Search Results (1,005)

Under China’s dual-carbon strategic goals, large-scale public centralized heating plays a critical role in energy conservation. However, traditional manual open-loop control suffers from high response latency. Furthermore, existing unidirectional predictive methods lack dynamic feedback correction mechanisms. To address these issues, this study proposes an intelligent dual-system collaborative control architecture specifically designed for public heating systems. This architecture utilizes a cloud–edge device framework. It establishes a two-way linkage between heating equipment and the cloud decision platform. Consequently, it constructs an integrated regulation framework encompassing forward decision generation, reverse state verification, and dynamic feedback correction. Specifically, the forward module utilizes a Mamba-structured state-space model to generate data-driven boiler operation strategies. Meanwhile, the reverse module employs a Temporal Convolutional Network with Monte Carlo Dropout (TCN-MC Dropout). This probabilistic network enables state inversion evaluation with reliable uncertainty prediction intervals. These two modules are deeply coupled through an adaptive feedback correction mechanism. Together, they significantly improve system stability and operational robustness under complex thermal disturbances. Specifically, the proposed architecture achieves a room temperature compliance rate exceeding 96% and restricts temperature fluctuations to within $\pm 0.75 °\mathrm{C}$. Simultaneously, it reduces boiler energy consumption by over 16.4%. This solution has been successfully deployed in the heating network at Shaanxi Normal University as a representative real-world case study. Ultimately, it provides a practical technical reference for the intelligent upgrading and low-carbon transformation of public centralized heating systems. Full article

This article presents the results of a research study aimed at designing and evaluating sustainable housing (SH) in post-disaster contexts in Juchitán de Zaragoza, Oaxaca, Mexico, following the 2017 earthquakes. The proposed model integrates four fundamental dimensions: (i) housing design using traditional architecture; (ii) analysis of the environmental impact of materials using indicators of CO₂ emissions, energy consumption, and thermal performance; (iii) comparative evaluation of construction costs relative to conventional housing (CH); and (iv) implementation of a Social Production of Habitat (SPH) approach. A mixed-methods design was adopted, involving literature review, data collection through an environmental and hygrothermal impact study, comparative cost estimates, and semi-structured interviews. The findings show that the use of local, low-impact materials, together with participatory processes, not only reduces costs and environmental externalities but also strengthens the social fabric and contributes to a more equitable reconstruction that is contextualized within traditional architecture. The housing model developed may serve as a reference for post-disaster reconstruction strategies in other contexts, provided it is adapted to local climatic, cultural, material, and regulatory conditions. Full article

This paper presents a novel fault-tolerant model predictive control (FTMPC) framework for fuel cell hybrid electric vehicles (FCHEVs) used for postal delivery applications. The main contribution of the proposed FTMPC is the adaptive adjustment of the model predictive control cost function weights based on fault severity. The proposed reformulation incorporates fault characterization across the diverse degradation mechanisms while maintaining reliable vehicle operation. The FTMPC approach dynamically adapts cost function weights and system constraints based on the fault severity index. The resulting control strategy provides fault-aware power allocation between the fuel cell and battery while accounting for the specified operating and safety constraints. To isolate the contribution of the proposed health-dependent adaptation mechanism, a controlled ablation study was performed against a structurally identical fixed-MPC controller under the same vehicle model, driving cycle, initial conditions, prediction and control horizons, solver configuration, and fault scenarios. The adaptive FTMPC achieved a 10.6956% reduction in direct hydrogen consumption relative to the fixed-MPC baseline. Because differences in terminal battery state of charge (SoC) can influence comparisons based solely on hydrogen consumption, a charge-corrected hydrogen-equivalent metric was also evaluated; using this more conservative metric, the adaptive FTMPC retained a 2.7276% improvement. The final quadratic programming implementation achieved a 100% successful optimization rate in the validation run with no fallback-controller activation, while the maximum soft-constraint slack remained on the order of 10−9. Additional sensitivity analyses were conducted to evaluate the influence of relevant vehicle and operating conditions on energy consumption and battery utilization. These results provide direct quantitative evidence of the contribution of the proposed fault-adaptive mechanism and demonstrate its numerical feasibility for FCHEV energy management, while the limitations of the present simulation-based validation are explicitly acknowledged. Full article

