Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA
Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA
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DOI：
10.3390/buildings10080139
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发表时间：
2020-08
期刊：
影响因子：
3.8
通讯作者：
Rezvan Mohammadiziazi;M. Bilec
中科院分区：
文献类型：
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作者：
Rezvan Mohammadiziazi;M. Bilec
鉴于气候变化的紧迫性，开发快速可靠的方法对于了解占美国总能源使用量40%的城市建筑能源使用量至关重要。虽然机器学习（ML）方法可能提供承诺，并且开发难度较小，但方法，结果和建议的差异已经出现，需要引起注意。现有的研究还表明，将气候变化模型纳入能源建模方面存在不一致性。为了应对这些挑战，四个模型：随机森林（RF），极端梯度提升（XGBoost），单回归树，和多元线性回归（MLR），开发使用商业建筑能源消费调查数据集，预测能源使用强度（EUI）下预计加热和冷却度日政府间气候变化专门委员会（IPCC）在美国各地的世纪。RF模型提供了更好的性能，与XGBoost，单回归树和MLR相比，平均绝对误差分别降低了4%，11%和12%。此外，使用RF模型进行气候变化分析表明，2030年至2080年期间，不同地理区域的办公楼的EUI将比2012年基线增加8.9%至63.1%。一个地区预计EUI将减少近1.5%。最后，良好的数据增强了ML的预测能力，因此，全面的区域建筑数据集对于在更精细的空间尺度上评估面对气候变化时建筑能源使用的抵消作用至关重要。
Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale.