基于实测能耗数据的商业建筑碳排放量预测

Carbon Emission Prediction of Commercial Buildings based on Measured Energy Consumption Data

商业建筑碳排放量受到气候、地理、经济水平的影响,为研究不同分类下商业建筑碳排放量的差异,制定合适的碳排放量指标与逐年降低比例,本研究整理了大量不同气候区及开业年份的商业建筑的实测能耗数据,在此基础之上,基于碳排放因子法、BP神经网络和回归拟合的方法,预测了不同分类商业建筑未来一年的碳排放量并分析了其发展趋势。结果显示,同一气候区下,纬度相差越大的商业建筑,对应的碳排放量指标差异也越大,且不同分类的商业建筑碳排放量都呈现出先逐渐降低后趋于平缓的发展趋势。

Carbon emissions from commercial buildings are affected by climate,geography,and economic level.In order to study the differences of carbon emissions of commercial buildings under different classifications and formulate appropriate carbon emission indicators and annual reduction ratio,this study sorted out a large number of measured energy consumption data of commercial buildings in different climate areas and operation years.Based on the method of carbon emission factor,BP neural network and regression fitting,the carbon emission of different commercial buildings in the next year was predicted and its development trend was analyzed.The results show that in the same climate zone,the greater the latitude difference of commercial buildings,the greater the corresponding difference of carbon emissions index,and the carbon emissions of commercial buildings in different dimensions show a development trend of gradually reducing first and then tending to be flat.