The increasing demand for sustainable outdoor lighting has accelerated the development of solar-powered lighting systems. However, conventional solar lamps typically employ fixed illumination levels and simple day–night switching mechanisms, resulting in inefficient battery utilization and limited adaptability to changing environmental conditions. This study proposes a TRIZ-guided intelligent solar-powered lighting system that integrates photovoltaic energy harvesting, adaptive pulse-width modulation (PWM)-based illumination control, ultrasonic sensing, wireless communication, and embedded control into a unified standalone platform. The TRIZ contradiction matrix was employed during the conceptual design stage to systematically resolve key engineering contradictions involving illumination performance, energy efficiency, hardware complexity, battery lifetime, and user convenience. The proposed prototype was developed using an AT89S51 microcontroller to coordinate battery charging protection, environmental sensing, adaptive brightness regulation, and manual wireless operation. Experimental validation demonstrated stable photovoltaic charging with a regulated battery charging voltage of 14.4 V, reliable execution of embedded control functions, seamless transition between manual and autonomous operating modes, and adaptive LED brightness regulation according to real-time environmental conditions. The integrated PWM control strategy reduced unnecessary energy consumption by dynamically adjusting illumination intensity based on object detection rather than maintaining constant full-power operation. The experimental results further verified the feasibility of combining software-driven adaptive control with renewable energy harvesting to achieve intelligent energy management without increasing hardware complexity. Overall, the proposed system demonstrates that the integration of TRIZ-based systematic innovation with embedded intelligent control provides a practical, energy-efficient, and cost-effective solution for autonomous outdoor lighting. The proposed architecture offers valuable engineering insights for future smart lighting applications in off-grid infrastructure, sustainable communities, and smart city environments. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems account for 60–70% of residential electricity consumption in Oman, where extreme desert climate, with temperatures regularly exceeding 45 °C create substantial cooling demands. Unlike general reviews of smart HVAC controls, this study specifically evaluates the applicability and performance of advanced control strategies for Omani residential buildings operating under extreme heat conditions, synthesizing evidence from international research, the Gulf Cooperation Council (GCC) region, and Oman. Based on a systematic review of peer-reviewed literature published between 2015 and 2025, this analysis examines Model Predictive Control (MPC), Deep Reinforcement Learning (DRL), Fuzzy Logic Control, and Internet of Things-based integrated approaches. International studies demonstrate that MPC strategies achieve energy savings of 16–40% compared to conventional thermostatic control by utilizing dynamic building thermal models to optimize control sequences over finite prediction horizons. DRL-based controllers achieve energy reductions of 17–23% through adaptive learning of optimal policies without requiring explicit system models, offering adaptability to dynamic occupancy patterns. Real-world implementation case studies from Oman and the GCC region—including the GUtech EcoHaus net-zero energy building and national-scale retrofit programs—demonstrate realized energy savings ranging from 25–75%, with higher savings achieved through comprehensive interventions that combine advanced controls with high-performance building envelopes. These findings suggest that substantial potential for reducing residential energy consumption while maintaining occupant thermal comfort under Oman’s extreme climatic conditions is achieved through the integration of advanced HVAC control strategies with high-performance building envelopes. Future research may address the development of occupant-centric adaptive comfort models calibrated for extreme heat conditions and context-specific control strategies that account for regional occupancy patterns and cultural preferences. Full article

Farmland in the hilly and mountainous regions of southern China is characterized by complex terrain and highly variable operating loads. Conventional diesel-powered tracked harvesters are constrained by high crop losses, excessive impurity rates, frequent blockages, and low overall energy-use efficiency. Distributed electric drive provides a promising solution; however, threshing cylinder blockage, high-frequency load transients, and slope operation make it difficult for conventional energy-management strategies to simultaneously ensure dynamic responses, fuel economy, and battery state of charge (SOC) stability. This study therefore proposes a deep deterministic policy gradient (DDPG)-based reinforcement learning energy-management strategy (RL-EMS) for a range-extended, distributed-drive hybrid tracked combine harvester. First, a full-vehicle dynamic model incorporating eight electric-drive units and strong electromechanical coupling is established. Second, power allocation is formulated as a Markov decision process (MDP), with a multi-objective reward function that accounts for fuel consumption, SOC tracking, and boundary violations; the load-rate-of-change is introduced as a feedforward state. Finally, a supervisory physical layer comprising feasible power projection, safety filtering, and rate limiting is inserted between the policy network output and the physical plant so that the executed command satisfies range extender power, battery SOC, current, and power-slew constraints. Under the standard 1000 s cycle, SOC-corrected energy-equivalent comparison shows that the RL-EMS reduces fuel consumption by 1.5% relative to the adaptive equivalent consumption minimization strategy (A-ECMS) and by 26.6% relative to the constant-torque energy-management strategy (CT-EMS). Under an unseen complex random cycle, the RL-EMS reduces fuel consumption by 5.1% relative to A-ECMS. It also suppresses DC-bus voltage sag during a threshing cylinder blockage transient, demonstrating favorable electromechanical transient response. The proposed method provides a modeling and control reference for the intelligent energy management of range-extended, distributed-drive agricultural machinery. Full article

