Large-scale Generation Research Articles

Efficient and Ultrastable Seawater Electrolysis at Industrial Current Density with Strong Metal-Support Interaction and Dual Cl--Repelling Layers.

Direct seawater electrolysis is emerging as a promising renewable energy technology for large-scale hydrogen generation. The development of Os-Ni4Mo/MoO2 micropillar arrays with strong metal-support interaction (MSI) as a bifunctional electrocatalyst for seawater electrolysis is reported. The micropillar structure enhances electron and mass transfer, extending catalytic reaction steps and improving seawater electrolysis efficiency. Theoretical and experimental studies demonstrate that the strong MSI between Os and Ni4Mo/MoO2 optimizes the surface electronic structure of the catalyst, reducing the reaction barrier and thereby improving catalytic activity. Importantly, for the first time, a dual Cl- repelling layer is constructed by electrostatic force to safeguard active sites against Cl- attack during seawater oxidation. This includes a strong Os─Cl adsorption and an in situ-formed MoO4 2- layer. As a result, the Os-Ni4Mo/MoO2 catalyst exhibits an ultralow overpotential of 113 and 336mV to reach 500mA cm-2 for HER and OER in natural seawater from the South China Sea (without purification, with 1m KOH added). Notably, it demonstrates superior stability, degrading only 0.37 µV h-1 after 2500 h of seawater oxidation, significantly surpassing the technical target of 1.0 µV h-1 set by the United States Department of Energy.

Chemical Reaction Simulator on Quantum Computers by First Quantization (II)─Basic Treatment: Implementation.

Chemical simulation is a key application area that can leverage the power of quantum computers. A chemical simulator that implements a grid-based first quantization method has promising characteristics, but an implementation fully in quantum circuits seems to have not been published. Here, we present "crsQ" (chemical reaction simulator Q), which is a quantum circuit generator that generates such a chemical simulator. The generated simulator is capable of antisymmetrization of the initial wave function and time-evolution of the wave function based on the Suzuki-Trotter decomposition. The potential energy term of the Hamiltonian is implemented using arithmetic gates, such as adders, subtractors, multipliers, dividers, and square roots. Circuit diagrams and output samples are shown. The number of qubits in the circuits scales on the order of O(η log η), where η is the number of electrons. Each component of the generated circuit was verified in unit tests. Along with this development, we designed frameworks to ease the development of large-scale circuits, namely, a temporary qubit allocation framework and an abstract syntax tree framework for arithmetic formulas. These frameworks are expected to be useful in large-scale quantum circuit generators.

Investigation of control strategies for the hydrogen fueled R-Graz cycle

In the context of large-scale hydrogen power generation, the Graz cycle and its improved version, the R-Graz cycle, exhibits excellent performance and technological feasibility. For such semi-closed, split cycles, apart from the conventional adjustments of fuel consumption and compressor IGV angle, compressor inlet pressure and split ratio are two additional crucial control parameters. In this study, six control strategies are firstly formulated and compared. The results indicate that the control strategy aiming to maintain the turbine inlet temperature outperforms the strategy aiming to maintain the exhaust temperature in terms of efficiency. Among them, the inventory control strategy demonstrates superior efficiency compared to the split ratio and IGV control strategies. Taking into account the advantages of various strategies, a hybrid inventory control strategy suitable for the R-Graz cycle is proposed. Based on this strategy, at 40% load and the ambient temperature of 19 °C, the efficiency reaches 87.06% of the design point, surpassing conventional F-class gas-steam combined cycle units. When the ambient temperature varies, under winter conditions of 10 °C and summer conditions of 30 °C, the full load net efficiency of the cycle reaches 72.06% and 69.60%, respectively. This research holds practical guidance value for the engineering application of the R-Graz cycle and the efficient utilization of hydrogen energy.

