy consumption, in winter and summer, respectively. Our approach also resulted in 26 less comfort violations during winter. On the other hand, during summer, our approach found a compromise between energy consumption and comfort with no more than 2.5 °C above ideal temperature limit. Full article

Long-term renovation strategies (LTRSs) play a central role in achieving the European Union’s objective of a climate-neutral building stock by 2050. In Poland, the challenge is particularly acute: a majority of the building stock was constructed before 1990 and does not even meet basic thermal performance standards. In view of the state of the buildings in Poland and the assumptions made about obtaining the necessary energy parameters in the coming years, it is necessary to undertake thermal modernization measures. The purpose of the paper is to assess the economic efficiency of the variants of modernization of building stock in Poland, taking into account the constraints related to improving energy efficiency. Additionally, the article also points out the problem of discrepancies resulting from climate zones that may significantly affect the final primary energy results (on average, 5–15%). In order to achieve the objectives, the paper focuses on the analysis of energy sources. According to the overall score in the analytic hierarchy process (AHP) method, the best solutions, with a global priority of 0.46, are renewable energy sources (RESs). The evaluation of selected fuel types in the 2055 perspective, using the technique for order preference by similarity to ideal solution (TOPSIS) method, indicate favorable environmental performance by sources based on electricity, i.e., air-source heat pumps, ground-source heat pumps, and electric heating, which achieved the highest relative closeness to the ideal solution. Heat pump systems can reduce energy consumption by 26–41% depending on the building and heat pump type. The final analysis in the paper concerns different options for thermal modernization of a model single-family house, taking into account different energy sources and stages of thermal modernization work. The scenario involves the simultaneous implementation of all renovation measures at an early stage, resulting in the lowest investment burden over time and the most favorable economic performance. Full article

Progressive collapse of building structures induced by accidental extreme loads has garnered significant attention. This study aimed to assess the impact resistance of steel-framed subassemblies with extended reverse channel connections under falling debris impact. It also sought to provide technical support for anti-collapse design. Drop-hammer impact tests were conducted to obtain baseline data. A validated finite element model using ANSYS/LS-DYNA was employed for the parametric analyses. The key parameters investigated included the impact location (mid-span vs. beam end), falling height of the impactor, and span-to-depth ratio of steel beams, with a focus on the impact resistance. The results reveal that the impact resistance depends on both the peak load capacity and the deformation capacity. The mid-span impacts exhibited higher resistance at falling heights ≥ 1.0 m due to greater plastic deformation. In contrast, the beam-end impacts performed better when the falling heights were ≤0.5 m. The impact resistance decreased with an increasing falling height. The reduction ratios exceeded the theoretical values due to the post-impact gravitational energy input. Smaller SDRs enhanced the peak resistance under both impact scenarios, with more pronounced effects in the mid-span cases. Catenary action significantly improved the mid-span impact resistance (19.3–66.7%). However, it contributed minimally to the beam-end impact resistance (0.61–1.09%), where shear action dominated. These findings offer critical technical support for optimizing steel structure designs to resist falling debris impact and enhance overall structural robustness. Full article

This study investigates the integration of green hydrogen into building energy systems using local solar power, with the electricity grid serving as a backup plan. A comprehensive bottom-up analysis compares six energy system configurations: the natural gas grid boiler system, all-electric heat pump system, natural gas and hydrogen blended system, hydrogen microgrid boiler system, cogeneration hydrogen fuel cell system, and hybrid hydrogen heat pump system. Energy efficiency evaluations were conducted for 25 homes within one block in a neighborhood across five typological house stocks located in Stoke-on-Trent, UK. This research was modeled using a spreadsheet-based approach. The results highlight that while the all-electric heat pump system still demonstrates the highest energy efficiency with the lowest consumption, the hybrid hydrogen heat pump system emerges as the most efficient hydrogen-based solution. Further optimization, through the implementation of a peak-shaving strategy, shows promise in enhancing system performance. In this approach, hybrid hydrogen serves as a heating source during peak demand hours (evenings and cold seasons), complemented by a solar energy powered heat pump during summer and daytime. An hourly operational configuration is recommended to ensure consistent performance and sustainability. This study focuses on energy performance, excluding cost-effectiveness analysis. Therefore, the cost of the energy is not taken into consideration, requiring further development for future research in these areas. Full article