Post-disaster reconstruction of educational facilities is frequently driven by heuristic decision-making that prioritises speed over long-term sustainability, resilience or climate compatibility. To address this gap, this study proposes a BIM-LCA decision-support framework for the evaluation and prioritisation of school retrofit strategies in post-disaster contexts. The framework integrates a BIM-derived building energy model with life cycle assessment based on EN 15978-compliant material take-offs and explicitly accounts for future climate projections. A two-storey school building in Damascus, Syria, classified under the Köppen-Geiger hot semi-arid climate zone, serves as the case study. Three retrofit scenarios are systematically evaluated against the status quo, namely shallow retrofit (external painting and shading), advanced retrofit (compliant with Passivhaus EnerPHit hot-climate standards) and deep retrofit (EnerPHit with photovoltaic integration). Simulations conducted in DesignBuilder v7 (DesignBuilder Software Ltd., Stroud, UK) assess three performance dimensions including operational and embodied energy, operational and embodied carbon footprint, and financial metrics including Net Present Value (NPV) and Marginal Abatement Cost (MAC). Future climate conditions for horizons 2030, 2050, and 2080 are generated using Meteonorm v8 (Meteotest AG, Bern, Switzerland) software under Representative Concentration Pathways RCP 2.6, RCP 4.5, and RCP 8.5. Results under the deep retrofit, on-site photovoltaic generation delivers net-positive energy performance, with an annual surplus of 27.15 MWh and net-negative operational carbon of −14,428 kgCO₂e. The advanced retrofit realises a 32% decline in operational energy consumption at a MAC of £0.89/kgCO₂e, rendering it the most favourable financial strategy under stable inflation-adjusted energy prices. Sensitivity analysis shows this ranking inverts towards the deep retrofit under sustained energy-price growth, and towards the shallow retrofit under a high cost of capital. Under RCP8.5 by 2080, cooling demand rises by up to 82% in the advanced and deep retrofits relative to their respective present-day values. The shallow retrofit records the lowest cooling demand among the retrofit options but remains approximately 9% above the contemporaneous status quo. These findings underscore the necessity of climate-adaptive, scenario-aware decision frameworks for post-disaster reconstruction, moving beyond static energy optimisation toward long-term resilience planning. Full article

Buildings account for a substantial share of global energy use and carbon emissions, making climate-adaptive building envelopes critical for sustainable urban development. Thermochromic glazing dynamically modulates solar transmittance with temperature, offering a promising pathway toward sustainable buildings; however, guidance on transition temperatures and composite configurations across China’s five climate zones remains limited. This study therefore derives an evidence-based, climate-adaptive design strategy for thermochromic hydrogel-based insulating glass units in office buildings across these climate zones. To achieve this aim, an EnergyPlus model validated against summer field measurements in Chongqing quantified heating and cooling loads for 220 simulated cases and identified suitable glazing configurations and transition temperature, while RF-SHAP ranked factor importance. The results provide climate- and orientation-specific guidance for office-glazing selection: a 25 °C transition temperature delivers 2.76–4.04% total load savings in the two warm zones, and surface-2/3 Low-E composite TSG achieves up to 12.93%. In cold and severe cold zones, south-facing use can raise loads, whereas west-, east-, and north-facing applications retain saving potential; in temperate climates, single-silver Low-E glass is generally preferable. The RF-SHAP analysis ranks the factors influencing the total energy saving rate as glazing configuration > climate zone > orientation > transition temperature. These findings translate into a practical climate zone-based selection framework that supports sustainable building envelope design and retrofitting, helping to reduce operational energy consumption and carbon emissions while maintaining indoor thermal comfort. Full article

