Energy Consumption Parameters Research Articles

APPLICATION OF ASCA TO SOLVE THE PROBLEMS OF HARMONIC DISTORTIONS SOURCES IDENTIFICATION

One of the most important components of the electricity market is its instrumental support, which is a set of systems, instruments, devices, communication channels, algorithms, etc. To control and manage the parameters of energy consumption and energy supply. The basis for the formation and development of instrumental support is the automated systems of commercial account of electric power (ASCA). In addition to the tasks of monitoring energy consumption and power supply, it is also advisable to use ASCA to solve the tasks of monitoring the power quality, identifying the culprits of harmonic distortions and determination the degree of participation of each element of the distribution system in the distribution of higher harmonics power in order to improve the quality and reliability of power supply consumers and adjust the amount of payment for consumed electricity. This paper discusses the issues of the possibility of ASCA for monitoring the harmonic set of voltage and current curves in distribution systems in order to identify and assess of harmonic distortion sources. The block diagram of an industrial facility ASCA and its main components are considered. An algorithm for expanding the functionality of electricity meters is proposed, which makes it possible to control the harmonic composition of the supplied power, to register the power and energy of higher harmonics. Considered are the issues of using counters with recording the power and energy of higher harmonics as part of the ASCA.

Design of Multi-Layer Protocol Architecture using Hybrid Optimal Link State Routing (HOLSR) Protocol for CR Networks

There is a lack of spectrum due to the rising demand for sensing device communication and the inefficient use of the existing available spectrum. Through opportunistic access to licenced bands, which does not obstruct the primary sensory users (PU), it is feasible to enhance the inefficient use of the current sensor device frequency spectrum. Cognitive settings are a demanding environment in which to carry out tasks like sensor network routing and spectrum access since it is difficult to access channels due to the presence of PUs. The basic goal of the routing problem in sensor networks is to establish and maintain wireless sensor multihop paths between cognitive sensor nodes. The frequency to be used as well as the number of hops at each sensor node along the path must be determined for this assignment. In order to improve performance while using less energy, scientists suggested a unique adaptive cross-layer optimisation subcarrier distribution technique with the HOLSR protocol for wireless sensor nodes. Throughput and energy consumption parameters are used to analyse the sensor network architecture protocol that has been developed. The energy usage of the sensor nodes in the network has increased by 50%. The performance of the proposed HOLSR algorithm is assessed using the simulation results, and the results are contrasted with those of a conventional multicarrier (MC) system in terms of bit error rate and throughput.

Adaptive thermal comfort model and active occupant behaviour in a mixed-mode apartment. A synergy to sustainability.

The adaptive comfort model was mainly developed for naturally ventilated buildings, but few recent studies explore its applicability in mixed-mode buildings. The present study uses the adaptive thermal comfort model (EN15251) in a dynamic thermal simulation (EnergyPlus) parametric analysis of a mixed-mode apartment, in an attempt to determine the energy savings. Two simulation models were created. The first one makes full use of mechanical heating and cooling systems when the required temperature is not reached. The second one adopts a hybrid approach, resembling the existing operation of residential buildings in Greece. The primary regulator of the indoor conditions is the occupant through his/her interaction with the building shell and only when this is not effective the mechanical systems are activated. The internal thermal gains in both models were determined based on the detailed recording of the real conditions in a typical apartment. Many design parameters (window size, thermal insulation position and thickness, orientation, airtightness, glazing properties and shading) along with different occupant behavioural patterns (derived from a questionnaire campaign) have been examined in sensitivity analysis. Machine learning algorithms, such as the Random Forest, were also incorporated to identify most important parameters. Results indicate that airtightness, occupant behaviour and shading are the most important parameters for primary energy consumption for cooling, while airtightness, window size and shading for the total of heating and cooling primary energy consumption.

