• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.8
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• pp.2821-2839
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• 2015

Energy consumption by large computing systems has become an important research theme not only because the sources of energy are depleting fast but also due to the environmental concern. Computational grid is a huge distributed computing platform for the applications that require high end computing resources and consume enormous energy to facilitate execution of jobs. The organizations which are offering services for high end computation, are more cautious about energy consumption and taking utmost steps for saving energy. Therefore, this paper proposes a scheduling technique for Minimizing Energy consumption using Adapted Genetic Algorithm (MiE-AGA) for dependent tasks in Computational Grid (CG). In MiE-AGA, fitness function formulation for energy consumption has been mathematically formulated. An adapted genetic algorithm has been developed for minimizing energy consumption with appropriate modifications in each components of original genetic algorithm such as representation of chromosome, crossover, mutation and inversion operations. Pseudo code for MiE-AGA and its components has been developed with appropriate examples. MiE-AGA is simulated using Java based programs integrated with GridSim. Analysis of simulation results in terms of energy consumption, makespan and average utilization of resources clearly reveals that MiE-AGA effectively optimizes energy, makespan and average utilization of resources in CG. Comparative analysis of the optimization performance between MiE-AGA and the state-of-the-arts algorithms: EAMM, HEFT, Min-Min and Max-Min shows the effectiveness of the model.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.35 no.4
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• pp.185-192
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• 2019

In this study, we, through case studies, formulated a method to implement net-zero energy daycare center at the current insulation and technology level, and calculated its energy expense. The reference model was a medium sized daycare center whose number of children was 99. We analyzed the energy consumption status for the reference model and developed TRNSYS simulation analytical model to realize net-zero energy . We assumed the reference model to be "All Electric Building" where all energy including cooking is supplied by electricity. The result is summarized as follows: First, the annual electricity consumption of daycare center was 53,291kWh. Plug load occupied the largest share of 48% followed by lighting, 10%, cooling, 9%, cooking, 9%, heating, 8%, hot water, 5% and ventilation, 2%. Second, the photovoltaic installation capacity to realize net-zero energy was 40.32kWp and its annual generation was 53,402kWh. Third, the annual energy expense(electricity bill) by realizing net-zero energy was 2,620,390won.

• Journal of Environmental Science International
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• v.12 no.6
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• pp.643-650
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• 2003

We analyzed the amount of environmental loads, and the amount of energy consumption through life cycle assessment from a discharge stage to the ultimate disposal to municipal solid waste in Seoul. We carried out inventory analysis of the amount of environmental loads that made the object range collection, intermediate treatment, and the final treatment, and took into consideration each stage exceptions CO$_2$ and NOx , the amount of SOx discharge, and energy consumption. We applied the data of an object model, and acquisition processed the scale of an object model suitably and applied to it to difficult data using the data of the Yokohama City incineration plant in Japan. The amount of environmental loads per Iton of municipal waste were analyzed CO$_2$ 0.4C-ton, SOx 0.4kg and NOx 0.8kg. Moreover, the amount of energy consumption which is 2.4Gcal was computed.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.53-54
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• 2006

In this paper, we analytically evaluate packet delay and energy consumption of S-MAC protocol with a modified Markov chain model. Although some models, based on IEEE 802.11 MAC protocol, to analyze the S-MAC protocol in wireless sensor network (WSN) have been proposed, they fail to consider the differences in architecture between the S-MAC and the 802.11 MAC. Therefore, by reflecting the significant features in the S-MAC function, we model the operation of S-MAC protocol, and derive its packet delay and energy consumption in single-hop WSN. Numerical results show the delay and the dissipated energy at various duty cycle values according to offered load, where a practical mote is used.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.97-122
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• 2013

The ARDL(Autoregressive Distributed Lag) method is employed analyzes the long-run equilibrium relationships among environmental pollution($CO_2$ emissions) per capita, income levels per capita, and energy consumption per capita. The error correction model is employed to analyze the short-term effects of income and energy consumption on $CO_2$ emissions. The Toda-Yammamoto method is employed for causal analysis among the three variables. The results show that income levels, energy consumption, and $CO_2$ emissions are cointegrated. We found the N type relationship between income and $CO_2$ emissions. Long-term elasticities of income and energy consumption with respect to $CO_2$ emission were greater than their short-term elasticities. There were a bilateral causality between energy consumption and $CO_2$ emissions. There was a unilateral causality from $CO_2$ emissions to income and from energy consumption to income not vice versa. Energy consumption can be an important variable to contribute to forecasting $CO_2$ emissions.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.502-505
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• 2012

This study suggested 'Environmental Friendly City Model' and 'Energy Planning Process' according to the increasing necessity of 'Energy Saving Green City and 3 technologies like (1)Urban Spatial Modeling, (2)Urban Energy Consumption, (3)Urban Energy Supply Planning technologies were suggested which are able to support sustainable urban energy planning'. The results are as follows. (1)E-GIS modeling system was suggested as a 'Planning Supporting System'. (2)Urban Energy Consumption Algorithm was systemized with planning information of E-GIS DB. (3)Urban Energy System Location was deduced by integrating E-GIS DB and ANN algorithm.

