Search Results (1,640)

The Indonesian government should seriously consider the use of renewable energy, given the natural potential that can still be utilized as an environmentally friendly power source. The utilization of renewable energy can be achieved by harnessing available natural resources. Pico hydro power plants (PLTPHs) can serve as an alternative electricity generator for use in Indonesia due to the existing natural potential. The output from this power plant can be utilized directly or stored in batteries. Directly measuring the generator’s performance on-site is deemed less effective. Therefore, a monitoring system is introduced as a solution to allow remote monitoring and display parameters such as voltage, current, frequency, and power of the generator online. This system is designed to display the micro hydro generator’s output parameter data on the Blynk application. The display on the Blynk application can be monitored via a connected mobile phone. Testing of the monitoring system was carried out by comparing two sets of measurements: one through the PZEM-004T sensor system and the other through a kWh meter (Kilowatt-hour meter). For the AC output from the battery with a 12-watt lamp load (tested 4 times), the reading error values obtained were a voltage reading error of 0.2%, a current reading error of 19.4%, a frequency reading error of 0.67%, and a power reading error of 18.2%. Full article

In this paper, a supervised deep reinforcement learning (SDRL)-based positioning scheme is proposed for indoor wireless localization. The proposed scheme formulates the positioning problem as a Markov decision process and introduces a target-aware reward design based on the artificial potential field (APF) to alleviate the sparse reward problem commonly encountered in search-based reinforcement learning. In the proposed scheme, supervision is provided at the reward level by incorporating the target position into the reward design, rather than at the action level via expert demonstrations. A multi-scale action set with 49 candidates is further adopted to provide a favorable trade-off between estimation accuracy and search efficiency. An anchor-based environment construction strategy is developed by selecting the four strongest reference points (RPs) and transforming their coordinates with respect to the strongest RP. Simulation results show that the proposed scheme achieves a mean absolute error (MAE) below 0.8 m and success rates above 99.1% within 1 m and 99.2% within 2 m under the default Bluetooth Low Energy setting, while the convex-valid rate of the anchor-based environment exceeds 99.5%. Compared with existing methods, the proposed scheme reduces the MAE by approximately 92.3%. Ablation studies confirm that multi-scale actions reduce the average search steps by approximately 69.5% compared with a single-scale baseline. The proposed scheme also retains stable performance across BLE, Wi-Fi, and Zigbee infrastructures when trained under a representative path-loss setting without retraining and maintains sub-meter accuracy under mild shadow fading. These results confirm that the proposed scheme can improve positioning accuracy and search efficiency for indoor wireless localization. Full article

The article is devoted to developing methodological approaches to multi-criteria resource optimization of technological solutions in Nature Use Projects, considering the growing shortage of water and energy resources, climate change, and post-war transformation of Ukraine’s agricultural sector. The need to transition from traditional technical and economic optimization models to integrated assessment approaches, which consider ecological, resource, and economic aspects of the project implementation effectiveness, is substantiated. The methodological basis of the study is a combination of Multi-Criteria Decision-Making and the Water-Energy-Food Nexus concept, enabling the necessary adaptive management and formalizing the process of project decision-making under multifactor uncertainty. A set of indicators of resource-ecological and economic efficiency is proposed, including indicators of productivity, weather and climate risk, resource use, environmental reliability, investment attractiveness, etc. A key feature of this approach is the transformation of resource-ecological indicators into a value form, ensuring their integration with economic indicators within a single optimization model. Based on a machine experiment for the conditions of the Kherson region, an assessment of the effectiveness of various irrigation regimes, which differ from the project irrigation regime in terms of watering and irrigation norms, in terms of their level of provision with water and energy resources, was carried out. It was determined that, under the studied conditions, in dry years (p = 70%), the permissible deficit threshold is approximately 30%, achieving a compromise between economic efficiency and environmental acceptability. Adaptive management of irrigation regimes has been shown to reduce the resource intensity of production without a significant loss of productivity. This creates a basis for revising outdated design standards, which focused on 100% satisfaction of water needs, in favor of adaptive models that account for the real resource potential of the territory. This approach transforms irrigation from a resource-intensive industry into a tool for sustainable territorial development, where the priority is the efficiency of each cubic meter of water and kilowatt-hour of energy used, rather than gross collection. It has been proven that the implementation of resource optimization as a basic principle of natural resource project management contributes to increasing the efficiency of natural capital use, minimizing ecological risks, and ensuring the sustainable development of the agricultural sector. The obtained results can be used to substantiate engineering solutions in projects for the restoration and modernization of water management and land reclamation systems in Ukraine. Full article

