• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.5
• /
• pp.153-160
• /
• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.

• New & Renewable Energy
• /
• v.7 no.2
• /
• pp.10-17
• /
• 2011

Korea is the 11th largest energy consumption country and 96% of its total energy consumption depends on imports from overseas. Therefore it is a very important task to secure renewable energy sources which can reduce both the carbon-dioxide emission and dependency on overseas energy imports. Among the various renewable energy sources, organic wastes are important sources. In Korea, 113 million toe of methane is generated from organic wastes annually, but only 3.7% is effectively used for energy conversion. Thus, it is very important to make better use of organic wastes, especially for power generation. The goals of this project are to develope the fuel supplying system of Bio Gasturbine (GT) for 5MW-class co-generation system. The fuel supplying system mainly consists of $H_2S$ removal system, Bio Gas compression system, Siloxane removal system and moisture separating systems. The fuel requirement of 5MW-class GT is at around 60% of $CH_4$, $H_2S$ (<30 ppm), Siloxane(<10 mg/$nm^3$) and supply pressure (> 25 bar) from biogas compressor. Main mechnical charateristics of Bio Gasturbine system have the specific performance; 1) high speed turbine speed (12,840 rpm) 2) very clean emmission NOx (<50 ppm) 3) high efficiency of energy conversion rate. This paper focuses on the development of design technology for food waste biogas pretreatment system for 5MW-class biogas turbine. The study also has the plan to replace the fuel of gas turbine and other distributed power systems. As the increase of bioenergy, this system help to contribute to spread more New & Renewable Energy and the establishment of Renewable Portfolio Standards (RPS) for Korea.

• KIEAE Journal
• /
• v.11 no.6
• /
• pp.133-138
• /
• 2011

This paper presents energy self-sufficiency simulated in municipal wastewater treatment plants (WWTPs) by adopting solar energy production systems that were simulated by varying azimuth and super-hydrophilic coating on the surface of photovoltaic (PV). Relative to the national average energy consumption in WWTPs, the employment of 100 kW PV system was simulated to achieve 2.75% of energy self-sufficiency. The simulated results suggested that the installation of PVs toward South or Southwest would produce the highest energy self-sufficiency in WWTPs. When super-hydrophilic coating was employed in the conventional PV, 5% of additional solar energy production was achievable as compared to uncoated conventional PV. When 100 kW of PV system was installed in a future test-bed site, Kihyeung Respia WWTP located in Yongin, South Korea, the energy self-sufficiency by solar power was simulated to be 1.77%. The simulated solar power production by azimuth and super-hydrophilic coating will be useful reference for practitioners in designing the solar PV systems in the WWTPs.

• KIEAE Journal
• /
• v.8 no.4
• /
• pp.19-27
• /
• 2008

The carbon dioxide is brought from the energy consumption and regarded as a criteria material to estimate the Global Warming Potential. Building shares about 30% in national energy consumption and affects to environment as much as the energy consumption. But there is not enough data to forecast the amount of the carbon dioxide during the maintenance stage. Various factors are related with the energy consumption and carbon dioxide emission such as the physical area, the building exterior area, the maintenance type and location. Among these factors, the building carbon-dioxide emission can be estimated by the overall building characteristics such as the maintenance area, the number of household, the heating type, etc., The physical amount such as the thickness of the insulation and window infiltration could explained the limited scope and might not be use to estimate the total carbon-dioxide emission energy because the each value could not include or represent the overall building. In this paper, it provided the estimation model of the carbon-dioxide emission, explained by the overall building characteristics. These factors are shown as the maintenance area, no. of household, the heating type, the volume of the building, the ratio of the window to wall area etc., For providing the estimation model of th carbon-dioxide emission, it conducted the corelation analysis to filter the variables and suggested the estimation model with the power model and multiple regression model. Most of the model have a good statistics and fitted in the curve line.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2016.10a
• /
• pp.893-895
• /
• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• Journal of Convergence Society for SMB
• /
• v.2 no.2
• /
• pp.13-19
• /
• 2012

