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Gradient boosting machine for modeling the energy consumption of commercial buildings

机译：用于模拟商业建筑能耗的梯度提升机

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Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm. (C) 2017 Elsevier B.V. All rights reserved.
机译：准确的节约估算对于促进能效项目并证明其成本效益非常重要。商业建筑中高级计量基础设施（AMI）的出现越来越多，导致高频间隔数据的可用性越来越高。这些数据可用于各种能源效率应用，例如需求响应，故障检测和诊断以及供暖，通风和空调（HVAC）优化。大量数据也为使用高级统计学习模型打开了大门，这些模型有望提供准确的建筑基准能耗预测，从而实现准确的节能估算。梯度提升机是一种功能强大的机器学习算法，在各种数据驱动的应用程序（例如生态学，计算机视觉和生物学）中正获得相当大的吸引力。在目前的工作中，提出了一种基于梯度提升机的能耗基线建模方法。为了评估此方法的性能，最近发布了一个测试程序，用于410个商业建筑物的大型数据集。模型的训练周期各不相同，并且使用几个预测准确性指标来评估模型的性能。结果表明，与基于分段线性回归的行业最佳实践模型相比，使用梯度提升机模型可以在80％以上的情况下提高R平方预测精度和CV（RMSE）。随机森林算法。 （C）2017 Elsevier B.V.保留所有权利。

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