• Proceedings of the Korea Information Processing Society Conference
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• 2019.10a
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• pp.585-588
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• 2019

Predictions of Energy Consumption for Industries gain an important place in energy management and control system, as there are dynamic and seasonal changes in the demand and supply of energy. This paper presents and discusses the predictive models for energy consumption of the steel industry. Data used includes lagging and leading current reactive power, lagging and leading current power factor, carbon dioxide (tCO2) emission and load type. In the test set, four statistical models are trained and evaluated: (a) Linear regression (LR), (b) Support Vector Machine with radial kernel (SVM RBF), (c) Gradient Boosting Machine (GBM), (d) random forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used to measure the prediction efficiency of regression designs. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.16 no.4
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• pp.31-38
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• 2020

The investment in the technology of using a combined heat source is insufficient, which utilizes the advantages of various heat sources to maximize the potential energy and at the same time increases the performance of the heat pump. In this study, as basic data for the development of a high-efficiency hybrid heat pump system that actively connects and uses various heat sources, simulations were conducted for the heat pumps in two cases where geothermal and hydrothermal heat were applied respectively. In May, COP increased by about 27.3% compared to geothermal heat. In February, the COP percentage decrease of hydrothermal heat compared to geothermal heat is -6.9%. In May, total energy consumption can be reduced by 21.1% when hydrothermal is applied compared to geothermal heat. In February, the total energy consumption increases by 3.4%.

• Journal of Information Technology Services
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• v.21 no.5
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• pp.81-94
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• 2022

This study sets the research goal of reducing energy consumption which 'H' University Industry-University Cooperation Foundation and resident companies are concerned with, as well as conducting policy research and data analysis. We tried to present a solution to the problem using the technique. The algorithm showing the greatest reliability in the power of the model for the analysis algorithm of this paper was selected, and the power consumption trend curves per minute and hour were confirmed through predictive analysis while applying the algorithm, as well as confirming the singularity of excessive energy consumption. Through an additional sub-sensor analysis, the singularity of energy consumption of the unit was identified more precisely in the facility rather than in the building unit. Through this, this paper presents a system building model for real-time monitoring of campus power usage, and expands the data center and model for implementation. Furthermore, by presenting the possibility of expanding the field through research on the integration of mobile applications and IoT hardware, this study will provide school authorities and resident companies with specific solutions necessary to continuously solve data-based field problems.

• Proceedings of the KIEE Conference
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• 2007.10a
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• pp.497-498
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• 2007

The most of important thing when we design a Wireless Sensor Network is resources. You have to consider energy efficient operation When you design Wireless Sensor Network. Because Sensor devices have a limited resources. In this paper, we proposed energy efficiency localization technique in Wireless Sensor Network. We used Cell ID technique for location search. This method can reduce power consumption and the network life time will be extension.

• Environmental and Resource Economics Review
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• v.19 no.2
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• pp.215-242
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• 2010

This study constructs a bayesian neoclassical DSGE model that applies oil usage. The model includes technology shocks, oil price shocks, and shocks to energy policies as exogenous driving forces. First, this study aims to analyze the roles of these exogenous shocks in the Korean business cycle. Second, this study examines the effects of long-term changes in the energy consumption structure, including the reduction in oil use as a share of energy consumption and improvement in oil efficiency. In the case of oil price shocks, results show that these shocks exert recessionary pressure on the economy in line with those obtained in the previous literature. On the other hand, shocks to energy policies, which reduce oil consumption per capital, result in opposite consequences to oil price shocks, decreasing oil consumption. Also, counterfactual exercises show that long-term changes in the energy consumption structure would mitigate the contractionary effects of oil price shocks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.312-334
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• 2023

In large-scale computing, cloud computing plays an important role by sharing globally-distributed resources. The evolution of cloud has taken place in the development of data centers and numerous servers across the globe. But the cloud information centers incur huge operational costs, consume high electricity and emit tons of dioxides. It is possible for the cloud suppliers to leverage their resources and decrease the consumption of energy through various methods such as dynamic consolidation of Virtual Machines (VMs), by keeping idle nodes in sleep mode and mistreatment of live migration. But the performance may get affected in case of harsh consolidation of VMs. So, it is a desired trait to have associate degree energy-performance exchange without compromising the quality of service while at the same time reducing the power consumption. This research article details a number of novel algorithms that dynamically consolidate the VMs in cloud information centers. The primary objective of the study is to leverage the computing resources to its best and reduce the energy consumption way behind the Service Level Agreement (SLA)drawbacks relevant to CPU load, RAM capacity and information measure. The proposed VM consolidation Algorithm (PVMCA) is contained of four algorithms: over loaded host detection algorithm, VM selection algorithm, VM placement algorithm, and under loading host detection algorithm. PVMCA is dynamic because it uses dynamic thresholds instead of static thresholds values, which makes it suggestion for real, unpredictable workloads common in cloud data centers. Also, the Algorithms are adaptive because it inevitably adjusts its behavior based on the studies of historical data of host resource utilization for any application with diverse workload patterns. Finally, the proposed algorithm is online because the algorithms are achieved run time and make an action in response to each request. The proposed algorithms' efficiency was validated through different simulations of extensive nature. The output analysis depicts the projected algorithms scaled back the energy consumption up to some considerable level besides ensuring proper SLA. On the basis of the project algorithms, the energy consumption got reduced by 22% while there was an improvement observed in SLA up to 80% compared to other benchmark algorithms.

• Journal of Korean Society of Transportation
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• v.28 no.2
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• pp.161-169
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• 2010

The amount of energy consumed by a transportation system in a city is influenced not just by a transportation system itself but also by city's spatial character. Yet there have been very limited studies on this subject. This study investigates the factors that influence the energy consumption by a transportation system and city's spatial character. The model of an urban area confined within a definite boundary is assumed in order to develop the relationships between the energy consumption and the transportation system. Various assumptions on the character of a city and activities are made. An equation for computation of the energy consumption is derived with such factors as modal split, densities of residents and workers, as well as technological development. Using the equation, sensitivity analyses are performed in order to identify the relationships between energy consumption and influencing factors. It is found that the modal split is the most powerful factor that determines the energy consumption by the transportation system. Yet it is also found that the densities of residents and workers and the technological factors are very important in determining the amount of energy consumption as well.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• Journal of Biosystems Engineering
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• v.8 no.2
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• pp.49-61
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• 1983

U shape multitube heat exchanger was equipped in the flue to recover the exhaust heat from the boiler system. The fluids of the exhaust heat recovery equipment were the flue gas as the hot fluid, and the water as the cold fluid. The flow geometry of the fluids was cross flow - two pass, the hot fluid being mixed and the cold fluid unmixed. The results of the theoretical and the experimental analysis and the economic evaluation are summarized as follows. 1) The heat exchanger effectiveness and the temperature efficiency of the hot fluid were about 35% when the fuel consumption rate was 140 - 150 L/15min. The temperature efficiency for the cold fluid ranged from 3.0% to 4.5%. The insulation efficiency ranged from 85% to 98%, which was better than the KS air preheater insulation efficiency of 90%. 2) The relationship between the fuel consumption rate, F, and the outlet temperature, $T_{h2}$, of the flue gas from the heat exchanger was $T_{h2}$ = 0.927F + 110. In order to prevent the low temperature corrosion from the coagulation of $SO_3$, it is necessary to maintain the fuel consumption rate above 82 L/15min. 3) The ratio of the exhaust heat from the boiler system to the total energy consumption was about 14.5%. With the installation of the exhaust heat recovery equipment, the energy recovery ratio to the exhaust heat was about 25%. Accordingly, about 3.6% of the total fuel consumption was estimated to be saved. 4) Economic analysis indicated that the installation of the exhaust heat recovery equipment was feasible to save the energy, because the capital reocvery period was only 10 months when the fuel consumption rate was 80 L/15min. 4 months when it was 160 L/15min. 5) Based on the theoretical and the experimental analysis, it was estimated to save the energy of about 18 million Won per year, if four heat exchangers are installed in a factory. 6) A further study is recommended to identify the relationship among the flow rate of the exhaust gas, the size of the heat exchanger and the capacity of the air preheater. For a maximum heat recovery from the exhaust gas an automatic control system is required to control the flow rate of the cold fluid depending on the boiler load.

• Journal of Environmental Science International
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• v.32 no.6
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• pp.465-475
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• 2023

In this study, we evaluated the treatment efficiency of livestock wastewater by altering the current density using boron-doped diamond (BDD) electrodes. As the current density was adjusted from 10 to 35 mA/cm2, the removal efficiency of organic matter increased from 22.2 to 71.5%. Similar to that of organic matter, the removal efficiency of color increased with increasing current density up to 85.7%, indicating a higher removal efficiency for color than that of organic matter. The removal efficiency of ammonia nitrogen increased from 14.6 to 53.3% as the current density increased, but it was lower than that of organic matter. In addition, the removal of organic matter, color, and ammonia nitrogen followed first-order reactions, according to the reaction rate analysis. The energy consumption ranged from 4.87 to 8.33 kWh/kg COD, and it was found that the organic matter removal efficiency was more efficient at high current densities. Based on various analyses, the optimal current density was 20 mA/cm2, and the corresponding energy consumption was 6.824 kWh/kg COD.