Point-based Models: A Unified Approach for Geometric Constraint Analysis of Planar N-Bar Mechanisms with Rotary, Sliding, and Rolling Joints

Abstract Kinematic simulation of planar n-bar mechanisms has been an intense topic of study for several decades now. However, a large majority of efforts have focused on position analysis of such mechanisms with limited links and joint types. This paper presents a novel, unified approach to analysis of geometric constraints of planar n-bar mechanisms with revolute joint (R-joint), prismatic joint (P-joint), and rolling joint. This work is motivated by a need to create and program a system of constraint equations, which deal with different types of joints in a unified way. A key feature of this work is that the rolling joint constraints are represented by four-point models, which enables us to use the well-established undirected graph rigidity analysis algorithms. As a result, mechanisms with an arbitrary combination of revolute-, prismatic-joints, and wheel/gear/wheel-belt chains without any limitations on their actuation, can be analyzed and simulated efficiently for potential implementation in an interactive computer software and large-scale data generation.

Noble-metal-free Bi-OZIS nanohybrids for sacrificial-agent-free photocatalytic water splitting: With long-lived photogenerated electrons

The production of hydrogen via the hydrogen evolution reaction, reliant on the use of valuable sacrificial agents, is not conducive to large-scale photocatalytic hydrogen generation. A more pragmatic approach involves the creation of catalyst materials free from noble metals, which possess enduring photogenerated electrons suitable for catalyzing hydrogen evolution from undiluted water. In our research, x%Bi-OZIS nanohybrids were effectively synthesized. Remarkably, the hydrogen evolution rate for the 10 %Bi-OZIS sample reached 170.22 μmol g-1h−1, marking an enhancement of 11.8 times over pure ZIS and 3.7 times over OZIS, achieved without resorting to sacrificial agents. Notably, post-light exposure, H2O2 presence was identified in the water solution. The strategy of substituting some of the sulfur with oxygen doping extends the lifespan of the photogenerated carriers. Further, the introduction of Bi spheres onto the OZIS nanosheet surfaces substantially reduces electron-hole interband recombination. These adaptations collectively augment the photogenerated electron lifespan in x%Bi-OZIS nanohybrids, leading to heightened photocatalytic redox proficiency. This research outlines a method for devising an economical solar-driven water splitting mechanism devoid of sacrificial agents.

Integrating Automated Labeling Framework for Enhancing Deep Learning Models to Count Corn Plants Using UAS Imagery.

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2-V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models.

Multiscale Sieve for Smart Prime Generation and Application in Info-Security, IoT and Blockchain

The huge computational cost required to test whether a number is prime and the inefficiency of the known sieving algorithms for extremely large inputs have posed significant challenges in computational number theory. Traditional deterministic prime generation methods struggle to maintain performance when the input sizes increase exponentially. In this work, we show that, through multiscale distribution and deterministic prime number generation, it is possible to create a multiscale sieve with drastically better performance than the deterministic algorithms known to date, providing a more efficient solution for large-scale prime number generation, demonstrated by several benchmarks that highlight the potential of our approach. Consequently, we can gain some advantages in cryptography and in info-security, such as in IoT and blockchain environments.

Harnessing the heat below: Efficacy of closed-loop systems in the cooper basin, Australia

Transitioning to renewable energy is vital for combating climate change and reducing CO2 emissions. Geothermal energy, sourced from the earth's crust, presents a promising alternative. While shallow geothermal energy extraction grows steadily, tapping into deep Hot Dry Rock reservoirs poses challenges. Sustainable utilization of deep geothermal energy is crucial for large-scale electricity generation. Australia's Habanero enhanced geothermal system (EGS) projects faced obstacles due to complex fracture networks. Borehole heat exchangers (BHEs) offer a solution, circulating a working fluid without direct contact with geofluids. This study assesses coaxial BHEs' feasibility in the Habanero reservoir using a 2D axisymmetric model (2D-AM), comparing it with a simpler 1D depth-integrated model (1D-DIAM). Both models yield similar outcomes. Additionally, results indicated that the production temperature of the coaxial BHE exponentially decreased from 250 °C to 100 °C within 10 h, rendering energy recovery for electricity generation unsuitable after 10 h of operation. The study proposes an energy recovery cycle of 10 h followed by a 110-h shutdown. Injection temperatures and flow rates significantly impact production efficiency, while steel casing's thermal conductivity has minimal influence on heat exchanger performance.

A cloud-based simulation framework for high-fidelity room impulse response dataset generation: case study, validation and computational performance

Increased cost-effective computational resources in the cloud in combination with state-of-the-art simulation algorithms has opened doors for generating large quantities of simulated high-fidelity room impulse responses. Such datasets can, e.g., be used for training machine learning based audio signal processing algorithms such as speech enhancement or dereverberation, as well as for carrying out virtual prototyping of audio devices in different acoustic conditions. The authors have previously presented a cloud-based simulation engine that is well suited for such large-scale dataset generation tasks. The simulation engine leverages a hybrid wave/geometrical solver, in combination with various geometry pre-processing technology and spatial audio post-processing technology. In this paper, we present a dataset generation case study using this simulation engine, as well as a novel validation study into certain aspects of the spatial audio post-processing. Finally, we present an analysis into computational performance when leveraging multi-node computational architectures to run extremely large wave-based simulations using the simulation framework. To the best of our knowledge, this is the first time a multi-node wave-based acoustics solver presented, enabling wave-based simulations with billions of degrees of freedom.

Innovation in clean energy from man-made wind and small-wind generation

The need to reduce global emissions leads us to look for various sources of clean energy. In recent decades, wind technology has advanced significantly, enabling large-scale power generation in both marine and terrestrial environments, as well as the development of mini-wind solutions. However, we often underestimate the capacity of certain human activities and production processes to generate clean energy, wasting their true potential. This work focuses on using artificially generated wind gusts to transform them into clean electricity through small wind turbines. The proposal is developed in four phases: (1) identify activities that generate wind, (2) collect data on wind speed and direction, (3) perform a descriptive statistical analysis of the wind resource, and (4) select the appropriate technology to calculate the electricity generation. The proposal is evaluated using the air flow produced by the air conditioning systems of a data center in Colombia. The results are analyzed from technical, economic, environmental, and political perspectives. Through small wind power, an annual production of approximately 468 MWh is estimated, avoiding the emission of 300 metric tons of CO2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CO}}_2$$\\end{document}.

Stochastic optimal allocation of grid-side independent energy storage considering energy storage participating in multi-market trading operation

The integration of large-scale intermittent renewable energy generation into the power grid imposes challenges to the secure and economic operation of the system, and energy storage (ES) can effectively mitigate this problem as a flexible resource. However, the conventional ES allocation is mostly planned to meet the regulation demands of individual entities, which is likely to result in low utilization of ES and difficult to recover the investment cost. Therefore, a two-stage stochastic optimal allocation model for grid-side independent ES (IES) considering ES participating in the operation of multi-market trading, such as peak-valley arbitrage, frequency regulation, and leasing, is proposed in this paper to improve the comprehensive benefits and utilization rate of ES. The first stage aims to allocate IES and develop a systematic scheduling plan based on the forecast of wind power output and load demand, while the second stage responds to the uncertainty of wind power output by re-dispatching generating units and invoking ES power leased by wind farms. Then, a two-layer loop iterative solution algorithm based on the Benders decomposition is formed to effectively solve the proposed model. Finally, the approach developed in this paper is applied to a modified IEEE RTS-79 test system, and the results verify that it is both feasible and effective.

Two-Stage Optimal Configuration Strategy of Distributed Synchronous Condensers at the Sending End of Large-Scale Wind Power Generation Bases

The transmission end of large-scale wind power generation bases faces challenges such as high AC-DC coupling strength, low system inertia, and weak voltage support capabilities. Deploying distributed synchronous condensers (SCs) within and around wind farms can effectively provide transient reactive power support, enhance grid system inertia at the transmission end, and improve dynamic frequency support capabilities. However, the high investment and maintenance costs of SCs hinder their large-scale deployment, necessitating the investigation of optimal SC configuration strategies at critical nodes in the transmission grid. Initially, a node inertia model was developed to identify weaknesses in dynamic frequency support, and a critical inertia constraint based on node frequency stability was proposed. Subsequently, a multi-timescale reactive power response model was formulated to quantify the impact on short-circuit ratio improvement and transient overvoltage suppression. Finally, a two-stage optimal configuration strategy for distributed SCs at the transmission end was proposed, considering dynamic frequency support and transient voltage stability. In the first stage, the optimal SC configuration aimed to maximize system inertia improvement per unit investment to meet dynamic frequency support requirements. In the second stage, the configuration results from the first stage were adjusted by incorporating constraints for enhancing the multiple renewable short-circuit ratio (MRSCR) and suppressing transient overvoltage. The proposed model was validated using the feeder grid of a large energy base in western China. The results demonstrate that the optimal configuration scheme effectively suppressed transient overvoltage at the generator end and significantly enhanced the system’s dynamic frequency support strength.