Smart Energy Meter Using IoT

The increasing demand for efficient energy management and the rapid growth of Internet of Things (IoT) technology have led to the development of smart energy metering systems. This abstract presents a conceptual design of a smart energy meter that leverages IoT capabilities for enhanced monitoring, control, and optimization of energy consumption in residential and commercial settings. The proposed smart energy meter integrates advanced metering infrastructure (AMI) with IoT sensors, connectivity, and data analytics to enable real-time energy monitoring, intelligent load management, and energy efficiency improvements. The meter collects data on energy consumption, voltage levels, power quality, and other relevant parameters, which are transmitted wirelessly to a central monitoring system. By utilizing IoT connectivity, the smart energy meter facilitates two-way communication, allowing consumers to access their energy usage data remotely via mobile applications or web portals. This empowers users to monitor their energy consumption patterns, set energy-saving goals, and receive personalized recommendations for optimizing their energy usage. Furthermore, the smart energy meter employs data analytics algorithms to analyze the collected energy data and generate actionable insights. These insights can be used to identify energy wastage, detect anomalies, and suggest energy-saving strategies. Additionally, the meter can automatically adjust energy loads based on peak demand periods, optimizing energy distribution and reducing overall energy costs. The proposed smart energy meter offers several benefits, including real-time monitoring, increased energy efficiency, cost savings, and reduced environmental impact. It enables consumers to make informed decisions about their energy usage, promotes sustainable practices, and contributes to the development of smarter and greener communities.

Integrating interval type‐2 fuzzy logic and modified dingo optimization for node ranking and energy efficient MANET

AbstractIn mobile ad hoc networks (MANET), the mobility of the node changes dynamically, thus affecting the stability of the connection and increasing energy consumption. The heterogeneity of the MANET nodes in terms of mobility, battery capacity, and processing capabilities often complicates the routing process and results in increased energy consumption and traffic overhead. To overcome the challenges associated with diverse MANET nodes, this article presents a hybrid interval type‐2 fuzzy logic controller and modified dingo optimization (IT2FLC‐MDO) algorithm‐based optimized link‐state routing protocol. In our proposed IT2FLC‐MDO algorithm, multiple Quality of Service (QoS) parameters (node distance, lifetime, residual energy, speed, queue length, etc) are integrated into a single multicriteria node metric to improve the multipath route analysis and topology formation. In this way, the need to broadcast multiple parameters for each session is reduced thus minimizing the control overhead. The proposed routing protocol mainly aims to achieve a tradeoff between the QoS metrics and energy consumption during data transmission, addresses the QoS and energy issues associated with multipath routing, and uses a multicriteria node metric. The proposed IT2FLC‐MDO protocol takes both the energy consumption and QoS parameters into account, thus achieving a balance between the energy consumption and QoS. The cluster head primarily chooses the path with the highest link quality to ensure reliable data transmission. The proposed IT2FLC‐MDO protocol's efficiency is demonstrated using several performance measures such as packet delivery ratio, average end‐to‐end delay, and normalized control overhead. The simulation results indicate that the proposed algorithm can handle MANET uncertainty while minimizing control overhead. The results show that the proposed algorithm offers a packet delivery rate nearly equal to 96% and improves the MANET's lifetime by up to 59%.

Optimization and control strategy of BOG re-liquefaction process for LNG vessels

The re-liquefaction of BOG is a necessary part of the safety and security of LNG cargo ships. And the volatility of BOG will fluctuate with the sea conditions of ship transportation, so how to deal with the fluctuation of the re-liquefaction system and improve the liquefaction capacity is a problem that cannot be ignored in the operation of the liquefaction system; In this paper, a BOG buffer tank is added to the Reverse Brayton Process. Moreover, a control strategy is proposed to optimize the liquefaction process. The proposed four possible operating conditions' efficiency,the specific energy consumption (SEC), liquefaction rate and main heat exchanger parameters were simulated and analyzed by using Aspen HYSYS software. According to the results, the specific energy consumption of the basic re-liquefaction process is 1.329 kWh/kgLNG, which is 5.7% less than that of the reference process; In the study of flow fluctuation, after adopting the control strategy, the specific energy consumption of the whole process is 1.29 kWh/kgLNG, with 15.6% efficiency and 99.5% liquefaction rate. The whole system can be maintained at a low specific energy consumption level when the feed gas volume is fluctuating. The analyses of various BOG composition and the parameters of the main heat exchanger show that the regulation of the inlet gas volume can make the primary re-liquefaction process flexible and maintain the heat exchanger in a good working condition; Even if the system is in a worse working condition, the inlet gas volume can be improved by 12.2% compared with the reference process.