Buildings account for a substantial proportion of global energy consumption and greenhouse-gas emissions, largely due to the widespread use of conventional heating, ventilation, and air-conditioning (HVAC) systems. Hybrid systems that integrate passive and active technologies have emerged as a promising strategy for reducing energy demand while maintaining adequate indoor environmental conditions. This study evaluates the thermal and airflow performance of a hybrid air-conditioning system (HACS) that combines transparent thermal insulation (TTI) filled with R-410A refrigerant and a pig-fat-based organic phase-change material (PCM). Experimental measurements of heat flux, temperature, airflow velocity, and CO₂ concentration were conducted in a controlled prototype system. In parallel, computational simulations were performed using computational fluid dynamics (CFD) and multizone airflow modeling. The hybrid system incorporates a TTI container acting as a solar absorber and a galvanized-steel PCM container filled with 10 kg of pig fat used as latent heat storage. Heat-flux measurements were obtained using an HFS-5 sensor connected to a data acquisition system, while airflow velocity and temperature were monitored with analog data loggers. Indoor CO₂ concentrations were recorded using a dedicated CO₂ meter and simulated using CONTAMW software version 3.4.0.8. The experimental results show that the TTI and PCM containers reached average heat-flux values of 77.04 W/m² and 55.31 W/m², respectively. Airflow within the system is induced by buoyancy forces arising from temperature gradients generated by heat transfer processes at the surfaces of the TTI and PCM, resulting in a mixed air stream with an average temperature of 37.54 °C during winter operation. Recorded CO₂ concentrations remained between 290 and 413 ppm, indicating high indoor air quality levels. The overall experimental campaign spanned 6 years and 3 months. CFD simulations confirmed the airflow patterns and heat-transfer behavior observed experimentally. The findings demonstrate that hybrid air-conditioning systems combining refrigerant-filled transparent insulation with bio-based phase-change materials can effectively enhance passive thermal performance while maintaining acceptable indoor air quality. The integration of photovoltaic-powered ventilation systems could further the operational autonomy and overall energy efficiency of such hybrid systems. Full article

The shift toward more electric aircraft has intensified thermal management challenges due to increased heat load from electrical actuators, power electronics and energy storage systems concentrated within confined fuselage bays. A Conventional Environmental Control System (ECS) alone is not sufficient to dissipate such high localized heat loads. This creates the need for innovative heat dissipation and heat reuse strategies. This paper presents two thermal management concepts evaluated at the Fraunhofer Flight Test Facility. The first, developed in the ORCHESTRA project, integrates a bilge skin heat exchanger with modified ventilation to dissipate elevated heat loads. The second, under investigation in the TheMa4HERA project, focuses on reusing avionics heat to warm the FWD cargo hold, thereby reducing ECS power demand. Both concepts depend on convective heat exchange, characterized using Fraunhofer’s Convective Heat Transfer Meter (CHM) to determine key heat transfer coefficients. In parallel, an aircraft-level thermal model was developed, validated against experimental data and subsequently used for virtual demonstration of a ground test scenario. Full article

