• Proceedings of the Korea Database Society Conference
• /
• 1999.06a
• /
• pp.195-202
• /
• 1999

본 논문은 배전 계통에서 부하 제약조건과 운전 제약조건을 고려한 손실 감소와 부하 평형에 대해 시뮬레이티드 어닐링 알고리즘을 적용한 재구성 방법을 서술하였다. 네트워크 재구성은 수많은 연계 개폐기와 구분 개폐기의 조합에 의해 이루어지기 때문에 조합적인 최적화 문제이다. 이러한 문제는 수많은 조합에 제약조건까지 있어 해를 구하기가 쉽지 않을 뿐 아니라 국소 해에 빠질 가능성이 많다. 따라서 신경망 중에서 제약조건에 따라 신경망 구조에 영향을 미치지 않으면서 전역 최소해에 수렴하는 특성을 가진 시뮬레이티드 어닐링 기법을 이용하여 배전 계통의 선로를 재구성하였다. 시뮬레이티드 어닐링은 이론적으로 최적해가 보장되지만 무한대의 시간이 걸리기 때문에 현실적으로 적용할 때 해 공간을 탐색하는 규칙과 온도를 적절히 내리는 냉각 스케줄(cooling schedule)이 중요하다. 본 논문에서는 알고리즘 상에서 제약조건 위반 여부를 점검할 수 있는 제약조건과 페널티 상수(penalty factor)를 통해 목적함수에 반영하는 제약조건으로 나누어 모든 후보해를 가능해가 되게 하였고 기존에 사용되던 Kirkpatrick의 냉각 스케줄 대신에 후보해의 통계적 처리에 의해 온도를 내리는 다항-시간 냉각 스케줄(polynomial-time cooling schedule)을 사용하여 수행시간을 단축하고 수렴성을 높였다. 제안한 알고리즘의 효용성을 입증하기 위해 32, 69모선 예제 계통으로 테스트하였다.

• Proceedings of the KIEE Conference
• /
• 1992.07a
• /
• pp.288-289
• /
• 1992

The method of simulated annealing is a technique that has recently attracted significant attention as suitable for optimization problem of very large scale. If the temperature is too high, then some of the structure created by the heuristic will be destroyed and unnecessary extra work will be done. If it is too low then solution is lost, similar to the case of a quenching cooling schedule in the Simulated Annealing (SA) phase. Therefore, a crucial issue in this study is the determination of the starting temperature and cooling schedule for SA phase.

• 제어로봇시스템학회:학술대회논문집
• /
• 1992.10a
• /
• pp.624-627
• /
• 1992

Mathematical modeling is majorly divided into three parts: the derivation of models, the fitting of models to data, and the simulation of data from models. This paper focuses on the parameter optimization which is necessary for the fitting of models to data. The method of simulated annealing(SA) is a technique that has recently attracted significant attention as suitable for optimization problem of very large scale. If the temperature is too high, then some of the structure created by the heuristic will be destroyed and unnecessary extra work will be done. If it is too low then solution is lost, similar to the case of a quenching cooling schedule in the SA phase. In this study, therfore, we propose a technique of determination of the starting temperature and cooling schedule for SA phase.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.4
• /
• pp.1419-1426
• /
• 2012

The heating and cooling energy load of the KNU plant factory was analyzed using the DesignBuilder. Indoor temperature set-point, LED supplemental lighting schedule, LED heat gain, and type of double skin window were selected as simulation parameters. For the cases without LED supplemental lighting, the proper growth temperature of lettuce $20^{\circ}C$ was selected as indoor temperature set-point together with $15^{\circ}C$ and $25^{\circ}C$. The annual heating and cooling loads which are required to maintain a constant indoor temperature were calculated for all the given temperatures. The cooling load was highest for $15^{\circ}C$ and heating load was highest for $25^{\circ}C$. For the cases with LED supplemental lighting, the heating load was decreased and the cooling load was 6 times higher than the case without LED. In addition, night time lighting schedule gave better result as compared to day time lighting schedule. To investigate the effect of window type on annual energy load, 5 different double skin window types were selected. As the U-value of double skin window decreases, the heating load decreases and the cooling load increases. To optimize the total energy consumption in the plant factory, it is required to set a proper indoor temperature for the selected plantation crop, to select a suitable window type depending on LED heat gain, and to apply passive and active energy saving technology.