The Evaluation of IEEE 802.11ah Performance Based on the Effect of Mobility, Node’s Number, and Traffic Using the Markov Chain Model

The Internet of Things (IoT) is currently growing rapidly and one of the technologies supporting it is wireless fidelity (Wi-Fi) standard IEEE 802.11ah. This technology supports mobility and has a large number of nodes or devices with small energy consumption; hence it is capable of functioning for a long time. In this study, three work scenarios were proposed, namely 1) the mobility, which involves changing the distance between the access point (AP) and the nodes, 2) changing the node’s number, and 3) testing the variations in traffic by changing the collision possibilities and the RAW (Restricted Access Window) (the full title). slot duration in order to analyze the IEEE 802.11ah network performance parameters. The results showed that there was a decrease in throughput, an increase in energy consumption, and a delay due to changes in the nodes’ number and movement/mobility. Also, the variation in traffic by changing the collision probability causes a change in throughput, hence when the collision probability is large, the throughput decreases, while the delay value increases, and vice versa. In conclusion, this study proved that changes in the nodes’ number, movement/mobility, and traffic collision probability affected the IEEE 802.11ah network's performance in throughput, delay, and energy consumption parameters.

Performance Analysis of Voltage-Controlled Magnetic Anisotropy MRAM-Based Logic Gates and Full Adder

In the last decade, spintronics technology has been extensively researched for future non-volatile memories use. VCMA-MRAM exploits the voltage-controlled magnetic anisotropy (VCMA) principle to write data into magnetic tunnel junctions. In this paper, analysis, and comparison of the Energy consumption and delay performance parameter of Voltage Controlled Magnetic Anisotropy (VCMA) based logic gates are being done. This paper has implemented AND/NAND, OR/NOR, XOR/XNOR, and Full Adder using CMOS/VCMA models. Three models of VCMA have been used in this paper for performance analysis and comparison—STT-assisted thermally activated VCMA, STT-assisted precessional VCMA, and Precessional VCMA. The performance of these circuits has been analyzed, and the results have been compared within VCMA modules. Performance parameters like energy consumption and delay has been analyzed in this paper. From the gate latency (delay) and energy consumption analysis, it has been derived that the precessional VCMA-based gates perform better in these domains as compared to the other VCMA model-based gates. It is observed that performance improvements of ∼20% and ∼60% have been seen over STT-assisted precessional VCMA and STT-assisted thermally activated VCMA based gates in terms of gate latency at 1.2 V operating voltage and improvement of around 80% is seen in terms of energy over STT assisted thermally activated magnetic anisotropic MRAM, respectively.

Comparing energy profiles of different building types by determining their sensitivities

ABSTRACT The main purpose of this study is to understand the energy consumption profiles of different building types by identifying and comparing their energy consumption parameters with their sensitivities to each other while indicating the long-term energy consumption implications on energy cost and CO2 emissions. A duplex house, apartment building, convention center, and school building were selected to develop dynamic simulation models for the investigation. The annual electric and gas demand of buildings were calculated and compared based on their energy usage intensity (kWh/m2), emission intensity (kg CO2/m2), and cost intensity (USD/m2) with their sensitivity to selected parameters. Further, the 50-year longevity of the buildings that include the selected materials’ production and demolition stages were included to estimate the lifetime. Also, retrofitting strategies including renewable technology integration were developed for each building type separately based on their energy concerns. The average energy consumption of parameters from highest to lowest are space heating, area lights, equipment, space cooling, hot water, fans, and pumps, respectively. The critical primary energy consumption of the educational building and convention center is 43% and 38% from equipment and 30% from space heating for residential buildings. A large portion of the emission is allocated to energy consumption during the lifetime of buildings, mainly due to electricity consumption. The energy costs mainly are also electricity related. Although the effects of retrofit strategies are different depending on the energy behavior of buildings, the most effective scenario is envelope retrofitting, followed by lighting improvement.

