Improving Building Energy Efficiency through Data Analysis
Improving Building Energy Efficiency through Data Analysis
复制标题
通过数据分析提高建筑能源效率
DOI：
10.1145/3599733.3600244
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Zhu, Zhigang
中科院分区：
文献类型：
--
作者：
Phillip, DiAndra;Chen, Jin;Maksakuli, Fani;Ruci, Arber;Sturdivant, E'edresha;Zhu, Zhigang
对于许多立法者来说，节能建筑一直是美国大城市的主要关注点。建筑物消耗的能源最多，产生的温室气体排放量也最多。对于纽约市 (NYC) 的公共和私人建筑来说尤其如此，仅这些建筑的排放量就占该市温室气体排放总量的三分之二以上。因此，提高建筑能源效率已成为减少温室气体排放和化石燃料消耗的重要目标。纽约市建筑物的历史能耗数据用于机器学习模型，以确定其能源之星分数，以进行时间序列分析和未来预测。机器学习模型用于预测未来的能源使用情况，并回答如何将机器学习纳入有效决策以优化城市最大建筑物内的能源使用的问题。结果表明，按物业类型（而不是按位置）对建筑物进行分组可以更好地预测能源之星得分。
For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future prediction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores.