Energy consumption prediction of new energy vehicles in smart city based on LSTM network
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In order to overcome the traditional problems such as large prediction error and long prediction time, this paper proposes a new energy consumption prediction method of smart city new energy vehicles based on LSTM network. By analysing the energy operation process of new energy vehicles in smart city, the energy consumption prediction parameters such as vehicle battery energy, resistance energy consumption, rolling resistance and air resistance are determined. On this basis, the energy consumption prediction model of new energy vehicles is constructed, and the LSTM network is used to solve the energy consumption prediction model of new energy vehicles, and the energy consumption prediction results are obtained. Experimental results show that the prediction error of the proposed method is always less than 2%, and when the number of iterations is 50, the prediction time of the proposed method is only about 0.95 s, which is relatively short.Keywords:
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energy demand
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온실가스 배출 감소 및 에너지소비량을 감소시키기 위하여 많은 정책들이 수립되고 있다. 그러나 이러한 정책은 건축물의 에너지 소비 특성을 고려하지 않고 건축물에 대하여 일괄적인 에너지 절감률을 적용하고 있다. 일괄적인 에너지소비량 절감율을 일괄적으로 적용할 경우, 에너지소비량이 적은 건축물은 이를 달성 할 수 없다. 따라서 본 연구에서는 건축물의 에너지소비특성에 따른 건축물 분류와 건축물의 에너지소비량에 따른 개별적인 에너지소비량 절감률 산정 방법에 대하여 연구하였다. 에너지소비량 특성 분석결과 건축물의 용도에 따라 에너지소비량 차이가 있음이 나타났다. 또한 이를 바탕으로 건축물을 분류하였다. 또한 기존의 건축물 에너지절감과 관련한 제도들을 분석하여 건축물의 에너지소비량 절감 목표 및 대상 등을 선정하였고. 이을 바탕으로 에너지소비량에 따른 에너지 소비량 절감률 공식을 제안하였다. 또한 서울시 공공건축물 중 제1종 근린생활시설에 본 연구에서 제안한 공식을 적용할 경우 국가 온실가스 배출로드맵에서 목표로 하고 있는 절감률 26%를 달성할 수 있음을 확인하였다. 본 연구를 통하여 건축물의 개별적인 목표 에너지절감률을 산정할 수 있지만, 건축물 목표 에너지 절감률을 실제건축물에 반영하기 위해서는 건축물의 에너지소비량과 에너지소요량의 관계 및 건축물에서 절감 가능한 에너지소비량에 대한 연구가 필요하다. There are a number of policies that have been established to reduce greenhouse gases emissions and reduce energy consumption. These polices, however, only reflect the building energy consumption saving rate without considering building energy consumption characteristic. If building that already use less energy apply the same saving rate to reduce energy consumption, it is less effective. Therefore, this study classified building energy consumption characteristics and investigated approaches to adopt individual reduction rates according to energy consumption. Analysis results showed that there are differences in energy consumption depending on building use, and therefore, we classified buildings by use. We also proposed a formula to set a reduction goal for rate of energy consumption according to energy consumption. We verified our approach in a top-end neighborhood facility in Seoul. The energy consumption savings goal of this study can be applied individually, but determining whether a building can reduce energy consumption towards a saving goal requires a closer look at each building.
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Energy accounting
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