Comparative study of different bread baking methods: Combined ohmic – infrared, ohmic – conventional, infrared – conventional, infrared, and conventional heating

Developing bakery techniques is essential to improve the qualitative and quantitative parameters of the bread baking process. In this study, the influence of different baking methods was evaluated on baking kinetics, energy consumption, mechanical properties, volume expansion, and sensory parameters of the bread. The results showed that ohmic-conventional method increased the baking time compared with conventional baking. The baking time of the combined infrared–conventional was 63.0–73.7% lower than the conventional method. The maximum values of specific energy consumption and volume expansion were found in the combined ohmic-conventional method. The specific volume expansion, firmness, and shearing force of the baked samples under infrared-conventional conditions were higher than the infrared and infrared-ohmic conditions. The maximum energy efficiency was obtained using the oven-infrared method. The sensory evaluation indicated that the baked samples in the infrared-conventional method had higher scores of taste and flavor than the other baking methods (p < 0.05). Future studies to optimize the baking parameters on bread quality are recommended for improved results.

A Forecasting Approach for IoT-Based Energy and Power Quality Monitoring in Buildings

This paper proposes a novel energy consumption and power quality forecasting technique based on the combination of poly-exponential (PE) and random forest (RF). This technique paves away seamlessly integrating the forecast to the Internet-of-things (IoT)-based monitoring system due to its capability of improving computational efficiency. For the experimental setup, the monitoring system in a building supplied the data for forecasting consisting of daily energy consumption and power quality parameters. The developed algorithm for efficient computational forecasting has already considered the space limitation in online real-time monitoring with a minor effect on the model prediction error (less than 0.3% in our experimental setup). The algorithm can alert the user once an indication of energy waste (more than the determined boundary) or the actual voltage violated the tolerance might occur. An experimental dataset taken from the Bappeda Pontianak building in West Kalimantan, Indonesia, shows that the proposed algorithm required fewer samples than the RF yet could predict the daily energy consumption and voltage inside a building accurately up to 96%. Note to Practitioners—As the energy and power quality monitoring using IoT is applied for buildings nowadays, how to handle the big data and forecast the energy and power for evaluation and strategy in buildings is significant. A standard solution is to apply sophisticated technology with a high computational resource. However, this will increase the cost either in the investment or maintenance. The algorithm in this paper is a new approach for IoT-based monitoring and forecasting of energy consumption and power quality by combining PE and RT models, which is relatively cheap in computational complexity yet does not require much space to store in an online setup. The proposed algorithm first creates the model with the RT algorithm and then tracks the residual generated by this algorithm with the PE model and extended Kalman filter. We then show that this model can predict the daily energy consumption accurately. The algorithm based on this model can be integrated into the monitoring system and advise the user when the current energy consumption has passed the “normal” energy consumption concerning the information from previous days. Experiments on a building located in Pontianak show that this algorithm is applicable and can precisely predict daily energy consumption and power quality with a limited amount of data. In future research, we plan to expand this technique further, such that it is not only for monitoring purposes but also for control.

Sensitivity Analysis of Factors Influencing Rural Housing Energy Consumption in Different Household Patterns in the Zhejiang Province

Unlike urban dwellings, it is very common for elderly people to stay at home alone in Chinese rural families, and some families have three generations in the same house who are in different situations, and their different family patterns lead to different highly sensitive parameters of building energy consumption. This paper first selects the three most common family patterns based on a questionnaire survey. The measured energy consumption behavior and electrical parameters, energy consumption time, and basic building parameters were input into DesignBuilder to build three building simulation models, and these were verified by comparing the predicted and measured values of the residential month-by-month electricity consumption. The global sensitivity analysis was then conducted using DesignBuilder software to determine the interactions between the variables by using the second-order Sobol index for cooling load, heating load, and comfort of the models to obtain standardized regression coefficients (SRC) for each factor to determine the most sensitive parameters. The results show that the different household patterns had little influence on the ranking of highly sensitive factors for heating and cooling, but annual electricity consumption and discomfort in different household patterns had a significant influence on the ranking of highly sensitive factors. For example, model 1 showed the most sensitivity to general lighting power density when optimizing the total amount of electricity was the goal, while the one that had the greatest degree of influence on the total amount of electricity in model 2 and model 3 was equipment power density.