Various quality requirements have increased in developing embedded systems. One of those requirements in mobile embedded system is to reduce energy consumption because of limited power supply. Especially as the complexity of embedded software is increased, the interests of the software energy consumption analysis is also increased. In recent years, some studies has been carried out model-based analysis because it can be able to reflect energy consumption requirements in design phase. In this paper, we proposes a model-based energy analysis technique using SysML parametric diagram. The proposed technique designs software activity model using characteristics of parametric diagram, and then energy consumption value by the execution of the model is derived. Our technique has merit that can perform tradeoffs analysis for energy quality property between different implementations.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.8
• /
• pp.3821-3841
• /
• 2019

Cloud radio access networks (C-RANs) have been regarded in recent times as a promising concept in future 5G technologies where all DSP processors are moved into a central base band unit (BBU) pool in the cloud, and distributed remote radio heads (RRHs) compress and forward received radio signals from mobile users to the BBUs through radio links. In such dynamic environment, automatic decision-making approaches, such as artificial intelligence based deep reinforcement learning (DRL), become imperative in designing new solutions. In this paper, we propose a generic framework of autonomous cell activation and customized physical resource allocation schemes for energy consumption and QoS optimization in wireless networks. We formulate the problem as fractional power control with bandwidth adaptation and full power control and bandwidth allocation models and set up a Q-learning model to satisfy the QoS requirements of users and to achieve low energy consumption with the minimum number of active RRHs under varying traffic demand and network densities. Extensive simulations are conducted to show the effectiveness of our proposed solution compared to existing schemes.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.24 no.4
• /
• pp.297-305
• /
• 2012

In recent large-scale semiconductor manufacturing clean rooms, the energy consumption of outdoor air conditioning systems to heat, humidify, cool and dehumidify incoming outdoor air represents about 45% of the total air conditioning load required to maintain a clean room environment. Therefore, the energy performance evaluation and analysis of outdoor air conditioning systems is useful for reducing the outdoor air conditioning load for a clean room. In the present study, an experiment was conducted to compare the energy consumption of outdoor air conditioning systems with a simple air washer, an exhaust air heat recovery type air washer and a DCC return water heat recovery type air washer. It was shown from the present lab-scale experiment with an outdoor air flow of 1,000 $m^3/h$ that the exhaust air heat recovery type and DCC return water heat recovery type air washer outdoor air conditioning systems were more energy-efficient for the summer and winter operations than the simple air washer outdoor air conditioning system and furthermore, the DCC return water heat recovery type one was the most energy-efficient in the winter operation.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.4 no.4
• /
• pp.258-263
• /
• 2011

In this paper, a high quality battery charge-discharge controller for new & renewal energy power generation system is designed. The proposed new controller has a function to manipulate the battery charging current precisely and it is suitable for various batteries including Lead-Acid battery generally used for solar power generation system. LCD display function is implemented to enhanced the user's convenience and minimization of standby power consumption is realized by optimal design using CAD tools.

• KEPCO Journal on Electric Power and Energy
• /
• v.6 no.4
• /
• pp.405-411
• /
• 2020

In this paper, we designed the COS MEMS system for sensing the falling detection and explosive noise of fuse link in COS (Cut Out Switch) installing on the power distribution. This system analyzed the failure characteristics and an instantaneous breakdown of power distribution. Therefore, our system strengths the industrial competence and guaranties the stable power supply. In this paper, we applied BLE (Bluetooth Low Energy) technology which is suitable protocol for low data rate, low power consumption and low-cost sensor applications. We experimented with LSM6DSOX which is system-in-module featuring 3 axis digital accelerometer and gyroscope boosting in high-performance mode and enabling always-on low-power features for an optimal motion for the COS fuse holder. Also, we used the MP34DT05-A for gathering an ultra-compact, low power, omnidirectional, digital MEMS microphone built with a capacitive sensing element and an IC interface. The proposed COS MEMS system is developed based on nRF52 SoC (System on Chip), and contained a 3-axis digital accelerometer, a digital microphone, and a SD card. In this paper of experiment steps, we analyzed the performance of COS MEMS system with gathering the accelerometer raw data and the PDM (Pulse Data Modulation) data of MEMS microphone for broadcasting the failure of COS status.