Installation Costs Research Articles

Structural performance evaluation of mobile solar-powered battery swap station for electric motorcycles

This study introduces a structural design and static analysis of a Mobile Battery Swap Station for electric motorcycles, powered by solar energy, to address the critical need for sustainable and off-grid charging infrastructure. As the adoption of electric motorcycles continues to grow, driven by the demand for eco-friendly transportation alternatives, the lack of widespread and accessible charging infrastructure poses a significant barrier to their widespread use. In many regions, the expansion of traditional grid-connected charging stations is hindered by high installation costs, space limitations in urban environments, and logistical challenges in remote or underserved areas. The design focuses on a robust, mobile frame made from hollow iron of AISI 1010 steel, supporting the integration of photovoltaic (PV) panels to supply renewable energy directly to the battery-swapping system. Using Finite Element Analysis (FEA), the station’s structural integrity was evaluated under a uniformly distributed load of 700 kg, simulating real-world loading conditions for components essential to electric motorcycle operations, including PV mounts and battery racks. Results show a maximum displacement of 4.541 mm, a peak stress of 57.716 MPa, and a Factor of Safety (FOS) of 2.9, confirming the design’s ability to securely and stably support the necessary equipment for battery swapping. This mobile, solar-powered solution advances sustainable infrastructure for electric motorcycles, enabling flexible, grid-independent battery swapping that is particularly beneficial in urban areas and remote locations. This station contributes to greener mobility solutions tailored for electric motorcycles, aligning with broader efforts to support eco-friendly transportation systems

Lighting design affects the uniformity and growth of plants in a vertical farming system

Light is essential for plant production and has various effects on plant quality. Vertical farms typically use light-emitting diodes (LEDs) as light sources. However, the cost of LEDs varies with wattage and the initial installation costs are generally high. Therefore, to explore more cost-effective LED designs, we aimed to investigate the impact of red LED chips density on light distribution and plant growth under the same total electricity consumption. To this end, we exposed baby leaf soybean (Glycine max (L.) Merr.; 5 days) and kale (Brassica oleracea var. acephala; 18 days) to LEDs light with different arrangements of red and white chips. Plants were exposed to either 2 W chips with a red: white ratio of 4: 64 (2W4R treatment) or 1 W chips with a red: white ratio of 8: 64 (1W8R treatment) across the entire LED bar. We observed that the distribution of red light in the cultivation room differed depending on the density of the red LED chips. We found that arranging low-power red LED chips at narrow intervals resulted in uniform light distribution across the entire cultivation bed, positively affecting crop growth. Baby leaf soybean and kale exhibited uniform growth under 1W8R and growth was particularly enhanced in kale. This may be because of the dense leaf structure of kale, which promotes photosynthesis under a uniform light environment. The results of this study demonstrate that a favorable light environment can be created by altering the position and distribution of red LED chips, thereby inducing uniform growth in plants.

Development and Optimization of a Recyclable Non-Embedded Support System for Thermal Pipeline Trenches in Urban Environments.

Existing support systems for thermal pipeline trenches often fail to meet the specific needs of narrow strips, tight timelines, and short construction periods in urban environments. This study introduces a novel recyclable, non-embedded support system composed of corrugated steel plates, retractable horizontal braces, angle steel, and high-strength bolts designed to address these challenges. The system's effectiveness was validated through prototype testing and optimized using Abaqus finite element simulations. The research hypothesizes that this new support structure will enhance construction efficiency, reduce installation costs, and provide adaptable and sustainable solutions in urban trench applications. Prototype tests demonstrated that the proposed support had maintained safety and stability in trenches of 2 m and 3 m depth under a 58 kPa load and rainfall, as well as the 4 m deep trenches under asymmetric loading of 80 kPa. Optimization of the proposed system included installing two screw jacks on each horizontal brace and adjusting the corrugated plates, resulting in reduced weight, improved node strength, and enhanced screw jack adjustability. Numerical simulations confirmed the optimized system's reliability in trenches up to 3 m deep, with caution required for deeper applications to avoid structural failure. The proposed support system offers notable advantages over traditional methods by improving construction efficiency, flexibility, and adaptability while also reducing costs, ensuring safety, and promoting environmental sustainability. Its modular design allows for rapid installation and disassembly, making it suitable for projects with strict deadlines and diverse construction conditions. The findings uphold the initial hypotheses and demonstrate the system's practicality in urban trench projects.

