실시간 데이터를 이용한 도심지역의 에너지 수요예측

Energy demand prediction is not only essential to planning stage, but also to operating stage of buildings. If likely this study conduct energy demand prediction per time unit, it could make possible to maximize the efficiency of energy use. In addition, it could build more effective system of local energy demand-supply by using hybrid energy system. Keeping premises above in mind, in this study will practice energy demand prediction available in operating stage also, which has been implemented in planning stage. Meanwhile, precedent studies used artificial neural network as a tool; since it matches energy demand prediction up well with actual energy usage. Refer to early studies, it decided to designed artificial neural network which is considered suitable for this research. And used real time data to practice energy demand prediction in operating stage. This method applied to actual office building in use and classified the urban district(baseline models) by purpose of its energy use. The result analyzed error and energy profile of each one.