Because smart microgrids can flexibly integrate distributed energy resources and support grid-connected and islanded operation modes, they enhance power supply reliability and promote the efficient utilization of renewable energy. However, the open communication environment and physically exposed infrastructure introduce critical security challenges, including risks of physical hijacking and data leakage. Many existing authentication protocols either fail to address these threats or rely on heavyweight cryptographic operations such as bilinear pairings and modular exponentiation, resulting in high computational and communicational overhead. To address these issues, a lightweight authentication and key agreement (AKA) protocol for smart microgrids is proposed. The protocol symmetrically integrates Physical Unclonable Functions (PUFs) into the smart meter (SM) and smart microgrid control center (SMC) to protect stored secret information against capture attacks. Meanwhile, the SM and SMC register with the data center (DC) in a symmetric manner. During the AKA phase, the DC only assists in authenticating the identities of the SM and SMC online in a symmetric way, without participating in session key computation, thereby reducing the trust burden and computational load on the smart meters and control center. Formal security proof and informal security analysis demonstrate that the proposed protocol can resist known attacks such as physical hijacking and data leakage. Compared with existing smart microgrid authentication protocols, the proposed protocol has performance advantages and the lowest computational cost, confirming its suitability for resource-constrained microgrid environments. Full article

The existing studies on Small-Scale Embedded Generation (SSEG) have not addressed the net billing framework behavior that applies to different import and export tariff rates. This paper presents the simulation and modeling of a smart net billing electricity meter for SSEG in MATLAB/Simulink R2018b. The model integrates a PV array, MPPT controller, DC-DC boost converter, three-phase voltage source inverter (VSI), LC filter, synchronous generator, and a bidirectional energy meter. A smart billing subsystem was developed to compute real-time energy costs using differential tariff rates consistent with South African utility policies. Simulations were conducted under fixed irradiance, with electrical performance evaluated over a short interval and billing dynamics assessed over an extended period. Results show stable PV generation, proper inverter synchronization with the utility grid, and accurate tracking of imported and exported energy. The system effectively calculates the net bill, demonstrating transparency, automation, and economic accuracy in line with policy-driven net billing frameworks. These outcomes validate the technical feasibility and practical relevance of smart net billing meters in modern grid-connected renewable energy applications. Full article

This study numerically investigates the aerodynamic and thermal interactions between a full-scale metro train and the surrounding airflow within a confined tunnel environment using steady-state Reynolds-averaged Navier–Stokes (RANS) simulations. The six-car train, with a total length of 108 m and a cross-sectional area of 5.97 m², operates in a tunnel with a 9.83 square meter cross-section, resulting in a high blockage ratio of approximately 60 percent. The Shear Stress Transport (SST) k–ω turbulence model and a high-resolution finite-volume mesh comprising over 8.5 million elements were employed to capture detailed near-wall phenomena. Six representative motion scenarios were analyzed, including early acceleration, peak cruising, and deceleration phases, with realistic thermal boundary conditions applied by assigning the tunnel air temperature as 29.2 °C and the train surface temperature as 35.0 °C. Velocity, pressure, temperature, and turbulence kinetic energy distributions were extracted from both longitudinal and cross-sectional planes. In addition to visual contour assessments, pointwise and spatially averaged field data were examined to quantify the development of airflow structures, pressure distribution, and thermal behavior. The results reveal speed-dependent aerodynamic resistance, pronounced recirculation and stagnation zones around the train nose and tail, and variations in convective heat transfer rates that evolve with train velocity. These findings provide insights into tunnel ventilation design and thermal management for underground metro operations, representing a novel integration of full-scale computational fluid dynamics (CFD) with thermal characterization under realistic conditions. Full article