Optimization of Video Surveillance System Deployment Based on Space Syntax and Deep Reinforcement Learning

With the widespread deployment of video surveillance devices, a large number of indoor and outdoor places are under the coverage of cameras, which plays a significant role in enhancing regional safety management and hazard detection. However, a vast number of cameras lead to high installation, maintenance, and analysis costs. At the same time, low-quality images and potential blind spots in key areas prevent the full utilization of the video system’s effectiveness. This paper proposes an optimization method for video surveillance system deployment based on space syntax analysis and deep reinforcement learning. First, space syntax is used to calculate the connectivity value, control value, depth value, and integration of the surveillance area. Combined with visibility and axial analysis results, a weighted index grid map of the area’s surveillance importance is constructed. This index describes the importance of video coverage at a given point in the area. Based on this index map, a deep reinforcement learning network based on DQN (Deep Q-Network) is proposed to optimize the best placement positions and angles for a given number of cameras in the area. Experiments show that the proposed framework, integrating space syntax and deep reinforcement learning, effectively improves video system coverage efficiency and allows for quick adjustment and refinement of camera placement by manually setting parameters for specific areas. Compared to existing coverage-first or experience-based optimization, the proposed method demonstrates significant performance and efficiency advantages.

МЕМБРАННЫЙ ДАВИД И РАМНЫЙ ГОЛИАФ

The article is devoted to explaining the advantages of membrane structures in comparison with frame structures, which are traditionally produced in large quantities by Russian metal structures factories. With the mass application of membrane structures, it is possible to reduce the cost of building structures, as well as significantly reduce the cost of manufacturing and installation, which will significantly reduce the total cost of the building structure of a building or structure. To promote the mass application of membrane structures in construction production, the authors invite interested organizations to cooperate and to combat nfair competition.

Design, Implementation, and Analysis for Reducing Energy Losses in Solar Inverters through the Use of SiC MOSFETs

The integration of Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) in solar inverters has emerged as a promising solution for enhancing energy conversion efficiency. This study presents the design and performance analysis of a high-efficiency solar inverter utilizing SiC MOSFETs, targeting increased power output and improved reliability in photovoltaic (PV) systems. The proposed inverter design focuses on reducing switching losses, minimizing heat dissipation, and achieving higher switching frequencies compared to traditional silicon-based devices. The adoption of SiC technology enables reduced conduction and switching losses due to its superior thermal properties and high breakdown voltage, making it ideal for solar inverter applications. Simulation results demonstrate significant improvements in efficiency—exceeding 98%—under varying load conditions. Additionally, the inverter’s performance was evaluated in terms of total harmonic distortion (THD), with values well within acceptable limits, ensuring clean and stable power output. The thermal management capabilities of SiC MOSFETs are also highlighted, showing reduced heat sink requirements and longer operational lifetimes. This research further explores the practical implementation challenges, such as gate driver considerations and EMI suppression, to optimize inverter design for real-world scenarios. The findings suggest that utilizing SiC MOSFETs in solar inverters not only enhances energy efficiency but also contributes to system compactness, potentially reducing the overall cost of PV installations. The study concludes with recommendations for future developments in SiC-based power electronics for renewable energy applications.

Hydraulic fault detection of wind turbine generators using artificial neural networks

In the current context where fossil resources are diminishing globally, and carbon emissions are increasing daily, the importance of green energy, particularly wind energy, is growing significantly. The increasing of wind turbines will not only reduce the carbon footprint but also decrease dependence on external resources. To increase the installed capacity of wind turbines, it is crucial to reduce not only installation costs but also operational costs. The largest proportion of operational costs is service, and maintenance costs. One of the most critical approaches to reducing service, and maintenance costs is preventive maintenance activities. The objective of preventive maintenance activities is to minimize or ideally eliminate production losses through scheduled turbine shutdowns before failures occur. In this study, artificial neural network-based algorithms that predict potential hydraulic failures during the operational period were utilized. For this purpose, data from the turbine SCADA system over a period of two years, considering the equipment, and sensors connected to hydraulic systems, were compiled. The study was conducted using the WEKA program, comparing Multilayer Perceptron (MLP), Radial Basis Function Classifier (RBF Classifier), SMOreg (Support Vector Machines for Regression) algorithms. Result of the study, the MLP algorithm was applied with a percentage split of 66% for training, and 33% for testing, achieving a prediction accuracy of 96.32%

