• 한국태양에너지학회:학술대회논문집
• /
• 한국태양에너지학회 2008년도 춘계학술발표대회 논문집
• /
• pp.304-310
• /
• 2008

Currently energy use planning council system is mandatory especially for the urban development project planned on a specified scale or more. The goal of existing demand prediction was to calculate the maximum load by multiplying energy load per unit area by building size. The result of this method may be exaggerated and has a limit in the information of period load. The paper suggests a new forecasting process based on standard unit household in order to upgrade the limit in demand prediction method of multi-family housing complex. The new process was verified by comparing actual using amount of multi-family housing complex to forecasting value of energy use plan.

• 설비공학논문집
• /
• 제25권6호
• /
• pp.310-316
• /
• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• 한국태양에너지학회:학술대회논문집
• /
• 한국태양에너지학회 2009년도 춘계학술발표대회 논문집
• /
• pp.184-189
• /
• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• KEPCO Journal on Electric Power and Energy
• /
• 제5권3호
• /
• pp.157-163
• /
• 2019

현재까지 피크완화 및 에너지 절감을 위해 한국전력공사 120여개 사옥에 K-BEMS (KEPCO Building Energy Management System)가 운영 중이다. 이 시스템은 PV, PCS, BESS, EMS 등으로 구성되어 있으며 건물에너지 수요예측을 기반으로 BESS, PV 등을 활용하여 에너지 관리를 도모하고 있다. 이 시스템은 단기 과거데이터에 신경망기법을 단순 적용하여 수요를 예측함에 따라 예측 정확도가 높지 않고 운영자 수작업을 통한 BESS 충방전으로 피크 저감이 곤란하며 운영 경제성 제고가 어려운 실정이다. 이러한 문제를 해결하기 위해 전력연구원에서는 2016년부터 3년간 연구과제를 수행하였는데 이를 통해 에러를 최소화하며 높은 신뢰도를 가지는 실시간 수요예측기법과 이에 기반한 BESS충방전 최적화 자동화 기술 개발, 성능을 검증하였기에 이를 본 논문에서 소개하고자 한다.

• 한국건축친환경설비학회 논문집
• /
• 제12권6호
• /
• pp.627-640
• /
• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2009년도 하계학술발표대회 논문집
• /
• pp.940-945
• /
• 2009

Energy planning for hybrid energy system is important to increase the flexibility in the urban community and national energy systems. Expected maximum loads, load profiles and yearly energy demands are important input parameters to plan for the technical and environmental optimal energy system for a planning area. The method for energy demand prediction has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. This method can produce 10% of errors hourly load profile from individual building to urban community. As the results of this paper, energy demand prediction system has been developed based on simulink.

• 대한설비공학회:학술대회논문집
• /
• 대한설비공학회 2006년도 하계학술발표대회 논문집
• /
• pp.789-794
• /
• 2006

The potential impacts of climate change on heating and cooling energy demand were investigated by means of transient building energy simulations and hourly weather data scenarios for Inchon. Future trends for the 21 st century was assessed based oil climate change scenarios with 7 global climate models(GCMs), We constructed hourly weather data from monthly temperatures and total incident solar radiation ($W/m^2$) and then simulated heating and cooling load by Trnsys 16 for Inchon. For 2004-2080, the selected scenarios made by IPCC foresaw a $3.7-5.8^{\circ}C$rise in mean annual air temperature. In 2004-2080, the annual cooling load for a apartment with internal heat gains increased by 75-165% while the heating load fell by 52-71%. Our analysis showed widely varying shifts in future energy demand depending on the season. Heating costs will significantly decrease whereas more expensive electrical energy will be needed of air conditioning during the summer.

• Advances in environmental research
• /
• 제2권3호
• /
• pp.203-227
• /
• 2013

Global demand for dwelling energy and implications of changing climatic conditions on buildings confront the built environment to build sustainable dwellings. This study investigates the variability of future climatic conditions on newly built detached dwellings in the UK. Series of energy modelling and simulations are performed on ten detached houses to evaluate and predict the impact of varying future climatic patterns on five building performance indicators. The study identifies and quantifies a consistent declining trend of building performance which is in consonance with current scientific knowledge of annual temperature change prediction in relations to long term climatic variation. The average percentage decrease for the annual energy consumption was predicted to be 2.80, 6.60 and 10.56 for 2020s, 2050s and 2080s time lines respectively. A similar declining trend in the case of annual natural gas consumption was 4.24, 9.98 and 16.1, and that for building emission rate and heating demand were 2.27, 5.49 and 8.72 and 7.82, 18.43 and 29.46 respectively. The study further analyse future heating and cooling demands of the three warmest months of the year and ascertain future variance in relative humidity and indoor temperature which might necessitate the use of room cooling systems to provide thermal comfort.

• 설비공학논문집
• /
• 제21권2호
• /
• pp.71-78
• /
• 2009

Up until recently, the energy and the economic analysis of a cogeneration system have been implemented by a manual calculation that is based on monthly thermal loads of buildings. In this study, a cogeneration system modeling validation with a detail building energy simulation, eQUEST, for a building energy and cost prediction has been implemented. By analyzing the hourly building electricity and thermal loads, it enables users to decide proper cogeneration system capacity and to estimate more accurate building energy consumption. eQUEST also verified the energy analysis when the heat pump system is integrated with the cogeneration system. The mechanical system configuration benefits from the high efficiency heat pump system while avoiding the building electricity demand increase. Economic analysis such as LCC (Life Cycle Cost) method is carried out to verify economical benefits of the system by applying actual utility rates of KEPCO(Korea Electricity Power COmpany) and KOGAS(KOrea GAS company).

• 한국지열·수열에너지학회논문집
• /
• 제7권1호
• /
• pp.65-71
• /
• 2011

Recently, central and local governments of Korea have established and implemented various energy policies such as making energy map of city level and establishment of environment friendly city plan to materialize low carbon green city. To implement effectively these policies, however, conditions of energy consumption by each administrative district and each usage have to be verified exactly. This study is aimed to suggest a basic data for planing energy policy and energy demand prediction of city level by analyzing energy consumption unit and conditions of city gas by borough and usage in Busan.