Passive solar sunspace in a Tibetan buddhist house in Gannan cold areas: Sensitivity analysis

Attached sunspace is an effective energy system of passive solar energy utilization, which has been extensively employed in many nations. This study investigated, tested, and analyzed a representative Tibetan Buddhist house with attached sunspace in Gannan. The thermal comfort of the building was evaluated. The influence of building orientation, depth of sunspace, glazing type, insulation type and thickness of external wall and roof, thickness of wall and roof on thermal comfort and energy consumption were ranked by sensitivity analysis and numerical simulation. Local sensitivity analysis was also performed on the components with high sensitivity, and suggestions were provided for the design of attached solar houses in this area. The results show that the most relevant parameters of thermal comfort and energy consumption are roof insulation thickness and roof thickness. The optimum thickness of rammed earth is 1.1–1.2 m, and the design range of thermal insulation thickness is 10–90 mm, of which 10–40 mm is the best. At the same time, the best orientation of the attached sunspace is south, and the glazing type has a little effect on thermal comfort and limited impact on energy consumption.

Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database

Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.

Statistical Optimization of E-Scooter Micro-Mobility Utilization in Postal Service

New-generation technologies on vehicles provide many advantages in terms of cost, time, and the environment in the transportation, logistics, freight, and delivery service sectors. This study aimed to measure the effect of using e-scooter vehicles in mail delivery on the energy cost and delivery time in Turkey. Considering the number of test drives in e-scooter applications of potential regions, the amount of energy consumption and driving time data were used. The number of test drives for each e-scooter was assumed as a package or postal delivery amount. The methodology of this study consisted of measuring the effect of input parameters on output variables using the linear response optimization regression method and minimizing the amount of energy consumed and delivery time. The nine input variables and two output variables based on the test drive were analyzed in this study. The distance to the delivery address, region where the delivery address was located, and temperature were found to be statistically significant predictors of the amount of energy required for delivery. The statistical significance levels of time zone, distance, temperature, rainfall, and region factors were calculated as 0.053, 0.001, 0.0033, 0.044, and 0.042, respectively. Driver age, data time zone, distance, wind speed, and delivery region factors affected the time required for delivery with an e-scooter. The statistical significance levels of these factors were calculated as 0.02, 0.001, 0.001, 0.043, and 0.001, respectively. Additionally, N (p; 0.042), NE (p; 0.030), and W (p; 0.057) wind directions directly influenced the delivery time. SE (p; 0.017) was the only wind direction that statistically significantly affected energy consumption. The objective functions were estimated by calculating the optimum values of the input parameters for the minimum energy consumption and delivery time. The optimum values of both input and output variables were calculated based on the desirability values of the optimization models, which were in the optimum solution set. The average data of the optimum values of the objective functions were computed as 2.83 for the number of tests and TRY 0.021 (per 0.098 km) for the energy cost required for delivery. The necessity of using e-scooters, which are more environmentally friendly, economical, and time-saving than traditional delivery vehicles, in postal delivery service is among the prominent suggestions of this study.

Deep reinforcement learning based optimization of automated guided vehicle time and energy consumption in a container terminal

The energy efficiency of port container terminal equipment and the reduction of CO2 emissions are among one of the biggest challenges facing every seaport in the world. The article presents the modeling of the container transportation process in a terminal from the quay crane to the stack using battery-powered Automated Guided Vehicle (AGV) to estimate the energy consumption parameters. An AGV speed control algorithm based on Deep Reinforcement Learning (DRL) is proposed to optimize the energy consumption of container transportation. The results obtained and compared with real transportation measurements showed that the proposed DRL-based approach dynamically changing the driving speed of the AGV reduces energy consumption by 4.6%. The obtained results of the research provide the prerequisites for further research in order to find optimal strategies for autonomous vehicle movement including context awareness and information sharing with other vehicles in the terminal.

Моделювання витрат та вимірювання електричної енергії електродвигунів

The activity of determining the energy efficiency of electric motors includes several stages of work. These include modeling the operation of engines with the help of specialized software products, which allows you to determine the necessary parameters of electrical energy consumption, load, and rotational parameters in various operating modes. However, the most effective way is to obtain the real operating characteristics of the electric motor on test stands. This is due to many factors, from mechanical malfunctions to inconsistencies in the technical characteristics declared by the manufacturer. At the same time, both for the first and other methods, the problem lies in the optimization of the measuring equipment, which requires reducing the error, increasing the speed and sensitivity of the measuring transducers. The parameters that require special attention include the consumption of electrical energy, which in certain areas of industry is decisive during the design of technological sites, determination of industrial policy, production volumes, etc. This article proposes a way to establish the relationship between these parameters, the practical value of which, in different operating modes of the electric motor, will make it possible to evaluate both the economic efficiency of the electric machine and the possible risks associated with safe further operation, the period of operation and the ability to create the necessary acceleration moments at given loads.