Source‐storage‐transmission planning method considering carbon emission responsibility allocation

AbstractFair sharing of carbon responsibility is crucial to achieve the goal of low‐carbon transformation and dual‐carbon power system. In response to the current issue of a certain proportion of joint responsibility between source and load, it does not comply with the principle of “who causes who bears”. This thesis proposes a source storage transport coordinated planning method for allocating carbon responsibility from a planning perspective. Firstly, based on the basic principle of “who causes, who bears”, it classifies and holds accountable various stakeholders, establishes reasonable and fair allocation principles including reward and punishment indicators, and a dual carbon responsibility cost allocation rule for electricity balance and power balance in the power system; Secondly, based on the previous works, a source storage network joint planning model which comprehensively considers the investment cost of conventional thermal power installation, energy storage investment, wind abandonment penalty cost, transmission line expansion cost, and carbon emission responsibility cost is constructed. Finally, taking the Garver‐6 node system as an example, the effectiveness and feasibility of the proposed method were verified by comparing it with the source storage and transportation planning method that does not consider carbon responsibility allocation.

Adoption and Diffusion of Residential Photovoltaics under Heterogeneous Behaviors

Driven by global sustainability goals, residential photovoltaic (PV) has become a key driver in achieving carbon neutrality. Residential PV systems, due to their low cost and ease of installation, are gaining increasing significance. However, uneven regional development in China has posed challenges.This study, based on consumer behavior theory and supported by text analysis, designed a survey to explore significant internal perception factors influencing PV adoption. Hypothesis testing confirmed the importance of these factors. Using agent-based modeling, we incorporated these significant internal perceptions and demographic attributes, along with external factors such as information dissemination, to simulate consumer decision-making and the effectiveness of promotional strategies.The results show that positive perceptions of benefits, ease of use, and lower risk significantly enhance the willingness to adopt PV systems. Furthermore, controlling the scope of information dissemination and leveraging weak ties (such as online networks) are crucial for improving promotional effectiveness. This research offers practical insights for optimizing PV marketing strategies, aiding both Chinas and global green energy transitions.

Assessing the economic performance of agrivoltaic systems in vineyards – framework development, simulated scenarios and directions for future research

IntroductionThis paper introduces a framework for assessing the economic performance of agrivoltaic systems (AVS) in vineyards. The study aims to classify factors influencing the profitability of integrating photovoltaic (PV) systems with viticultural practices, emphasizing potential synergistic benefits. Focused on the geographic and climatic conditions of Geisenheim, Germany—home to the first AVS installation in viticulture in Germany—the framework highlights the need to explore economic and operational parameters to assess AVS feasibility.MethodsThe study operationalized its framework by simulating various scenarios based on key variables such as capital expenditures (CAPEX), operational expenditures (OPEX), and revenue streams from grape and energy production. Key factors considered included solar radiation, panel transparency, and regulatory impacts. Scenarios evaluated included: i) Configurations with fully opaque and semi-transparent PV modules. Ii) Adjustments for full mechanization of viticulture practices. Iii) Self-consumption of the produced energy. The framework employed these simulations to evaluate economic outcomes and identify profitability determinants under different conditions.ResultsFindings indicate that under current conditions in Germany, AVS systems are not economically viable for widespread adoption. Key results include: Economic Outcomes: High initial costs (CAPEX) and insufficient revenue from combined grape and energy production lead to negative net present values over a 20-year period across all simulated scenarios. Profitability Determinants: CAPEX and energy prices emerged as critical factors, while viticulture-related costs and revenues had a minor impact on overall profitability. Potential Improvements: Scenarios incorporating multiple positive changes—such as premium wine pricing, higher feed-in tariffs, and increased self-consumption of energy—demonstrated potential for economic feasibility.DiscussionThe study underscores that high CAPEX and low revenues from grape and energy production are the main barriers to AVS profitability. For AVS systems to achieve economic sustainability, substantial reductions in installation costs, increases in energy prices, or technological advances in efficiency are required. Promising configurations combining multiple favorable changes suggest a pathway toward economic feasibility but highlight the need for further development and innovation. The framework provides valuable guidance for future research and investment strategies, emphasizing the importance of: i) Long-term Trials: To evaluate viticultural impacts and synergistic benefits. Ii) Dynamic Models: Incorporating evolving revenue streams, cost structures, and regulatory impacts. iii) Broad Assessments: Exploring the environmental and social benefits of AVS alongside economic performance. Continuous updates to the framework will ensure its relevance, reflecting advancements in PV technology, viticultural practices, and policy environments. This approach will help bridge the gap between renewable energy and sustainable agriculture.