• Journal of the Korea Society of Computer and Information
• /
• v.9 no.3
• /
• pp.189-194
• /
• 2004

This paper proposes a Simulated Annealing(SA) algorithm for cluster-based Multicast Routing problems. Multicasting, the transmission of data to a group, can be solved from constructing multicast tree, that is. the whole network is partitioned to some clusters and the clusters are constructed by multicast tree. Multicast tree can be constructed by minimum-cost Steiner tree. In this paper, an SA algorithm is used in the minimum-cost Steiner tree. Especially, in SA, the cooling schedule is an important factor for the algorithm. Hence, in this paper, a cooling schedule is proposed for SA for multicast routing problems and analyzed the simulation results.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.28 no.7
• /
• pp.299-304
• /
• 2016

In recent years, heat source systems which have a principal effect on the performance of buildings are difficult to design optimally as a great number of design factors and constraints in large and complicated buildings need to be considered. On the other hand, it is necessary to design an optimum system combination and operation planning for energy efficiency considering Life Cycle Cost (LCC). This study suggests a multi-level and multi-objective optimization method to minimize both LCC and investment cost using a genetic algorithm targeting an office building which requires a large cooling load. The optimum method uses a two stage process to derive the system combination and the operation schedule by utilizing the input data of cooling and heating load profile and system performance characteristics calculated by dynamic energy simulation. The results were assessed by Pareto analysis and a number of Pareto optimal solutions were determined. Moreover, it was confirmed that the derived operation schedule was useful for operating the heat source systems efficiently against the building energy requirements. Consequently, the proposed optimization method is determined by a valid way if the design process is difficult to optimize.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2000.04b
• /
• pp.458-465
• /
• 2000

In the field of structural optimization simulated annealing (SA) algorithm has widely been adopted as an optimizer with the positive features of SA such as simplicity of the algorithm and possibility of finding global solution However, annealing process of SA algorithm based on random generator with the zeroth order structural information requires a large of number of iterations highly depending on cooling schedules and stopping criteria. In this paper, MSA algorithm is presented in the form of two phase annealing process with the effective cooling schedule and stopping criteria. With the application to optimal seismic design of steel structures, the performance of the proposed MSA algorithm has been demonstrated with respect to stability and global convergence of the algorithm

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2009.10a
• /
• pp.279-283
• /
• 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%.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.22 no.6
• /
• pp.392-397
• /
• 2010

Energy loss from windows accounts for large scores of heating and cooling loads also in energy saving apartments that is reduced over 30% of total energy consumption. Movable reflective insulations, insulation shutters, blinds, insulated shades are used to reduce energy loads from windows. In this study, energy saving performance of insulated shades was simulated by control methods. According to installation of insulated shades, heating loads were decreased about 10.5~11.3%, and cooling loads are decreased about 29.1~38.3% on an energy saving apartment. The heating peak load was reduced about 9.5% by insulated shades and the cooling peak load was reduced about 25.7~31.5%. In the case of insulated shades with automatic control system, simple time schedule control system would be more efficient than outdoor detection control system that should use several sensors.

• Architectural research
• /
• v.19 no.1
• /
• pp.7-11
• /
• 2017

Energy consumption in university building has steadily increased over the last decade, and a strong upward trend in recent years. This study was undertaken to analyze the relationship between energy consumption and their affecting factors, six academic buildings were considered. The factors limited to heating and cooling, which is the main end use (nearly 60 per cent of total energy consumption in university buildings), encompassing system and operating schedules (user activity) and area use. To understand how to building is used, operated and managed, walk-through assessment was conducted as well as interview with university staff. The results show that the energy consumption of the humanities building was somewhat smaller than the consumption of the science and engineering building, and its range was from $31.26kgoe/m^2$ to $23.52kgoe/m^2$, depending on heating and cooling system and area use. And the energy consumption of the science and engineering building was related to operating schedules (user activity) as well as laboratory equipment characteristics. More analysis on a larger number of buildings is required in the future, including building form and material performance level to generalize the significant factors influencing building energy consumption.