Effective peak load management enables utilities to mitigate increased electricity demand and optimize the use of available resources during periods of maximum consumption. Accurate forecasting of the peak load is essential for ensuring the reliability, efficiency, and resilience of contemporary power systems. In this study, commercial customer-level data were employed to identify electricity peak demand within the Polish power system, drawing upon historical records of both energy consumption and meteorological variables. Departing from conventional time series forecasting approaches, the problem was intentionally reformulated as a pattern recognition task. Three classification techniques were systematically evaluated to identify individual customers’ peak load events, thereby offering a basis for demand-side management strategies and incentive mechanisms aimed at flattening load profiles and improving grid stability. The proposed approach demonstrates how data-driven analytics can support utilities in extracting actionable knowledge from large-scale energy datasets and enabling proactive demand response programs. Empirical results indicate that the proposed methods are capable of predicting up to 90% of electricity peak occurrences, with a forecasting horizon of 24 h leading to significant shifts in the load curve. Full article

The rapid proliferation of artificial intelligence (AI) has pushed hyperscale data center rack densities beyond 100 kW, driving facility power requirements to the gigawatt scale. As developers attempt to deploy these massive Zettascale compute loads across US wholesale electricity markets, they encounter severe transmission planning bottlenecks, multi-year interconnection delays, and escalating grid transient stability risks. This paper presents a generalizable techno-economic framework for evaluating grid-tied, behind-the-meter (BTM) energy architectures as a means of bypassing these constraints. The framework is demonstrated through a detailed case study in the Electric Reliability Council of Texas (ERCOT), selected for its rapid data center growth and evolving large-load regulatory environment. Using a scenario-based comparative approach, this study models the feasibility of transitioning from pure-grid reliance to hybrid, on-site generation across a three-phase deployment pathway scaling from 25 MW to 250 MW. Six distinct microgrid configurations are evaluated, integrating baseload technologies—including Enhanced Geothermal Systems (EGSs), Small Modular Reactors (SMRs), and Reciprocating Internal Combustion Engines (RICEs)—with a tiered-performance Battery Energy Storage System (BESS) combining high C-rate lithium-ion units and repurposed electric vehicle batteries. System viability is assessed through two primary metrics: the Levelized Cost of Energy (LCOE) and the Avoided Loss of Load Probability (ALOLP). The results indicate that the blended LCOE scenario ranges from $64.50/MWh (Geothermal + Solar PPA) to $94.20/MWh (SMR-anchored), compared to a $75.00/MWh pure-grid baseline. The 100% Geothermal configuration achieves a scenario-dependent ALOLP exceeding 99.9%, while gas-dependent configurations range from 58.0% to 91.2%. These findings suggest that geographic siting co-optimized with localized generation offers a viable pathway for balancing regulatory compliance, capital cost, and Uptime Tier IV operational resilience in early-stage data center development across constrained grid environments. Full article

The growing need to enhance observability in distribution networks has driven the development of load pseudomeasurement generation methods, particularly under partial smart meter (SM) penetration. This paper proposes a load pseudomeasurement framework that builds representative daily load profiles (load curves) from hourly SM time series using clustering techniques, with and without weather information. Markov chain models are then used to capture day-to-day dynamics by predicting the most likely next-day profile to be assigned to customers without SM. To enable this transfer, a hierarchical grouping scheme based on monthly energy consumption is introduced to map behaviors from SM-equipped customers to customers without SM measurement. The methodology is validated with real residential data from the Low-Carbon London project under multiple observability scenarios including different SM availability levels, where SM measurements are withheld from the inputs to emulate customers without SM measurement, and the resulting pseudomeasurements are benchmarked against the original measurements. The results show that the Euclidean representative curve method achieved the most robust overall performance, with a minimum MAE of 1.65 in the Reduced × 75% SM configuration. The best-performing configuration depended on the observability level: Reduced was the most robust option under medium-to-high observability, whereas Temp_reduced with a 21-day window performed best under the lowest-observability condition. In addition, the Euclidean method showed low practical deviation in the Reduced × 25% SM case, with a bias of 0.63 and Cohen’s d = 0.27. Overall, the proposed approach accurately reproduces the hourly load shape and captures inter-day variability under partial observability conditions. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

