• Annual Conference of KIPS
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3066-3079
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• 2016

Recent years have witnessed a dramatic increase in topology research of wireless sensor networks (WSNs) where both energy consumption and survivability need careful consideration. To balance energy consumption and ensure survivability against both random failures and deliberate attacks, we resort to complex network theory and propose an energy-aware preferential attachment (EPA) model to generate a robust topology for WSNs. In the proposed model, by taking the transmission range and energy consumption of the sensor nodes into account, we combine the characters of Erdős -Rényi (ER) model and Barabasi-Albert (BA) model in this new model and introduce tunable coefficients for balancing connectivity, energy consumption, and survivability. The correctness of our theoretic analysis is verified by simulation results. We find that the topology of WSNs built by EPA model is asymptotically power-law and can have different characters in connectivity, energy consumption, and survivability by using different coefficients. This model can significantly improve energy efficiency as well as enhance network survivability by changing coefficients according to the requirement of the real environment where WSNs deployed and therefore lead to a crucial improvement of network performance.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.8
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• pp.1825-1842
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• 2013

Rapid growth of the IT industry has led to significant energy consumption in the last decade. Data centers swallow an enormous amount of electrical energy and have high operating costs and carbon dioxide excretions. In response to this, the dynamic consolidation of virtual machines (VMs) allows for efficient resource management and reduces power consumption through the live migration of VMs in the hosts. Moreover, each client typically has a service level agreement (SLA), this leads to stipulations in dealing with energy-performance trade-offs, as aggressive consolidation may lead to performance degradation beyond the negotiation. In this paper we propose a heuristic based resource allocation of VM selection and a VM allocation approach that aims to minimize the total energy consumption and operating costs while meeting the client-level SLA. Our experiment results demonstrate significant enhancements in cloud providers' profit and energy savings while improving the SLA at a certain level.

• JSTS:Journal of Semiconductor Technology and Science
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• v.16 no.3
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• pp.330-338
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• 2016

Phase change memory (PCM) has been studied as an emerging memory technology for last-level cache (LLC) due to its extremely low leakage. However, it consumes high levels of energy in updating cells and its write endurance is limited. To relieve the write pressure of LLC, we propose a delta value indicator (DVI) by employing a small cache which stores the difference between the value currently stored and the value newly loaded. Since the write energy consumption of the small cache is less than the LLC, the energy consumption is reduced by access to the small cache instead of the LLC. In addition, the lifetime of the LLC is further extended because the number of write accesses to the LLC is decreased. To this end, a delta value indicator and controlling circuits are inserted into the LLC. The simulation results show a 26.8% saving of dynamic energy consumption and a 31.7% lifetime extension compared to a state-of-the-art scheme for PCM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.996-1004
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• 2013

In this paper, we propose two clustering schemes to prolong lifetime by improving unbalance of energy consumption among sensor nodes in wireless sensor networks. The first proposed scheme make up clusters according to density of sensor nodes in initial stage of wireless sensor networks for reducing energy consumption of wireless sensor networks. After the initial stage, a cluster header is selected by a relay scheme that determines a cluster header of next round among cluster members. by estimating of energy consumption of cluster members for improving unbalance of energy consumption among cluster members.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.4
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• pp.236-240
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• 2011

Currently one of the most importance issues in industrial sector is energy cost and energy efficiency. The manufacturing plants especially have made many efforts to reduce energy cost by implementing maintenances. But in many cases, they are not aware that how much energy could be saved more. If we know the best energy consumption, which signifies energy baseline, we can control the intensity of maintenances. One way to obtain the baseline is using proper statistics from a specific plant, a sector of industry. Energy bandwidth signifies the gap between actual Specific Energy Consumption(SEC) of a certain plant and minimum SEC of the best plant, and estimate energy saving potential(ESP) is a result of bandwidth analysis. We chose a model plant and implemented some maintenance for a year, and then we obtained ESP. Additionally we could determine the decreased amount of carbon emissions from the plant using Carbon Emissions Factor(CEF) by Intergovernmental Panel on Climate Change(IPCC).

• Journal of the Korean Solar Energy Society
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• v.33 no.1
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• pp.24-31
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• 2013

More than 23% of total nation's energy is consumed by residential building and 57.2% of Korean people are living in apartment. This study was carried out to two kind of process. First, after selecting one standard apartment, our research team investigate realistic energy consumption. Second, using 3-dimension heat transfer tool(TRISCO RADICON) and building energy simulation tool(Visual DOE) As a result, amount of heating and hot-water energy is composed of above 80 percent in standard apartment. And, after applying high performance technologies to standard apartment, namely, after being green home apartment, total energy consumption is reduced by54.6 percent. Also, because of energy consumption characteristics of green home apartment, for making more high performance green home apartment, especially, we have to figure out effective method to reduce electric and hot water energy.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.214-219
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• 2008

This study is describes thermal performance of solar cooling and hot water for demonstration system with ETSC(Evacuated tubular solar collector) installed at Seo-gu culture center of Kwanju. Control condition for solar cooling and hot water system is changed by connection of auxiliary heater. Demonstration system was connected to central air conditioning system. Demonstration system was operated by two types. First type(A) was operated to cooling and hot water supply in that order. Second type(B) was operated to hot water supply and cooling in that order. As a result. it was indicated that the total solar energy consumption of (A) was 799 MJ and the solar energy consumption rate for the cooling and hot water supply was 70% and 30% respectively. Total solar energy consumption of (b) was 898 MJ and the solar energy consumption rate for the cooling and hot water supply was 31% and 69% respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3071-3095
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• 2013

Configuring optimization of wireless sensor networks, which can improve the network performance such as utilization efficiency and network lifetime with minimal energy, has received considerable attention in recent years. In this paper, a cross layer optimal approach is proposed for multi-source linear network and grid network under Rayleigh block-fading channels, which not only achieves an optimal utility but also guarantees the end-to-end reliability. Specifically, in this paper, we first strictly present the optimization method for optimal nodal number $N^*$, nodal placement $d^*$ and nodal transmission structure $p^*$ under constraints of minimum total energy consumption and minimum unit data transmitting energy consumption. Then, based on the facts that nodal energy consumption is higher for those nodes near the sink and those nodes far from the sink may have remaining energy, a cross layer optimal design is proposed to achieve balanced network energy consumption. The design adopts lower reliability requirement and shorter transmission distance for nodes near the sink, and adopts higher reliability requirement and farther transmission distance for nodes far from the sink, the solvability conditions is given as well. In the end, both the theoretical analysis and experimental results for performance evaluation show that the optimal design indeed can improve the network lifetime by 20-50%, network utility by 20% and guarantee desire level of reliability.