• Advances in Energy Research
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• 제6권2호
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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.

• 스마트미디어저널
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• 제8권4호
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• pp.72-79
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• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• 대한건축학회논문집:구조계
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• 제34권2호
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• pp.67-74
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• 2018

Resource leveling minimizes resource fluctuations by deferring the earliest start times (ESTs) of non-critical activities within their corresponding total float. The intentional float-consumption for resource leveling purpose reduces the schedule delay contingency. This paper presents a method called Genetic Algorithm based Resource Leveling (GARL) that minimizes resource fluctuations and float-consumption impact over project duration. It identifies activities that are less sensitive to float-consumption and performs resource leveling using those activities. The study is of value to project scheduler because GARL identifies the set of activities to be deferred and the number of shift day(s) of each and every activities in the set within its total float expeditiously. It contributes to establish a baseline schedule which implements an optimal resource leveling plan. A case study is presented to verify the validity and usability of the method. It was confirmed that GARL satisfies the project duration constraint by considering resource fluctuations and float-consumption over project duration.

• 한국지열·수열에너지학회논문집
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• 제16권4호
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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%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권1호
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• 한국ITS학회 논문지
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• 제16권3호
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• pp.73-82
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• 2017

2016년 12월 기준 우리나라 자동차 총 등록대수는 2100만대를 넘어섰으며, 이러한 영향으로 교통부문 온실가스 배출량도 매해 증가하였다. 연비와 온실가스 배출량에 영향을 미치는 주요 요인으로 알려진 주행 행태와 도로 조건을 고려한 주행전략을 개발하여 주행을 할 때, 높은 연비 개선 효과를 누릴 수 있을 것으로 판단하였다. 이에 본 연구는 오르막 구간과 곡선구간이 있는 복합 도로선형 구간에서의 연료 효율적인 주행전략 개발 및 연료소모량 절감 효과 분석을 목적으로 하였다. 도로의 시설 기준에 관한 규칙에 따른 도로지형을 설계하고, 다양한 시나리오를 선정하여 속도프로파일을 생성한 후, Comprehensive Modal Emission Model에 적용하여 연료소모량을 도출하였다. 연료소모량이 최소인 시나리오를 선택하였으며, 검증을 위해 일반 운전자의 주행과 최적주행전략의 연료소모량을 비교 분석하였다. 분석 결과, 최적 주행전략이 일반 운전자의 주행보다 연료소모량을 평균 21.2% 절감하는 것으로 나타났다.

• 한국멀티미디어학회논문지
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• 제14권11호
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• pp.1420-1430
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• 2011

본 논문은 IEEE 802.15.4의 MAC 계층에서 노드간 거리와 환경에 따라 적절한 전송전력 값을 설정하여 무선 모뎀의 에너지 소비를 최적화하는 방법에 대하여 제안한다. 제안된 방법은 적절한 전송전력을 결정할 때 이진검색 기법으로 빠르게 최적의 전송전력을 찾을 수 있고, 다수의 메시지 전송에 따르는 오버헤드를 최소화할 수 있다. 결정된 전송전력은 데이터 패킷을 전송할 때 사용되고, 데이터-수신확인 패킷의 교환과정에서 전파환경에 따라 변경된 네트워크 품질에 따라 동적으로 수정된다. 시뮬레이션 실험결과 본 논문에서 제안하는 방법은 IEEE 802.15.4 표준에 비해 에너지 소비량은 30% 감소하고, 단위 에너지양에 의한 데이터 전송량은 2.5배 증가하는 결과를 보였다.

• Journal of Computational Design and Engineering
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• 제1권4호
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• pp.256-265
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• 2014

Energy efficiency is an essential consideration in sustainable manufacturing. This study presents the car fender-based injection molding process optimization that aims to resolve the trade-off between energy consumption and product quality at the same time in which process parameters are optimized variables. The process is specially optimized by applying response surface methodology and using non-dominated sorting genetic algorithm II (NSGA II) in order to resolve multi-object optimization problems. To reduce computational cost and time in the problem-solving procedure, the combination of CAE-integration tools is employed. Based on the Pareto diagram, an appropriate solution is derived out to obtain optimal parameters. The optimization results show that the proposed approach can help effectively engineers in identifying optimal process parameters and achieving competitive advantages of energy consumption and product quality. In addition, the engineering analysis that can be employed to conduct holistic optimization of the injection molding process in order to increase energy efficiency and product quality was also mentioned in this paper.

• 설비공학논문집
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• 제27권5호
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• pp.247-253
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• 2015

The presented work demonstrates the effects of flow rate on the secondary side of DHS (District Heating System). Increasing flow rate at the secondary side of DHS decreases energy consumption and time to reach the set-point of the heated room while increasing heat flux on the floor in the heating space. When flow rate increases, the overall heat transfer rate of radiant floor also increases. However, the results also show overall heat transfer rateto not increased linearly and thus the existence of an optimal flow rate for the secondary side of DHS. Control of the radiant floor with hot water may be more effectively accomplished with a combined control strategy that includes heat flux and a temperature set-point. This experimental analysis has been performed using a lab-scaled DHS pilot plant located at Jeonju University in Korea.

• 한국산업정보학회논문지
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• 제25권1호
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• pp.1-11
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• 2020

본 논문은 큰 스케일의 무선센서네트워크(Wireless Sensor Networks: WSN)에서 클러스터링을 위한 제어 메시지의 최적화된 전송반경을 사용하여 전체 네트워크의 수명을 증가시키는 방법을 제안한다. 또한 스케일이 큰 네트워크에서 노드들의 제어 메시지의 최대 전송반경과 에너지 소모에 대하여 고찰한다. 이를 위하여 일반 노드가 소모하는 에너지와 클러스터 헤드가 소모하는 에너지의 함수 형태로 제어 메시지 전송반경을 분석하고, 이를 바탕으로 최적의 전송반경을 구하는 방법을 제시한다. 제안한 방법을 이용하여 싱글 홉 및 멀티 홉 등의 여러 WSN 라우팅 환경에서 시행한 시뮬레이션 성능 분석 결과를 바탕으로, 본 논문에서 제안한 방법이 기존의 방법들에 비하여 전체 네트워크의 수명을 더욱 길게 운용하는 것을 보였다.