Earned value management used to monitor the project costs of ballast system fabrication and installation on a cargo ship

Earned value management used to monitor the project costs of ballast system fabrication and installation on a cargo ship

Cooperative Advisory Residual Policies for Congestion Mitigation

Fleets of autonomous vehicles can mitigate traffic congestion through simple actions, thus improving many socioeconomic factors such as commute times and gas costs. However, these approaches are limited in practice as they assume precise control over autonomous vehicle fleets, incur extensive installation costs for a centralized sensor ecosystem, and fail to account for uncertainty in driver behavior. To this end, we develop a class of learned residual policies that can be used in cooperative advisory systems and only require the use of a single vehicle with a human driver. Our policies advise drivers to behave in ways that mitigate traffic congestion while accounting for diverse driver behaviors, particularly drivers’ reactions to instructions, to provide an improved user experience. To realize such policies, we introduce an improved reward function that explicitly addresses congestion mitigation and driver attitudes to advice. We show that our residual policies can be personalized by conditioning them on an inferred driver trait that is learned in an unsupervised manner with a variational autoencoder. Our policies are trained in simulation with our novel instruction adherence driver model and evaluated in simulation and through a user study (N = 16) to capture the sentiments of human drivers. Our results show that our approaches successfully mitigate congestion while adapting to different driver behaviors, with up to 20% and 40% improvement as measured by a combination metric of speed and deviations in speed across time over baselines in our simulation tests and user study, respectively. Our user study further shows that our policies are human-compatible and personalized to drivers.

Optimal reconfiguration of a distribution network integrated photovoltaic and wind systems using blood-sucking leech optimizer

This paper presents a planning strategy for integrating renewable distributed generation (DG) units into a distribution network, incorporating network reconfiguration to enhance the network's technical, economic, and environmental performance. Utilizing a novel meta-heuristic algorithm, the Blood-Sucking Leech Optimizer (BSLO), the study addresses a multi-objective optimization problem aimed at determining the optimal placement and sizing of DG units, as well as the most effective network topology. This approach seeks to minimize active power losses, improve voltage profiles, reduce installation costs, and lower greenhouse gas emissions. The model accounts for variable load demands, climatic factors (such as ambient temperature, solar irradiation, and wind speed), and fluctuating energy prices, reflecting realistic operating conditions. Tested on the IEEE 69-bus distribution network, the BSLO algorithm demonstrated rapid convergence to the global optimum by effectively balancing exploration and exploitation phases. Compared to other meta-heuristic methods, such as the Grey Wolf Optimizer, Gorilla Troops Optimizer, Walrus Optimization Algorithm, and Artificial Hummingbird Algorithm, the BSLO consistently achieved superior accuracy and faster convergence, resulting in higher precision and optimization efficiency. The optimal deployment of two PV generators and two wind turbines, combined with selective line switch openings, resulted in an 87.66% reduction in active power losses, a 73.30% decrease in voltage deviation, a 51.91% reduction in overall system costs, and a 62.74% decrease in greenhouse gas emissions compared to the base case.

Tail-End Evaluation Based on the Cost-Effectiveness of Bridge Life Cycles

The number of bridges in China is increasing year by year. The contradiction between the declining performance of bridge structures and the increasing traffic volume in China is becoming increasingly prominent, causing a concentrated upsurge in operation and maintenance needs during the service life of the bridges. In accordance with the goal and vision of the comprehensive construction of the Bridge Structural Health Monitoring (BSHM) system, the study of bridge cost-effectiveness now focuses on the installation, operation, and maintenance costs of the BSHM system, the renovation costs of bridge structure repair and maintenance, and the benefits of the BSHM system in assisting in expansion of the service life of bridges. In this study, a new cost-effectiveness framework is proposed that could be used for the operation and maintenance period. The cost-effectiveness framework is constructed from five perspectives: installation, operation, and maintenance costs of the BSHM system; repair and renovation costs during the operation and maintenance period; demolition costs; operation benefits during the operation and maintenance period; and the recovery benefits. The difference between costs and benefits is calculated to evaluate the cost-effectiveness, the service life, and the structural reliability of bridges, in order to provide auxiliary decision making for bridges. A bridge in China is used as an example; the difference between the reliability and operation of the bridge and the maintenance benefits and costs corresponding to different operating times is calculated, and maintenance decision analysis is performed. This can greatly extend the service time of the bridge without major repairs when the bridge reliability meets the requirements. Therefore, the BSHM operation and maintenance cost-effectiveness framework reduces resource waste, reduces operation and maintenance costs, and maximizes resource utilization and benefits, while ensuring the safety of the bridge structure and extending the service life of the bridge.