The increasing integration of distributed energy resources and flexible loads has transformed university campuses into complex energy systems that require coordinated operational strategies capable of managing renewable uncertainty while maintaining economic and environmental performance. This paper proposes a two-stage stochastic mixed-integer linear programming (MILP) framework for the optimal day-ahead energy management of a smart university campus. The proposed model jointly coordinates photovoltaic generation, battery energy storage systems, electric vehicle charging, HVAC operation, and demand response under uncertainties associated with solar generation, electricity demand, energy prices, and ambient temperature. Unlike previous campus energy management approaches, the proposed framework explicitly distinguishes first-stage scheduling decisions from second-stage recourse actions, enabling adaptive operation while preserving decision consistency across uncertainty scenarios. A realistic case study based on the operational characteristics of the Universidad Politécnica Salesiana campus in Ecuador is used to evaluate the proposed methodology. The results demonstrate that the coordinated stochastic scheduling strategy reduces daily operating costs by 36.37%, decreases CO₂ emissions by 42.81%, and lowers peak grid demand by 37.99% compared with conventional operation. In addition, photovoltaic self-consumption reaches 91.7%, while renewable energy utilization increases to 93.4% without compromising occupant thermal comfort. The proposed framework provides a scalable pathway toward low-carbon, resilient, and energy-efficient smart campus operation. Full article

To prevent DC-bus voltage sag and overvoltage during power-supply takeover after a public-grid outage in an industrial microgrid, this study proposes a continuous power-supply control strategy for critical loads that combines dynamic source-side-deficit feedforward with DC-bus energy compensation. Direct feedforward of real-time load power can overlap with residual injection from the grid-side converter, repeatedly charging the DC-bus capacitor and causing an overvoltage. Based on the three-port power-balance relationship among the grid-side interface, bidirectional DC–DC converter, and load-side grid-forming interface, the handover transient is attributed to residual source-side supply, delayed DC–DC power buildup, and persistent load-side consumption. The source-side power deficit, defined as the difference between the load-side DC-power demand and actual source-side injected power, is filtered to generate a dynamic feedforward signal. Thus, storage power is established adaptively as source-side supply withdraws, avoiding repeated compensation. An energy-compensation branch derived from the relation between DC-bus capacitor energy and squared bus voltage corrects residual mismatch caused by filtering, converter losses, and DC–DC dynamic lag. Mathematical models and a small-signal three-port DC-bus model are developed. Simulation and hardware-in-the-loop results verify the effectiveness of the proposed strategy. Full article

Achieving the Sustainable Development Goals (SDGs) requires a transition from conventional fossil-fuel-powered vehicles to alternative energy sources, such as electricity. However, reliably predicting energy consumption under highly variable real-world driving conditions and across different temporal horizons remains a critical challenge regarding the widespread adoption of Electric Vehicles (EVs), directly impacting the precision of range estimation, route planning, and charging strategies. To address this, a novel approach is proposed, combining advanced machine learning models—such as XGBoost, Random Forest, and regression-based techniques—with innovative dataset manipulation using statistical methods. The methodology integrates feature engineering to incorporate vehicle-specific metrics, including driving patterns and environmental conditions, ensuring that models dynamically adapt to real-world scenarios. The proposed framework demonstrates high accuracy and robustness in predicting energy consumption, providing valuable insights for sustainable transportation and efficient energy management toward SDG achievement. Full article

The increased deployment of electric vehicles (EVs) has increased the demand for intelligent energy management strategies to ensure reliable and efficient operation during long-distance travel. Existing EV energy management methods are mainly concerned with energy optimization of the batteries and charging scheduling; however, they often assume reliable charging infrastructure and fixed travel routes. In real-world environments, charging stations can be congested, unavailable, or out of service, and traffic incidents and road closures can affect route feasibility and energy usage. These uncertainties may lead to energy depletion (vehicle stranding) and may compromise successful trip completion. Therefore, this paper proposes a resilience-oriented predictive energy management and route-adaptation framework for long-distance EVs operating under uncertainties in charging infrastructure and the transportation network. The proposed unified decision-making framework incorporates the ability to predict the battery state of charge, estimate the energy requirement of an EV, and determine the availability of charging stations, traffic conditions, disruptions in the road network, and regenerative energy recovery opportunities. A safety-constrained predictive controller is developed to dynamically manage energy consumption while maintaining a minimum energy reserve that guarantees access to feasible charging alternatives under adverse operating conditions. Further, real-time route adaptation is achieved via continuous monitoring of charging station conditions, projected wait times, and road network conditions to determine the most energy-efficient and resilient routes. To assess operational robustness, a resilience index is introduced to quantify the vehicle’s capability to complete a trip while satisfying energy safety constraints in the presence of infrastructure and traffic disturbances. Monte Carlo simulation results on a representative long-distance corridor demonstrate that the proposed framework achieves a 99.3% mission success rate versus 67.3% for a naive shortest-distance baseline, while reducing generalized operating costs by 21.5% and charging stops by 42% relative to a conservative full-charge baseline. These results are simulation-based case study outcomes for the representative corridor and disturbance model studied here and are not measured or expected real-world performance. The proposed approach provides a practical pathway toward resilient, intelligent, and reliable next-generation electric mobility systems. Full article