The traditional construction industry significantly contributes to global resource consumption and climate change. Conventional methods limit the development of complex and multifunctional architectural forms. In contrast, 3D concrete printing (3DCP), an additive manufacturing technique, enables the creation of intricate building envelopes that integrate architectural and energy-efficient functions. Bioinspired design, recognized for its sustainability, has gained traction in this context. This study investigates the thermal and energy performance of various bioinspired and regular 3DCP infill patterns compared to conventional concrete building envelopes in tropical climates. A three-stage methodology was employed. First, bioinspired patterns were identified and evaluated through a literature review. Next, prototype models were developed using Rhino and simulated in ANSYS to assess thermal performance. Finally, energy performance was analyzed using Ladybug and Honeybee tools. The results revealed that honeycomb, spiral, spiderweb, and weaving patterns achieved 35–40% higher thermal and energy efficiency than solid concrete, and about 10% more than the 3DCP sawtooth pattern. The findings highlight the potential of bioinspired spiral infill patterns to enhance the sustainability of 3DCP building envelopes. This opens new avenues for integrating biomimicry into 3DCP construction as a tool for performance optimization and environmental impact reduction. Full article

Accurate identification of equivalent thermal parameters (ETPs) is crucial for optimizing energy efficiency in residential buildings during winter electric heating. This study proposes a physics-informed neural network (PINN) approach to estimate ETP model parameters, integrating physical constraints with data-driven learning to enhance robustness. The method is validated using real-world measurements from seven rural residences, with indoor and outdoor temperatures and heating power sampled every 15 min. The PINN is compared with linear regression (LR), heuristic methods (GA, PSO, TROA), and data-driven methods (RF, XGBoost, LSTM). The results show that the PINN reduces MAE by over 90% compared to LR, 42% compared to heuristic methods, and 75% compared to pure data-driven methods, with similar improvements in RMSE and MAPE, while maintaining moderate computational time. This work highlights the potential of PINNs as an efficient and reliable tool for building energy management, offering a promising solution for parameter identification within the specific context of the studied residences, with future work needed to confirm scalability across diverse climates and building types. Full article

Building a causal-loop diagram (CLD) is central to system-dynamics modeling but demands domain insight, the mastery of CLD notation, and the ability to juggle AI, mathematical, and execution tools. Pipeline Algebra (PA) reduces that burden by treating each step—LLM prompting, symbolic or numeric computation, algorithmic transforms, and cloud execution—as a typed, idempotent operator in one algebraic expression. Operators are intrinsically idempotent (implemented through memoization), so every intermediate result is re-used verbatim, yielding bit-level reproducibility even when individual components are stochastic. Unlike DAG (directed acyclic graph) frameworks such as Airflow or Snakemake, which force analysts to wire heterogeneous APIs together with glue code, PA’s compact notation lets them think in the problem space, rather than in workflow plumbing—echoing Iverson’s dictum that “notation is a tool of thought.” We demonstrated PA on a peer-reviewed study of novel-energy commercialization. Starting only from the article’s abstract, an AI-extracted problem statement, and an AI-assisted web search, PA produced an initial CLD. A senior system-dynamics practitioner identified two shortcomings: missing best-practice patterns and lingering dependence on the problem statement. A one-hour rewrite that embedded best-practice rules, used iterative prompting, and removed the problem statement yielded a diagram that conformed to accepted conventions and better captured the system. The results suggest that earlier gaps were implementation artifacts, not flaws in PA’s design; quantitative validation will be the subject of future work. Full article

This study empirically verified the effect of solar radiation on the building envelope, with particular emphasis on the generated surface temperature. A model of a cellular concrete block wall with ETICS (External Thermal Insulation Composite System) was constructed with varying insulation-plaster configurations, followed by tests in a “sun chamber” aging chamber and numerical analyses. The measurement results were compared with those from the numerical simulations, taking into account the thermal properties of the materials used and the radiation exposure conditions. The purpose of the study was to determine to what extent different types of plasters and insulation materials affect the heating of the façades. Computer simulations confirmed the direction of energy flow and the gradual heating of successive layers. Furthermore, the differences between the material variants were consistent with the experimental observations. By modeling perfectly uniform conditions, the numerical analysis allowed us to limit the impact of radiation variability, resulting in results with reduced error. Full article

The lack of modularity in building design information within multi-domain building performance analysis environments impedes efficient multidisciplinary analysis during the building design process. This study proposes a Functional Mock-up Interface (FMI)-based data model to facilitate the translation of building design information into a Building Information Modeling (BIM)-integrated building performance analysis framework that can be seamlessly integrated with object-oriented physical models. The proposed data model employs both FMI and BIM to decouple the design information required for physics-based analysis from existing Building Information Models. It then generates a physical BIM-based Functional Mock-up Unit (PBIM-FMU), which encapsulates the necessary building design information and can operate independently within a multi-domain building performance analysis environment. The PBIM-FMU can be readily interfaced with object-oriented physical modeling (OOPM)-based analysis models, as demonstrated in this study through its integration with an OOPM-based thermal analysis model for estimating annual building energy demand. To validate the proposed framework, simulation results from a manually constructed thermal analysis model were compared with those from a model integrated with the PBIM-FMU. The results were consistent, confirming that the data model supports accurate data exchange between BIM and multi-domain building performance simulation platforms. Full article

