Correlation Analysis of Usage Pattern in Home Appliance with Boosting Algorithm

Joahyoung Lee, Y. Doh, Tae-Wook Heo
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Abstract

Energy consumption in the home increases recently due to the extremely hot or cold weather. Because of COVID 19, many people stay in the home and energy consumption in the home is increasing very much. Moreover, many homes are using new electric home appliances such as dishwasher or washer dryer which consumes much electric energy for a long duration. To reduce electric energy consumption and use energy more efficiently, the usage pattern of the home appliance should be analyzed. In the paper, we propose a pattern analysis method of the home appliance using Boosting technique. Boosting method is a sort of ensemble machine learning algorithm and is based on the decision tree. The correlation between home appliance usage can be analyzed with the result of feature importance in boosting algorithm. To verify the method, we analyzed the electric usage record in the UK with boosting algorithm.
基于Boosting算法的家电使用模式相关性分析
由于极端炎热或寒冷的天气,最近家庭的能源消耗增加了。由于新冠肺炎疫情,很多人呆在家里,家里的能源消耗增加了很多。此外,许多家庭正在使用新的家用电器,如洗碗机或洗衣机烘干机,这些电器消耗大量的电能,持续时间很长。为了减少电能消耗,更有效地利用能源,应该分析家电的使用模式。本文提出了一种利用boost技术对家电产品进行模式分析的方法。Boosting算法是一种基于决策树的集成机器学习算法。利用增强算法中的特征重要性分析家电使用之间的相关性。为了验证该方法,我们使用boosting算法分析了英国的用电量记录。
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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