Supporting strategy for investment evaluation of photovoltaic power generation engineering projects using multi-criteria decision analysis methods

This scientific study examines the evaluation of photovoltaic power generation projects through the application of multi-criteria decision analysis methods. Two groups of large-scale grid-connected PV power generation system projects with a nominal power of 50 MW and 500 MW respectively were analyzed and evaluated. These systems were designed to be installed in the same wider geographical area in northern Greece, but they were differentiated in the part of the PV circuit. Twelve systems were analyzed in which either monocrystalline silicon panels or poly-crystalline silicon panels or bifacial photovoltaic panels were to be installed. In these systems either central photovoltaic inverters or photovoltaic string inverters were considered for installation. The following criteria were used to evaluate the investment in these projects. These criteria were related to the profitability, the financial cost, the technical level, and the electrical energy production of the systems and these were the initial investment cost, the operation and maintenance cost, the levelized cost of electricity, the net present value, the internal rate of return, the capital recovery or payback period, the technical level of the photovoltaic circuit, the technical maturity of the photovoltaic circuit, and the annual electricity production. The evaluation of these criteria was initially conducted with fixed weighting coefficients followed by a sensitivity analysis of these weighting coefficients. The results of the evaluation using the PROMETHEE, AHP and TOPSIS multi-criteria decision analysis methods showed that the PV power generation systems which should be preferred are those with increased nominal power, where monocrystalline silicon technology panels are employed following the central inverter topology.

Large-scale prediction of solar irradiation, shading impacts, and energy generation on building Façade through urban morphological indicators: A machine learning approach

Incorporating Building-integrated Photovoltaics (BIPVs) into urban environments offers a sustainable approach by transforming building exteriors into renewable energy sources, especially by utilizing the extensive vertical surfaces in cities. While individual building applications have been extensively studied, there is a lack of research on BIPV potential at the urban scale due to the complexity of urban fabrics. Therefore, this study presents a novel method for predicting large-scale solar irradiation and energy generation on building surfaces using urban morphological indicators through machine learning models. Using an Australian city as a case study, a hierarchical two-level machine learning model is developed to predict the solar potential for building façade PV systems. The model incorporates 13 urban morphological predictors to determine average shading height and unshaded solar irradiance. By combining these predictions with wall datasets and different façade BIPV efficiencies, the proposed method predicts unshaded electricity generation. The results showcase high efficiency and accuracy in the prediction of the shading height, solar irradiance, and electricity generation. This innovative approach aids urban planners in considering large-scale surface BIPVs into urban development.

Algal Biofuels: A Comprehensive Review and Analysis

The depletion of fossil fuels and increased atmospheric CO2 levels have precipitated a global energy and environmental crisis. The cultivation of microalgae offers a viable remedy by utilizing solar energy and assimilating CO2 for biofuel production via photosynthesis. Microalgae demonstrate rapid growth in diverse environments, rendering them a dependable biomass source for large-scale biofuel generation. Furthermore, their biofuel production can be tailored through the manipulation of growth conditions or genetic engineering. This review aims to highlight the variety of biofuels produced from microalgae and the methodologies to optimize their yield. This review also explores the economic implications of algal biofuel production in both open ponds and closed photobioreactors. Various strategies for the photo-production of biohydrogen utilizing the hydrogenase enzyme from green algae are investigated. Additionally, certain microalgae species are recognized for their potential as biodiesel sources due to their high lipid content. The lipid profiles of leading oil-producing algal strains under optimal conditions are assessed. This review further examines the potential of microalgae in synthesizing petroleum-based chemicals and in generating bioethanol and biogas from algal biomass.

A New Precise Point Positioning with Ambiguity Resolution (PPP-AR) Approach for Ground Control Point Positioning for Photogrammetric Generation with Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) are now widely preferred systems that are capable of rapid mapping and generating topographic models with relatively high positional accuracy. Since the integrated GNSS receivers of UAVs do not allow for sufficiently accurate outcomes either horizontally or vertically, a conventional method is to use ground control points (GCPs) to perform bundle block adjustment (BBA) of the outcomes. Since the number of GCPs to be installed limits the process in UAV operations, there is an important research question whether the precise point positioning (PPP) method can be an alternative when the real-time kinematic (RTK), network RTK, and post-process kinematic (PPK) techniques cannot be used to measure GCPs. This study introduces a novel approach using precise point positioning with ambiguity resolution (PPP-AR) for ground control point (GCP) positioning in UAV photogrammetry. For this purpose, the results are evaluated by comparing the horizontal and vertical coordinates obtained from the 24 h GNSS sessions of six calibration pillars in the field and the horizontal length differences obtained by electronic distance measurement (EDM). Bartlett’s test is applied to statistically determine the accuracy of the results. The results indicate that the coordinates obtained from a two-hour PPP-AR session show no significant difference from those acquired in a 30 min session, demonstrating PPP-AR to be a viable alternative for GCP positioning. Therefore, the PPP technique can be used for the BBA of GCPs to be established for UAVs in large-scale map generation. However, the number of GCPs to be selected should be four or more, which should be homogeneously distributed over the study area.

Network topology planning method for interconnecting large-scale renewable generators considering dynamic stability limitations

It is well-known that the connection of renewable generators (RGs) can result in stability issues. However, existing network topology planning methods primarily focus on economic factors and static safety constraints, often neglecting dynamic stability considerations. The aim of this study is to avoid the risk of oscillations at the planning stage, rather than addressing them through damping control after their occurrence. First, an index is proposed for numerically evaluating the impact of different network topologies on the stability of connecting RGs, concluding that parallel connection is the most stable among various topologies. Then, in scenarios involving multiple collection regions of RGs, a method is proposed to select the connection locations of RGs. This indicates that the distribution of wind generators should consider the transmission line length of collection nodes, rather than connecting all wind generators to one of them. Finally, the network topology planning steps from a dynamic stability perspective are summarized. It is meaningful to improve the stability of a power system connected by large-scale RGs without the need for additional damping control. Two case studies are conducted to validate the accuracy of the analyzed results, but in the future, more applicable examinations will be conducted.

A Hybrid Prototype Method Combining Physical Models and Generative Artificial Intelligence to Support Creativity in Conceptual Design

Conceptual design is an essential stage in the design process, and its ultimate success largely depends on designers’ creativity. Both physical and digital prototypes are commonly adopted by designers to support ideation and creativity, providing intuitive perception and rapid iteration, respectively. In recent advancements, large-scale generation models are able to offer data-enabled creativity support by generating high-quality solutions comparable to human designers. This opens up an imaginary space for designers and brings new possibilities for design tools. In this study, we proposed a hybrid prototype method that synergistically combines physical models and generative artificial intelligence (AI) in the conceptual design stage. Correspondingly, we developed a hybrid prototype system to implement the proposed method. We conducted a comparative user study with 45 designers who completed a design task using the physical prototype method, standalone generative AI, and the hybrid prototype method, respectively. Our results verified the effectiveness of the hybrid prototype method and investigated its mechanism in supporting creativity. Finally, we discussed the application value and optimisation space of the hybrid prototype method.

Comparative Effectiveness of Second-Line Antihyperglycemic Agents for Cardiovascular Outcomes: A Multinational, Federated Analysis of LEGEND-T2DM

BackgroundSodium-glucose cotransporter 2 inhibitors (SGLT2is) and glucagon-like peptide-1 receptor agonists (GLP-1 RAs) reduce the risk of major adverse cardiovascular events (MACE) in patients with type 2 diabetes mellitus (T2DM). However, their effectiveness relative to each other and other second-line antihyperglycemic agents is unknown, without any major ongoing head-to-head clinical trials. ObjectivesThe aim of this study was to compare the cardiovascular effectiveness of SGLT2is, GLP-1 RAs, dipeptidyl peptidase-4 inhibitors (DPP4is), and clinical sulfonylureas (SUs) as second-line antihyperglycemic agents in T2DM. MethodsAcross the LEGEND-T2DM (Large-Scale Evidence Generation and Evaluation Across a Network of Databases for Type 2 Diabetes Mellitus) network, 10 federated international data sources were included, spanning 1992 to 2021. In total, 1,492,855 patients with T2DM and cardiovascular disease (CVD) on metformin monotherapy were identified who initiated 1 of 4 second-line agents (SGLT2is, GLP-1 RAs, DPP4is, or SUs). Large-scale propensity score models were used to conduct an active-comparator target trial emulation for pairwise comparisons. After evaluating empirical equipoise and population generalizability, on-treatment Cox proportional hazards models were fit for 3-point MACE (myocardial infarction, stroke, and death) and 4-point MACE (3-point MACE plus heart failure hospitalization) risk and HR estimates were combined using random-effects meta-analysis. ResultsOver 5.2 million patient-years of follow-up and 489 million patient-days of time at risk, patients experienced 25,982 3-point MACE and 41,447 4-point MACE. SGLT2is and GLP-1 RAs were associated with lower 3-point MACE risk than DPP4is (HR: 0.89 [95% CI: 0.79-1.00] and 0.83 [95% CI: 0.70-0.98]) and SUs (HR: 0.76 [95% CI: 0.65-0.89] and 0.72 [95% CI: 0.58-0.88]). DPP4is were associated with lower 3-point MACE risk than SUs (HR: 0.87; 95% CI: 0.79-0.95). The pattern for 3-point MACE was also observed for the 4-point MACE outcome. There were no significant differences between SGLT2is and GLP-1 RAs for 3-point or 4-point MACE (HR: 1.06 [95% CI: 0.96-1.17] and 1.05 [95% CI: 0.97-1.13]). ConclusionsIn patients with T2DM and CVD, comparable cardiovascular risk reduction was found with SGLT2is and GLP-1 RAs, with both agents more effective than DPP4is, which in turn were more effective than SUs. These findings suggest that the use of SGLT2is and GLP-1 RAs should be prioritized as second-line agents in those with established CVD.

IoT-based real-time analysis of battery management system with long range communication and FLoRa

In the current scenario, the world is focused on renewable energy generation to achieve sustainability by 2030 regarding clean and affordable energy. Lithium-ion (Li-ion)-based Battery Energy storage (BES) is a prominent approach that is widely adopted for managing large-scale renewable energy generation. Battery Management Systems (BMS) play a critical role in optimizing battery performance of BES by monitoring parameters such as overcharging, the state of health (SoH), cell protection, real-time data, and fault detection to ensure reliability. Previous studies have concluded that the implementation of Internet of Things (IoT) with LoRa ensures effective real-time monitoring of the BMS of Li-ion batteries. This study proposed and implemented a customized LoRa and IoT-based hardware system with a gateway to acquire parameters such as terminal voltage, current, charge voltage, charge current, cycle, temperature, state of charge (SoC), and SoH, and log them into the cloud server. An OMnet++-based Framework for LoRa (FLoRa) simulation was implemented to analyze the power consumption and residual energy of the customized LoRa nodes. The simulation was configured with a spread factor of 7, a carrier frequency of 433 MHz, a bandwidth of 125 KHz, and a transmission power of 2 dBm. The simulation results indicated that Node 3 had the highest mean power consumption (0.028233) and total energy consumption (0.146592), whereas Node 0 exhibited the lowest mean power consumption (0.023413) and total energy consumption (0.070204). Additionally, a comprehensive dataset encompassing voltage, current, and time was created and utilized for precise calculations of the battery's capacity and state of health, with potential use in future predictions.