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首页> 外文期刊>ASHRAE Transactions >Typical Meteorological Year and Actual Weather Data in Data-Driven Machine Learning Models for Residential Building Energy Use
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Typical Meteorological Year and Actual Weather Data in Data-Driven Machine Learning Models for Residential Building Energy Use

机译：数据驱动机器学习模型中的典型气象年和实际天气数据，用于住宅建筑能源使用

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摘要

Physics-based computer models use typical meteorological year (TMY) weather files or actual weather data (A WD) for a specific location to predict the building energy performance. TMY files are based on the long-term measurements of weather data. On the other hand, AWD, which is obtained from direct measurements for a specific period, might result in a different energy simulation outcome. Moreover, most of the building systems, such as heating and cooling systems, which are optimized by model predictive control (MPC) methods, work based on weather data. Therefore, it is vital to understand and quantify the impact of weather data on the accuracy of predictive models. This paper differentiates the impact of TMY and AWD in building energy simulation in data-driven models (DDMs) as opposed to physics-based models to study the importance of weather data in DDMs. This research study uses real-time aggregated data with a 15-minute interval collected from residential buildings in Austin, Texas. Generalized Linear Model, Deep Learning, Decision Tree, Random Forest, and Gradient Boosted Trees models are exploited to predict the energy use based on available features in TMY and AWD, where the latter is obtained from an open-access database that includes about 11 types of weather data such as temperature, humidity, wind speed, and atmospheric pressure. Extensive sensitivity analysis is performed to tune the parameters of the proposed DDMs. The models' outputs are compared for both TMY, and AWD based on the root-mean-square error (RMSE) of each model. These outputs quantify the impact of weather data on the accuracy of DDMs predictions for building energy use, which contribute to the performance of MPC systems in buildings to maximize energy saving.
机译：基于物理的计算机模型使用典型的气象年（TMY）天气文件或实际天气数据（A WD）用于特定位置，以预测建筑能量性能。 TMY文件基于天气数据的长期测量。另一方面，从特定时段的直接测量获得的AWD可能导致不同的能量模拟结果。此外，大多数建筑系统，例如加热和冷却系统，通过模型预测控制（MPC）方法优化，基于天气数据。因此，理解和量化天气数据对预测模型的准确性的影响至关重要。本文区分了TMY和AWD在数据驱动模型（DDMS）中的能量模拟中的影响，而不是基于物理的模型，以研究DDMS中天气数据的重要性。该研究使用实时汇总数据，从德克萨斯州奥斯汀的住宅建筑收集了15分钟的间隔。广义线性模型，深度学习，决策树，随机森林和渐变升降树模型被利用来预测基于TMY和AWD中的可用功能的能源使用，其中后者是从包括约11种类型的开放式访问数据库获得的天气数据如温度，湿度，风速和大气压。进行广泛的灵敏度分析以调整所提出的DDMS的参数。基于每个模型的根均方误差（RMSE），对模型的输出进行比较。这些产出量化了天气数据对建筑能源使用的DDMS预测准确性的影响，这有助于MPC系统在建筑物中的性能最大化节能。

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  • 来源
    《ASHRAE Transactions》 |2020年第2期|88-95|共8页
  • 作者

    Ehsan Kamel; Shaya Sheikh;

  • 作者单位

    Department of Energy Management and the Energy and Green Technologies Laboratory (EnTech Lab) New York Institute of Technology (NYIT) Old Westbury NY;

    Department of Management Science Studies NYIT Old Wcstbury NY;

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