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Weather clustering for machine learning-based hourly building energy prediction models at design phase
Energy and Buildings ( IF 6.6 ) Pub Date : 2025-01-11 , DOI: 10.1016/j.enbuild.2025.115308
Dongxue Zhan, Shaoxiang Qin, Liangzhu (Leon) Wang, Ibrahim Galal Hassan
Energy and Buildings ( IF 6.6 ) Pub Date : 2025-01-11 , DOI: 10.1016/j.enbuild.2025.115308
Dongxue Zhan, Shaoxiang Qin, Liangzhu (Leon) Wang, Ibrahim Galal Hassan
With global efforts aimed at reaching carbon neutrality by 2050, there is an increased emphasis on optimizing building energy management. Accurate hourly building energy predictions support crucial tasks such as predicting peak loads for equipment sizing, comparing energy systems, and optimization during the design phase. The main methods used to model building energy are physics-based and data-driven. The former method has been extensively studied, whereas the latter has not been thoroughly investigated. This paper investigates the advantages of using the machine learning (ML) model as a surrogate model in building engineering, specifically for predicting hourly building energy consumption during the design phase. Synthetic data is commonly used for training and testing ML models when real-life measured data is unavailable due to privacy concerns or pre-construction scenarios. However, the challenge arises from the vast dataset of synthetic data generated by combining long-term hourly meteorological data with building characteristics. Using an example building, 82 million data points were generated as a result of simulating 8,760 h when considering ten building performance parameters. To address this issue, a methodology utilizing weather clustering techniques is proposed in this work. This approach aims to reduce dataset size associated with day-by-day simulations by identifying representative weather patterns. Consequently, 7 million data points were generated by identifying ten weather patterns and selecting 30 days, with three days chosen from each cluster. The Extreme Gradient Boosting (XGBoost) algorithm is applied to develop the ML model using the condensed dataset. This model demonstrated commendable performance with testing results that are within the tolerances established by ASHRAE guideline 14. Although we used data from a residential building in Qatar, our application demonstrated that the approaches could be applied to other building types and climate zones. The developed ML model, utilizing easily accessible inputs, can predict hourly building energy consumption. It is user-friendly for non-experts, such as city developers and stakeholders, during the design and retrofit stage.
中文翻译:
在设计阶段为基于机器学习的每小时建筑能源预测模型进行天气聚类
随着全球努力到 2050 年实现碳中和,人们越来越重视优化建筑能源管理。准确的每小时建筑能耗预测支持关键任务,例如预测设备选型的峰值负载、比较能源系统以及在设计阶段进行优化。用于对建筑能量进行建模的主要方法是基于物理和数据驱动的。前一种方法已被广泛研究,而后者尚未得到彻底研究。本文研究了在建筑工程中使用机器学习 (ML) 模型作为替代模型的优势,特别是用于预测设计阶段每小时的建筑能耗。当由于隐私问题或施工前场景而无法获得真实测量数据时,合成数据通常用于训练和测试 ML 模型。然而,挑战来自于通过将长期每小时气象数据与建筑特征相结合而生成的庞大合成数据集。使用示例建筑,在考虑 10 个建筑性能参数时,模拟 8,760 小时,生成了 8200 万个数据点。为了解决这个问题,这项工作提出了一种利用天气聚类技术的方法。这种方法旨在通过识别代表性天气模式来减少与日常模拟相关的数据集大小。因此,通过识别 10 种天气模式并选择 30 天(每个集群中选择 3 天),生成了 700 万个数据点。应用极端梯度提升 (XGBoost) 算法使用压缩数据集开发 ML 模型。 该模型表现出值得称道的性能,测试结果在 ASHRAE 指南 14 规定的公差范围内。尽管我们使用了来自卡塔尔住宅楼的数据,但我们的应用程序表明,这些方法可以应用于其他建筑类型和气候区。开发的 ML 模型利用易于访问的输入,可以预测每小时的建筑能耗。在设计和改造阶段,它对非专家(如城市开发商和利益相关者)来说是用户友好的。
更新日期:2025-01-11
中文翻译:
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在设计阶段为基于机器学习的每小时建筑能源预测模型进行天气聚类
随着全球努力到 2050 年实现碳中和,人们越来越重视优化建筑能源管理。准确的每小时建筑能耗预测支持关键任务,例如预测设备选型的峰值负载、比较能源系统以及在设计阶段进行优化。用于对建筑能量进行建模的主要方法是基于物理和数据驱动的。前一种方法已被广泛研究,而后者尚未得到彻底研究。本文研究了在建筑工程中使用机器学习 (ML) 模型作为替代模型的优势,特别是用于预测设计阶段每小时的建筑能耗。当由于隐私问题或施工前场景而无法获得真实测量数据时,合成数据通常用于训练和测试 ML 模型。然而,挑战来自于通过将长期每小时气象数据与建筑特征相结合而生成的庞大合成数据集。使用示例建筑,在考虑 10 个建筑性能参数时,模拟 8,760 小时,生成了 8200 万个数据点。为了解决这个问题,这项工作提出了一种利用天气聚类技术的方法。这种方法旨在通过识别代表性天气模式来减少与日常模拟相关的数据集大小。因此,通过识别 10 种天气模式并选择 30 天(每个集群中选择 3 天),生成了 700 万个数据点。应用极端梯度提升 (XGBoost) 算法使用压缩数据集开发 ML 模型。 该模型表现出值得称道的性能,测试结果在 ASHRAE 指南 14 规定的公差范围内。尽管我们使用了来自卡塔尔住宅楼的数据,但我们的应用程序表明,这些方法可以应用于其他建筑类型和气候区。开发的 ML 模型利用易于访问的输入,可以预测每小时的建筑能耗。在设计和改造阶段,它对非专家(如城市开发商和利益相关者)来说是用户友好的。