Cabin temperature prediction is a key technique for improving occupant thermal comfort and reducing energy consumption of thermal management systems. Most existing cabin temperature prediction models do not integrate external meteorological information, resulting in poor adaptability to complex and variable environmental conditions. To address this issue, this paper proposes a multi-source data fusion prediction framework combined with regional meteorological information to achieve accurate cabin temperature prediction under real-world operating scenarios. Shapley additive explanations (SHAP) reveal key feature contributions and interaction mechanisms for dynamic cabin temperature prediction and improve the interpretability of the data-driven model. The precise prediction results quantify the changing patterns of cabin temperature rise and passive cooling. Based on these findings, adaptive strategies for low-temperature preheating and shutting down parking air conditioners early are designed to balance energy efficiency and occupant thermal comfort. Experimental results show that under snowy conditions, compared with the baseline model, the proposed framework reduces mean absolute errors (MAEs) by 13.05%, while the MAE decreases by 22.8% under sunny conditions. The introduction of meteorological data reduces the MAE of the XGBoost and GRU models by 14.9% and 23.28%, respectively. SHAP analysis further uncovered the feature interaction rules governing cabin temperature evolution. In addition, the proposed air-conditioning optimization strategy achieves significant energy savings without compromising occupant thermal comfort. Specifically, the steady-state energy consumption during sunny-morning preheating is reduced by 21.5%, and the pre-shutoff optimization yields energy-saving benefits ranging from 4.9% to 25.5% under typical winter conditions. This study provides theoretical support and serves as an engineering reference for the optimal design of intelligent vehicle thermal management systems. Full article

The coupled effects of power distribution and stack temperature strongly influence hydrogen economy, durability, and operating stability in proton exchange membrane fuel cell (PEMFC) vehicles. This study proposes an integrated energy–thermal management strategy based on the multi-agent deep deterministic policy gradient (MADDPG) algorithm for a PEMFC hybrid bus. The energy management agent regulates PEMFC power using vehicle demand, battery state of charge, and stack temperature, while the thermal management agent controls coolant and air mass flow rates using temperature errors and commanded PEMFC power. Under the unseen CHTC-C cycle, MADDPG reduces equivalent hydrogen consumption by 0.49% and 1.79% compared with SAC and DDPG, respectively, while remaining 2.92% above the offline dynamic programming benchmark. Under an independent real-world bus cycle, MADDPG reduces the maximum stack outlet temperature deviation by 98.56% and 98.07%relative to SAC and MPC, respectively. Additional tests under ambient temperature and aging variations show bounded thermal responses without retraining, and HIL experiments confirm real-time execution at a 1 s control period. Overall, the proposed strategy improves energy economy, thermal regulation, adaptability, and real-time applicability through coordinated power–temperature information exchange. Full article

Fuel cell electric vehicles (FCEVs) require energy management strategies that can balance hydrogen economy, battery utilization, component protection, and real-time control under varying driving conditions. This paper proposes an Adaptive-Modality Deep Deterministic Policy Gradient and Model Predictive Control hierarchical energy management strategy (AMDDPG–MPC HEMS). In the proposed architecture, the upper-level AMDDPG controller identifies driving-condition patterns and generates adaptive weights for hydrogen consumption, battery power, and state-of-charge regulation, while the lower-level MPC controller performs constrained power allocation between the fuel cell and battery. To improve adaptability, the AMDDPG algorithm incorporates an adaptive modality perception mechanism that extracts driving-condition features and a multi-scale reward mechanism that coordinates short-term energy-saving objectives with long-term component-protection requirements. A dedicated weight-scheduling and switching mechanism is also introduced to ensure smooth transitions between operating conditions. The proposed strategy is evaluated under the World Light Vehicle Test Cycle and Urban Dynamometer Driving Schedule and compared with rule-based, equivalent consumption minimization, and fixed-weight MPC strategies. The results show that the AMDDPG–MPC HEMS achieves the lowest equivalent hydrogen consumption, with reductions of 18.853% and 11.732% relative to the rule-based strategy under the two driving cycles, respectively. It also improves fuel-cell operating efficiency and maintains feasible battery SOC regulation. These results demonstrate the effectiveness and engineering potential of the proposed hierarchical energy management strategy. Full article