Reducing energy consumption in belt conveyor systems is critical to improving the overall energy efficiency of lignite mining operations. This study presents a theoretical and empirical analysis of energy use in overburden and coal conveyors, with a focus on balancing the relationship between belt speed and load. Building on the theory of conveyor motion resistance, the energy consumption index (WskZE)—previously introduced by the authors—is revisited as a function of two key variables: belt speed (v) and real-time material flow rate (Q_r). Empirical validation was conducted using operational data from variable-speed conveyors in the Konin lignite mine and compared to similar-length conveyors in the Bełchatów mine. Energy consumption measurements allowed for the analysis of energy consumption for two different scenarios: (i) in the Bełchatów mine the belt speed was constant and the excavator capacity was variable and (ii) in the Konin mine the excavator capacity was kept constant and the conveyor belt speed was varied. The results confirm that WskZE is linearly dependent on belt speed and inversely proportional to throughput, as predicted by theoretical models. However, findings also show that lowering belt speed—while effective in reducing energy use—results in a higher proportion of power being consumed to move the belt and heavy idlers, especially when these components are sized for peak loads. This study suggests a revised conveyor design philosophy (a new paradigm) that emphasizes maximizing the mass ratio of transported material to moving components. Additionally, it recommends integrating real-time monitoring of energy performance indicators into mine control systems to enable energy-aware operational decisions. Full article

In modern intelligent buildings, the control of Building Energy Systems (BES) faces increasing complexity in balancing energy costs, thermal comfort, and operational flexibility. Traditional centralized or flat deep reinforcement learning (DRL) methods often fail to effectively handle the multi-timescale dynamics, large state–action spaces, and strict constraint satisfaction required for real-world energy systems. To address these challenges, this paper proposes an energy policy-aware layered control architecture that combines Virtual Energy Storage System (VESS) modeling with a novel Dynamic Constraint-Aware Policy Optimization (DCPO) algorithm. The VESS is modeled based on the thermal inertia of building envelope components, quantifying flexibility in terms of virtual power, capacity, and state of charge, thus enabling BES to behave as if it had embedded, non-physical energy storage. Building on this, the BES control problem is structured using a hierarchical Markov Decision Process, in which the upper level handles strategic decisions (e.g., VESS dispatch, HVAC modes), while the lower level manages real-time control (e.g., temperature adjustments, load balancing). The proposed DCPO algorithm extends actor–critic learning by incorporating dynamic policy constraints, entropy regularization, and adaptive clipping to ensure feasible and efficient policy learning under both operational and comfort-related constraints. Simulation experiments demonstrate that the proposed approach outperforms established algorithms like Deep Q-Networks (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3). Specifically, it achieves a 32.6% reduction in operational costs and over a 51% decrease in thermal comfort violations compared to DQN, while ensuring millisecond-level policy generation suitable for real-time BES deployment. Full article

This study investigates the financial and operational feasibility of deploying electric vehicle (EV) charging infrastructure within high-density residential buildings, utilizing empirical operational data combined with comprehensive financial modeling. A 14-day monitoring period conducted at a residential complex comprising 958 units revealed distinct charging behaviors, with demand peaking during weekday evenings between 19:00 and 22:00 and displaying more dispersed yet lower overall utilization during weekends. Energy efficiency emerged as a significant operational constraint, as standby power consumption contributed substantially to total energy losses. Specifically, while total energy consumption reached 248.342 kW, only 138.24 kW were directly delivered to users, underscoring the necessity for energy-efficient hardware and intelligent load management systems to minimize idle consumption. The financial analysis identified pricing as the most critical determinant of project viability. Under current cost structures, financial break-even was attainable only at a profit margin of 0.2286 USD (8 THB) per kWh, while lower margins resulted in persistent financial deficits. Sensitivity analysis further demonstrated the considerable vulnerability of the project’s financial performance to small fluctuations in profit share and utilization rate. A 10% reduction in either parameter entirely eliminated the project’s ability to reach payback, while variations in energy costs, capital expenditures (CAPEX), and operational expenditures (OPEX) exerted comparatively limited influence. These findings emphasize the importance of precise demand forecasting, adaptive pricing strategies, and proactive government intervention to mitigate financial risks associated with residential EV charging deployment. Policy measures such as capital subsidies, technical regulations, and transparent pricing frameworks are essential to incentivize private sector investment and support sustainable expansion of EV infrastructure in residential sectors. Full article

