Effective Features to Predict Residential Energy Consumption Using Machine Learning
Effective Features to Predict Residential Energy Consumption Using Machine Learning
复制标题
使用机器学习预测住宅能源消耗的有效功能
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
M. Syal
中科院分区：
文献类型：
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作者：
Yunjeong Mo;Dong Zhao;M. Syal
与其他类型的建筑相比，人类对居住建筑的能耗影响更大。虽然现有的研究集中在建筑技术和居住者人口统计对能源消耗的影响上，但很少有研究纳入居住者能源使用模式的影响。本研究的目的是找出影响住宅建筑能耗的特征，并衡量它们的预测性能。研究人员考察了居住者的能源使用行为和家用电器的能源使用模式对家庭能源消耗的影响。这些模式反映了设备的组合、它们的使用时间和频率以及用户设置的配置。使用多种机器学习算法，包括支持向量机和随机森林，对住宅能耗调查(REC)的数据进行分析以选择用于预测的特征。研究结果提供了一系列可有效预测住宅建筑能耗的功能。所选的32个特征达到了全部271个特征的98%的预测性能。这一有效功能清单可用于提高节能计划的有效性，并教育居住者有关能源使用模式的知识。本研究揭示的乘员行为模式与能源使用模式之间的关系，为研究者进一步探索从能耗预测乘员行为提供了基础。
Humans have a greater influence on energy consumption in residential buildings than other types of buildings. Although existing studies focus on how energy consumption is affected by building technologies and occupant demographics, few studies have incorporated the impact of occupant energy use patterns. The goal of this study is to identify the features that affect energy consumption in residential buildings and to measure their predictive performance. The researchers examined the impact of occupants’ energy use behaviors and the energy use patterns of home appliances on home energy consumption. The patterns reflect on a combination of appliances, their use times and frequencies, and the configurations set by users. Data from the Residential Energy Consumption Survey (RECS) are analyzed to select features for prediction, using multiple machine learning algorithms including support vector machine (SVM) and random forest. The results provide a list of features that efficiently predict energy consumption in residential buildings. The selected 32 features achieve 98% of the prediction performance of that from the entire 271 features. This list of effective features can be used to improve the effectiveness of energy saving programs and to educate occupants about their energy use patterns. The relationship between occupants’ behavior patterns and energy use patterns revealed from this study provides the groundwork for researchers to further explore the prediction of occupant behavior from energy consumption.