Synergizing Socioeconomic, Technological, and Environmental Factors Influencing the Adoption of High-Efficiency Irrigation Systems (HEIS): Insights from Highland Balochistan, Pakistan

Water plays a crucial role in sustaining human life and facilitating global economic development. Balochistan, the largest province of Pakistan, faces severe water scarcity primarily because of the unregulated extraction of groundwater aquifers. The scarcity of water resources can be effectively addressed by increasing Water Use Efficiency (WUE) achievable through the adoption of High-Efficienscy Irrigation Systems (HEIS). Despite substantial efforts by both public and private sectors, the adoption rate of efficient techniques remains notably low. Evaluating the factors influencing the adoption of high-efficiency irrigation methods and their resultant impact on water use efficiency is the need of time. In this study, an effort was made to study HEIS and its characteristics including the factors affecting the adoption decision, operation duration, and the economics of HEIS. The present study focused, on three main river basins using a multistage sampling technique in highland Balochistan. Three key basins namely Nari River Basin (NRB), Zhob River Basin (ZRB), and Pishin Lora Basin (PLB) Out of 18 basins in Balochistan were selected purposively. These basins are currently dealing with the issue of depleted groundwater table at a rate of more than 5-15 feet annually in the majority of their aquifers. A representative sample size of 300 farmers was chosen using a proportionate stratified sampling technique. Logistic regression was employed to analyze the data and the study findings revealed that the availability of off-farm income and higher education level were found to have highly significant and positive relationships with the likelihood of adoption. Farmers' age and the level of water scarcity were additional significant factors. Along with them, installation costs, maintenance expenses, and technological complexity were key barriers to HEIS adoption. To increase farmers' willingness to accept technology in highland Balochistan, organizations should undertake extension and educational training to increase knowledge and awareness of HEIS.

Optimal DSSC’s deployment in power system using PSO

Owing to the high cost of installation and operation, distributed flexible AC transmission system (FACTS) technology gives opportunity to provide cost-effective solution in power system operation and control. A distributed static series compensator (DSSC) is a series FACTS device and it is placed equidistantly placed on the existing line to vary the line current. Active power can be controlled in the line with the help of DSSC. This paper presents DSSC for active power flow control to enhance system loadability index and to minimize reactive power generation by generators. Since DSSC is of low power. a small value of reactance is emulated in the line. Large number of DSSC’s are distributed along the transmission line at regular intervals to realize considerable change in the current. A particle swarm optimization is implemented to determine DSSC’s emulated reactance optimally. In this condition, all the lines flowing power in their limits with increased loading condition. Maximum system loadability index is evaluated by employing optimal number of DSSC’s on the line. A multiobjective problem is formulated. One objective function is formed such that no line would become overloaded even when loading is increased. Other objective is formulated to minimize reactive power generation by generators. A compromise solution is investigated for optimally connected of DSSC devices to achieve both the objective functions. IEEE 14 bus system is taken for MATLAB simulation of DSSC compensated system.

Framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households

This review outlines a framework for tailoring consumer-centric communication to boost solar energy adoption in U.S. households. In response to growing environmental concerns and the need for sustainable energy solutions, this framework is designed to enhance residential solar adoption rates by customizing communication strategies to diverse consumer profiles. By leveraging consumer behavior insights, the framework addresses critical factors such as individual perceptions, social influences, financial considerations, and technological barriers that affect consumers' solar adoption decisions. The proposed framework emphasizes understanding various consumer segments, including early adopters, environmentally conscious individuals, and budget-focused households, to design targeted communication strategies. It integrates key elements like demographic analysis, psychographic profiling, and attitudinal segmentation to build a comprehensive picture of potential solar consumers. Using these insights, the framework proposes tailored messaging that resonates with each segment’s unique motivations and concerns, such as energy cost savings, environmental impact, and long-term investment value. To maximize engagement, the framework recommends deploying these tailored messages across a mix of digital and traditional media channels, ensuring optimal reach and relevance. Social media, for instance, can be effectively used to reach environmentally conscious and tech-savvy segments, while community events and local partnerships may better engage older or budget-focused demographics. Moreover, the framework advocates for the use of testimonials, data visualization, and interactive content to enhance credibility and user experience, further breaking down perceived barriers such as installation costs, maintenance concerns, and technology reliability. By focusing on consumer-centric communication, this framework aims to increase awareness, interest, and trust in solar energy solutions, thereby fostering a supportive environment for adoption. It also highlights the importance of iterative feedback mechanisms to continuously refine strategies and address emerging consumer needs. Ultimately, this framework serves as a strategic guide for policymakers, marketers, and solar companies to drive meaningful consumer engagement, boost conversion rates, and contribute to the broader goal of achieving sustainable energy adoption across the U.S. residential sector.

Survey Analysis Of Real Demand Of The Non Customer Community In Perumda Tirtauli Pematangsiantar City

This research aims to find out the real demand survey of the non-customer community of Perumda Tirta Uli, Pematang Siantar City, as well as to find out the reasons why they are not willing to register as customers of the company. The research design is a descriptive method with an in-depth survey. Data collection techniques through questionnaires, interviews and documentaries. Data analysis techniques with stages of data tabulation, data compilation, data analysis of demand factors, supply, socio-economics, customer willingness to pay for water and customer ability to pay water bills. The research results concluded: (1) the reason people were not willing to register as customers was because from the start they had used drilled wells (35%), because installation costs were high (19%), and distribution pipes were far away (6%). while the percentage of reasons for the water not being clear is low (6%), water distribution is often jammed (16%) and the reason for not being able to pay is only (14%). non-customers who have never applied for installing a water connection pipe (92.06%), only 7.94% have applied but it was canceled. It is recommended that the use of drilled wells must be controlled by the regional government through public awareness outreach and/or the issuance of Regional Regulations, so that underground water extraction (ABT) is not excessive

Triple Glazing Performance for Human Thermal Comfort: Systematic Literature Review and Bibliometric Quantitative Analysis

This study investigates the development and performance of triple-glazing technologies in buildings, highlighting the increasing need for human thermal comfort. Despite the popularity of bibliometric analysis in business research, there remains a lack of quantitative analysis and in-depth evaluation specific to glazing technologies. This research employs Systematic Literature Review (SLR) and Bibliometric-Quantitative Analysis (BQA) methods, utilizing the PRISMA framework to select and screen 214 articles. Descriptive analysis and scientometric statistics are conducted using bibliometric software. Key findings reveal trends in thermal comfort, environmental impacts, energy performance, and cost-effectiveness of triple-glazing technologies. The analysis identifies primary challenges, such as managing condensation, balancing insulation with solar gain, and addressing the complexities of installation and initial costs. The research aims to provide a comprehensive understanding of current advancements and the factors influencing the adoption and implementation of triple-glazing technologies, thereby contributing valuable insights to sustainable building practices.

Integrated IoT-Based Fire Prevention and Evacuation System for High-Rise Buildings

Conventional fire protection systems, characterized by low installation costs, often lack the sophistication to provide optimal protection, especially in high-rise buildings. This study aims to refine the operational productivity concerning fire prevention and evacuation by embracing a detailed fire safety framework that leverages Internet of Things (IoT) capabilities. This prototype integrates SMS and phone call alerts to facilitate timely response in case of fire detection. The system utilizes three key sensors: a KY-026 flame sensor module, an MQ-2 gas and gas sensor, and an LM35 temperature sensor. Testing results indicate significant sensor value variations between normal and fire conditions. The KY-026 flame sensor module, for instance, exhibited an average reading of 137.3 under normal conditions and 895.2 during fire detection. Similarly, the MQ-2 sensor recorded 1234.7 ppm and 4237.8 ppm, respectively. The LM35 temperature sensor measured 28.34°C and 48.46°C under normal and high-temperature conditions. Despite the sensors showcasing commendable efficacy, they displayed a minor error margin fluctuating between 0.04% and 1.08%.