A Hybrid Improved Manta Ray Foraging Optimization With Tabu Search Algorithm for Solving the UAV Placement Problem in Smart Cities

The concept of smart cities is to enhance the life quality of residents and provide efficient services by integrating advanced information and communication technologies, autonomous robots, Internet of Things (IoT) devices, etc. Unmanned Aerial Vehicles (UAVs) are a class of autonomous flying mobile robots that bring a lot of benefits to smart cities due to their mobility, accessibility, autonomy, and many other advantages. Their integration allows for accomplishing hard and complex tasks that humans or other entities are not able to complete. In most applications, multiple connected UAVs are required to build a network under which missions are completed and tasks are shared. This network can be established by finding the optimal UAV placement that meets some requirements such as user coverage, UAV connectivity, energy, and load distribution. In this paper, we present a hybrid algorithm, called IMRFO-TS, based on the hybridization of Improved Manta Ray Foraging Optimization (IMRFO) with the Tabu Search (TS) algorithm for solving the UAV placement problem in a smart city. First, the tangential control strategy is incorporated into the original MRFO algorithm to enhance its convergence speed and explore the search space effectively. Second, The TS algorithm is hybridized with the IMRFO algorithm to increase the exploitation capability of IMRFO and improve the best solution (placement quality) obtained after each iteration. The performance of the proposed IMRFO-TS algorithm is validated using 52 benchmarks considering the fitness value, coverage, connectivity, energy consumption, and load distribution parameters. Compared to eight well-known optimization meta-heuristics such as the original MRFO, TS algorithm, Bat Algorithm (BA), Firefly Algorithm (FA), Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), Whale Optimization Algorithm (WOA), and Reptile Search Algorithm (RSA), the results of the experiments revealed the significant superiority of the proposed IMRFO-TS algorithm by obtaining promising solutions (optimal positions of UAVs) in the majority of the cases.

Intelligent energy-based product quality control in the injection molding process

Energy is a critical resource for powering modern society, supporting economic growth, and meeting basic human needs. In fact, energy efficiency is an important factor for companies looking to remain competitive in the market. This need is particularly more important in energy-intensive industries, such as plastics manufacturing by process injection molding, where energy costs can account for a significant portion of the overall operating costs. By investing in smart energy-efficient technologies based on artificial intelligence tools and on good practices manufacturing, companies can reduce their energy consumption, improve their environmental performance, and enhance their sustainability. In this paper, an artificial neural network, trained on experimental dataset, has been used for modelling the relationships between energy consumption, product quality, and process setting parameters. Then, an energy control system has been building in Matlab Simulink to simulate the behaviour of real production process of polypropylene product and to identify the optimal process settings that achieve the desired level of product quality while controlling energy consumption. The proposed system demonstrated its effectiveness in the case study adopted and then can be used in others similar plastics production. Moreover, its approach can be used to develop the smart control systems for others industrial processes.

Development of Deflector Structure and Effects on Performance: Refrigerated Display Cabinet Application

Air flow is of great importance in terms of energy efficiency and homogeneity of the cooled package temperatures in open type refrigerated display cabinets (RDCs). Deflectors are critical components for air curtains which are provides air directioning to air channel and grill. A new deflector structure was investigated to increase the performance of the air curtain in this study. Current design deflector structure (Design 1) and new design deflector structure (Design 2) were compared for temperatures of air off-air on and energy consumption parameters. Airflow in the new design deflector structure was tested under Class-3 conditions (25 °C temperature and %60 relative humidity) with ISO 23953-2 2015 standard. According to the results obtained the data compared with the current design deflector structure. The thermal performance has been increased by reducing the temperature of the air off by 1.2 °C on average, the air on temperature by an average of 2.05 °C and the average package temperature by %17.8 with the new deflector design. Cooling performance was increased using new deflector structure with effective airflow between the air off and air on. In addition, thanks to Design 2, the annual energy consumption of RDC has been reduced by 10.17% and accordingly the CO2 emissions released to the environment have been reduced at the same rate.