Time of the week AutoRegressive eXogenous (TOW-ARX) model to predict thermal consumption in a large commercial mall

IF 7.1 Q1 ENERGY & FUELS
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引用次数: 0

Abstract

This paper proposes a procedure to build a Time of the Week AutoRegressive eXogenous (TOW-ARX) model, indexed with respect to time and day of the week, to characterize heat consumption in tertiary buildings. Models for building heat load characterization and prediction are crucial to enhance energy efficiency. The proposed model can be used for different purposes, e.g., control of indoor climate, or characterization of the thermal response of the building. A case study is described where the TOW-ARX model is used to characterize the energy consumption of a large retail building in Madrid. In order to discard the risk of model overfitting, cross validation is applied using the k-fold technique. The performance of the TOW-ARX model is compared with a set of different models: a reduced version of the model where similar segments are clustered using the k-means method (R-TOW-ARX), a general ARX model, a linear regression steady-state TOW model (TOW-LR), a version of the latter reduced through clustering (R-TOW-LR), and a general multiple linear regression model (LR). The results reveal that ARX-based models notably outperforms the rest. The TOW-ARX model shows the best metrics, but also outnumbers the number of coefficients of the other models by far. The selection of the most suitable model is not straightforward and should depend on the purpose of such model: the TOW-ARX model would arguably be the best for control purposes due to its low mean absolute error, but the ARX model would be preferable for an efficient characterization of the thermal response of a building due to its reduced number of parameters.
预测大型商业购物中心热能消耗的每周时间自回归模型(TOW-ARX)
本文提出了一种建立每周时间自回归外生(TOW-ARX)模型的程序,该模型以时间和星期为索引,用于表征第三产业建筑的耗热量。建筑热负荷特征描述和预测模型对于提高能源效率至关重要。所提出的模型可用于不同的目的,如控制室内气候或描述建筑物的热响应。本文介绍了一个案例研究,利用 TOW-ARX 模型对马德里一座大型零售建筑的能耗进行了分析。为了避免模型过拟合的风险,使用 k-fold 技术进行了交叉验证。TOW-ARX 模型的性能与一组不同的模型进行了比较:使用 k-means 方法对相似片段进行聚类的简化版模型(R-TOW-ARX)、一般 ARX 模型、线性回归稳态 TOW 模型(TOW-LR)、通过聚类对后者进行简化的版本(R-TOW-LR)以及一般多元线性回归模型(LR)。结果显示,基于 ARX 的模型明显优于其他模型。TOW-ARX 模型的指标最好,但系数数量也远远超过其他模型。选择最合适的模型并不简单,应取决于此类模型的用途:TOW-ARX 模型因其平均绝对误差较小,可以说是用于控制目的的最佳模型,但 ARX 模型因其参数数量较少,更适合用于有效描述建筑物的热响应。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.80
自引率
3.20%
发文量
180
审稿时长
58 days
期刊介绍: Energy Conversion and Management: X is the open access extension of the reputable journal Energy Conversion and Management, serving as a platform for interdisciplinary research on a wide array of critical energy subjects. The journal is dedicated to publishing original contributions and in-depth technical review articles that present groundbreaking research on topics spanning energy generation, utilization, conversion, storage, transmission, conservation, management, and sustainability. The scope of Energy Conversion and Management: X encompasses various forms of energy, including mechanical, thermal, nuclear, chemical, electromagnetic, magnetic, and electric energy. It addresses all known energy resources, highlighting both conventional sources like fossil fuels and nuclear power, as well as renewable resources such as solar, biomass, hydro, wind, geothermal, and ocean energy.
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