Smart metering of high-time-resolution energy data enables efficient power grid management. However, it also raises significant privacy concerns by revealing users’ consumption patterns. In this paper, a novel privacy-preserving idea is introduced by utilizing a rechargeable battery (RB) to reshape the smart meter readings to statistically align with random target readings, which are preset independently of the private user energy consumption data. For the long-term privacy-preserving and cost-efficient objectives, we formulate a sequential energy management unit (EMU) policy design as a constrained Markov decision process (CMDP), where the cost-efficient objective is optimized subject to the constraint on privacy preservation. We then develop a novel variational-constraint adversarial proximal policy optimization (VCA-PPO) algorithm to solve the CMDP without requiring prior knowledge of probabilistic models. Experimental results on a standard real-world dataset demonstrate the effectiveness of the proposed method and its superiority to the load-flatness benchmark method. Full article

Accurate thermal characterization of building insulation materials is essential for reliable energy performance assessment, regulatory compliance, and the development of high-performance envelopes. On one hand, the growing adoption of innovative insulating products, such as nanoporous materials, aerogel-based composites, bio-based panels, and thin insulating coatings, helps to enhance buildings’ energy efficiency by means of sustainable raw materials. On the other hand, conventional measurement techniques encounter significant challenges, due to their heterogeneity, reduced thickness, and unconventional geometries. In this study, an intra-laboratory comparison of three widely used methods for thermal conductivity determination is presented: the Transient Plane Source (TPS, Hot Disk) method, the Guarded Hot Plate (GHP) method, and the Heat Flow Meter (HFM) method. A total of twelve insulating materials, spanning super-insulating cores, insulating renders, bio-based panels, and nanocomposite coatings, were experimentally characterized under controlled laboratory conditions. A view on the analyzed insulating materials’ cradle-to-grave environmental impact is also given, to enhance the users’ awareness for the highly informed choice. The results highlight systematic differences between transient and steady-state approaches, with TPS measurements generally exhibiting larger deviations for materials characterized by surface roughness, limited thickness, or strong internal heterogeneity. In contrast, GHP and HFM methods show closer agreement when specimen geometry and stabilization requirements are satisfied. The influence of contact resistance, probing depth, specimen preparation, and uncertainty propagation is critically analyzed for each technique. The study provides practical insights into the applicability limits of commonly used thermal characterization methods and emphasizes the importance of selecting measurement techniques in relation to material morphology and testing constraints. These findings support more reliable thermal property assessment of emerging insulation materials and contribute to improved consistency between laboratory measurements and energy performance evaluations for buildings. Full article

Publicly available electricity datasets from operational industrial facilities remain limited due to instrumentation cost, retrofit complexity, and data governance constraints. This paper presents an openly accessible dataset of asset-level electrical energy measurements collected from a medium-scale industrial manufacturing facility over an approximately one-year observation window, with staged commissioning resulting in heterogeneous temporal coverage. The dataset includes time-series measurements from production machinery, auxiliary systems, and distribution-level assets instrumented using a heterogeneous fleet of Ethernet and RS-485 energy meters integrated via industrial gateways and programmable logic controllers. Measurements were acquired via a SCADA-based logging infrastructure and exported from an operational SQL historian. The publicly released dataset comprises fixed 15 min aggregated energy and power metrics derived from high-frequency SCADA telemetry. In its released ALL-phase representation, the dataset comprises measurements from 43 monitored assets and 1,039,873 15 min windows, corresponding to 2.96 GWh of measured electrical energy. Mean window-level data coverage is 99.99%, and 97.72% of ALL-phase windows satisfy the dataset’s reliability criterion. Interval records include energy consumption, demand, data coverage metrics, and reliability indicators. The dataset reflects real-world industrial monitoring conditions, including mixed communication pathways and irregular sampling behaviour, and is intended to support research in industrial energy analytics, data quality assessment, load profiling, and operational energy modelling. Full article