During the continuous operation of trailing suction hopper dredger (TSHD), equipment workload exhibits significant time-varying characteristics. Maintaining dynamic symmetry between power generation and consumption is crucial for ensuring system stability and preventing power supply failures. Key challenges lie in dynamic perception, accurate prediction, and real-time power management to achieve this equilibrium. To address this issue, this paper proposes and constructs a “prediction-driven dynamic power management method.” Firstly, to model the complex temporal dependencies of the workload sequence, we introduce and improve a dilated convolutional long short-term memory network (Dilated-LSTM) to build a workload prediction model with strong long-term dependency awareness. This model significantly improves the accuracy of workload trend prediction. Based on the accurate prediction results, a dynamic power management strategy is developed: when the predicted total power consumption is about to exceed a preset margin threshold, the Power Management System (PMS) automatically triggers power reduction operations for adjusfigure loads, aiming to maintain grid balance without interrupting critical loads. If the power that the generator can produce is still less than the required power after the power is reduced, and there is still a risk of supply-demand imbalance, the system uses an Improved Grey Wolf Optimization (IGWO) algorithm to automatically disconnect some non-critical loads, achieving real-time dynamic symmetry matching of generation capacity and load demand. Experimental results show that this mechanism effectively prevents generator overloads or ship-wide power failures, significantly improving system stability and the reliability of power supply to critical loads. The research results provide effective technical support for intelligent energy efficiency management and safe operation of TSHDs and other vessels with complex working conditions. Full article

Endolysins, phage-derived enzymes capable of lysing bacterial cell walls, hold significant promise as novel antimicrobials against resistant Gram-positive and Gram-negative pathogens. In this study, we undertook an integrative approach combining extensive in silico analyses and experimental validation to characterize the novel endolysin LysPALS22. Initially, sixteen endolysin sequences were selected based on documented lytic activity and enzymatic diversity, and subjected to multiple sequence alignment and phylogenetic analysis, which revealed highly conserved catalytic and binding domains, particularly localized to the N-terminal region, underscoring their functional importance. Building upon these sequence insights, we generated three-dimensional structural models using Swiss-Model, EBI-EMBL, and AlphaFold Colab, where comparative evaluation via Ramachandran plots and ERRAT scores identified the Swiss-Model prediction as the highest quality structure, featuring over 90% residues in favored conformations and superior atomic interaction profiles. Leveraging this validated model, molecular docking studies were conducted in PyRx with AutoDock Vina, performing blind docking of key peptidoglycan-derived ligands such as N-Acetylmuramic Acid-L-Alanine, which exhibited the strongest binding affinity (−7.3 kcal/mol), with stable hydrogen bonding to catalytic residues ASP46 and TYR61, indicating precise substrate recognition. Visualization of docking poses using Discovery Studio further confirmed critical hydrophobic and polar interactions stabilizing ligand binding. Subsequent molecular dynamics simulations validated the stability of the LysPALS22–NAM-LA complex, showing minimal structural fluctuations, persistent hydrogen bonding, and favorable interaction energies throughout the 100 ns trajectory. Parallel to computational analyses, LysPALS22 was heterologously expressed in Escherichia coli (E. coli) and Pichia pastoris (P. pastoris), where SDS-PAGE and bicinchoninic acid assays validated successful protein production; notably, the P. pastoris-expressed enzyme displayed an increased molecular weight (~45 kDa) consistent with glycosylation, and achieved higher volumetric yields (1.56 ± 0.31 mg/mL) compared to E. coli (1.31 ± 0.16 mg/mL), reflecting advantages of yeast expression for large-scale production. Collectively, these findings provide a robust structural and functional foundation for LysPALS22, highlighting its conserved enzymatic features, specific ligand interactions, and successful recombinant expression, thereby setting the stage for future in vivo antimicrobial efficacy studies and rational engineering efforts aimed at combating multidrug-resistant Gram-negative infections. Full article

This article presents a comparative evaluation of three established thermal comfort models (ISSO 74, ASHRAE 55, and EN 16798-1) in the context of residential buildings in Algiers, under current and projected Mediterranean climate conditions. By combining field measurements, occupant interviews, and dynamic simulations in DesignBuilder, this research analyzes thermal comfort responses using the RCP 8.5 climate scenario. The analysis demonstrates that ISSO 74 is more suitable for temperature adaptation, while EN 16798-1 offers better humidity tolerance in high-moisture environments. Results reveal that indoor thermal discomfort currently affects more than one-third of the annual hours, with summer discomfort projected to dominate by 2100. Bedrooms are identified as the most thermally vulnerable spaces during peak summer weeks. The article identifies a critical mismatch between existing comfort standards and local climatic realities, calling for the development of an adaptive thermal comfort model tailored to the socio-economic and hygrothermal characteristics of North African cities. Passive strategies and mixed-mode ventilation are recommended as essential for enhancing climate resilience and reducing energy demand. Full article