• Advances in Energy Research
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• v.6 no.2
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• pp.91-101
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• 2019

This paper proposes an efficient data-driven approach to build models for predicting energy consumption in buildings. Data used in this research is collected by installing humidity and temperature sensors at different locations in a building. In addition to this, weather data from nearby weather station is also included in the dataset to study the impact of weather conditions on energy consumption. One of the main emphasize of this research is to make feature selection independent of domain knowledge. Therefore, to extract useful features from data, two different approaches are tested: one is feature selection through principal component analysis and second is relative importance-based feature selection in original domain. The regression model used in this research is gradient boosting regression and its optimal parameters are chosen through a two staged coarse-fine search approach. In order to evaluate the performance of model, different performance evaluation metrics like r2-score and root mean squared error are used. Results have shown that best performance is achieved, when relative importance-based feature selection is used with gradient boosting regressor. Results of proposed technique has also outperformed the results of support vector machines and neural network-based approaches tested on the same dataset.

• KIEAE Journal
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• v.10 no.1
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• pp.57-64
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• 2010

Electric energy is indispensible of the development of the industrial and living sector. Among the energy sectors, the building area shares 20% of the produced electric power in Korea. As we plan to supply the apartment, we need to forecast the required amount of the electric energy and supply the infrastructure to apartment for the lighting, cooling. Nonetheless, it is not easy to forecast the required amount of the electric energy, considering the management aspect, building physical aspect and social-geographic aspect. In this paper, it studied the estimation model of the electric energy, reflecting the affecting variables such as total area, number of household, geography and so on. The estimation model is proposed in 3-types which explained in central heating, individual heating and district heating, and each type have two estimation model, reflecting the affecting variable and corelation between variables to eliminate the muticolinearity. The unit of electric energy consumption per area and year is similar in three heating type and the results are as follows; the central heating is $34.446kWh/yr{\cdot}m^2$, individual type is $35.756446kWh/yr{\cdot}m^2$ and district heating is $34.285446kWh/yr{\cdot}m^2$.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.177-182
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• 2022

Technology is progressing with every passing day and the enormous usage of electricity is becoming a necessity. One of the techniques to enjoy the assistances in a smart home is the efficiency to manage the electric energy. When electric energy is managed in an appropriate way, it drastically saves sufficient power even to be spent during hard time as when hit by natural calamities. To accomplish this, prediction of energy consumption plays a very important role. This proposed prediction model Coherent Weighted K-Means Clustering ARIMA (CWKMCA) enhances the weighted k-means clustering technique by adding weights to the cluster points. Forecasting is done using the ARIMA model based on the centroid of the clusters produced. The dataset for this proposed work is taken from the Pecan Project in Texas, USA. The level of accuracy of this model is compared with the traditional ARIMA model and the Weighted K-Means Clustering ARIMA Model. When predicting,errors such as RMSE, MAPE, AIC and AICC are analysed, the results of this suggested work reveal lower values than the ARIMA and Weighted K-Means Clustering ARIMA models. This model also has a greater loglikelihood, demonstrating that this model outperforms the ARIMA model for time series forecasting.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.176-183
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• 2009

Distributed generation(DG) of combined cooling, heat, and power(CCHP)has been gaining momentum in recent year as efficient, secure alternative for meeting increasing energy demands. This paper presents the energy performance of microturbine CCHP system equipped with an absorption chiller by modelling it in hospital building. The orders of study were as following. 1)The list and schedule of energy consumption equipment in hospital were examined such as heating and cooling machine, light etc. 2) Annual report of energy usage and monitoring data were examined as heating, cooling, DHW, lighting, etc. 3) The weather data in 2007 was used for simulation and was arranged by meteorological office data in Daejeon. 4) Reference simulation model was built by comparison of real energy consumption and simulation result by TRNSYS and ESP-r. The energy consumption pattern of building were analyzed by simulation model and energy reduction rate were calculated over the cogeneration. As a result of this study, power generation efficiency of turbine was about 30[%] after installing micro gas turbine and lighting energy as well as total electricity consumption can be reduced by 40[%]. If electricity energy and waste heat in turbine are used, 56[%] of heating energy and 67[%] of cooling energy can be reduced respectively, and total system efficiency can be increased up to 70[%].