Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods
Space cooling energy usage prediction based on utility data for residential buildings using machine learning methods
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DOI：
10.1016/j.apenergy.2021.116814
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发表时间：
2021-04-01
期刊：
影响因子：
11.2
通讯作者：
Wang, Julian
中科院分区：
文献类型：
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作者：
Feng, Yanxiao;Duan, Qiuhua;Wang, Julian
居住建筑的空间供冷能耗对户型的节能效果有重要影响。这项研究旨在开发一种用户友好的、无需基础设施的、准确的预测模型，该模型基于位于美国三个不同气候带的匿名志愿者住房的大规模公用事业数据集，以及相应的天气数据和建筑信息。值得注意的是，在建模过程中设计了几个与天气和建筑特征相关的新参数，并经测试有助于提高模型的预测性能-S。通过超参数优化和k重交叉验证，对几种回归方法进行了检验和比较。随后，还描述了如何实现所开发模型的工作流程。研究结果表明，极值梯度增强(XGBoost)模型具有最优的性能，特征重要性分析也确定并排序了关键预测因素，以提高该模型的解释能力。该模型在整个数据集上的R2值约为97%，而通过交叉验证方法对数据集的不同子集获得的R2值为92%。该模型的均方根误差为0.294，均方根误差为0.153。由此产生的预测制冷能耗的模型将有助于房主更好地了解他们的建筑？以最少的输入信息和不需要额外硬件安装的性能水平，最终帮助他们做出与节能战略相关的决策。
The energy used for space cooling in residential buildings has a significant influence on household energy performance. This study aims to develop a user-friendly, infrastructure-free, and accurate prediction model based on large-scale utility datasets from anonymized volunteer homes located in three different climate zones in the US, along with the corresponding weather data and building information. Notably, several new weather- and building characteristics-related parameters were designed in the modeling procedure and tested to be useful for enhancing the model?s prediction performance. A few regression techniques were examined and compared through hyperparameter optimization and k-fold cross-validation. Subsequently, a workflow was also described for how to implement the developed model. The research results showed that the eXtreme Gradient Boosting (XGBoost) model offered optimal performance, and the feature importance analysis also identified as well as ranked the key predictors to enhance the interpretability of this model. An R2 value of around 97% was obtained with that model on the whole dataset, while an R2 value of 92% was achieved with various subsets of the dataset through the cross-validation approach. The RMSE and RAE for this model were 0.294 and 0.153, respectively. The resultant model for predicting cooling energy consumption will facilitate homeowners better understanding their buildings? performance levels with minimum input information and without additional hardware installations, ultimately aiding their decision making related to energy-saving strategies.