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Forecasting the impact of climate change on thermal comfort using a weighted ensemble of supervised learning models

机译：预测气候变化对使用监督学习模型加权集合的热舒适的影响

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摘要

This work presents a weighted ensemble of supervised learning models for estimating the days of the year where compliance with the Adaptive Model of thermal comfort is exceeded. The ensemble combines several gradient boosting on decision tree algorithms and Bayesian logistic regression. In a presented case study, the model is trained on three summers of hourly weather data and indoor air temperature data of a south-facing, naturally-ventilated office in Vancouver, Canada. The model is then used to predict thermal comfort exceedance under a possible climate change scenario. It is found that the ensemble outperforms its individual models in terms of accuracy, precision, and similar metrics. In eight of nine trials using the ensemble to re-assess known history, the ensemble predicts total comfort-exceeding days within a margin of one day. Under the RCP 8.5 global climate change scenario, the model predicts annual comfort-exceeding days will double before the 2050s, by that point exceeding current local thermal comfort compliance guidelines. Future applications of the presented methodology may assist other areas of data-driven forecasting, such as peak energy demand prediction. It may also assist analysis of emerging space cooling solutions such as radiant cooling of free-running buildings.
机译：这项工作介绍了监督学习模型的加权集合，用于估算超过了符合热舒适性的适应性模型的年份。该合奏组合了多个渐变升压在决策树算法和贝叶斯逻辑回归上。在提出的案例研究中，该模型培训了加拿大温哥华南面，自然通风办公室的三个小时天气数据和室内空气温度数据的概述。然后，该模型在可能的气候变化场景下预测热舒适性。结果发现，在精度，精度和类似的指标方面，该集合优于其各个模型。在八个九项试验中，使用该集合重新评估已知历史，该集合可以预测一天边缘内的完全舒适日。根据RCP 8.5全球气候变化情景，该模型预测了2050年代之前的年度舒适时间将增加到2050年代，而那点超出了当前的当地热舒适性合规指南。所提出的方法的未来应用可以帮助其他数据驱动的预测领域，例如峰值能量需求预测。它还可以帮助分析新出现的空间冷却解决方案，例如自由运行建筑物的辐射冷却。

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