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A novel approach for residential load appliance identification
Sustainable Cities and Society ( IF 11.7 ) Pub Date : 2020-09-18 , DOI: 10.1016/j.scs.2020.102484
Emre Akarslan , Rasim Doğan

The analysis of the energy consumption of a house is important for its energy management. With the expansion of smart homes, energy management of a house gained more importance. To manage this expansion, loads should be identified. In this study, a novel load appliance identification approach is proposed. This approach utilizes from only current waveform while extracting the features. In the proposed approach, firstly a data preprocessing is performed to extract one period signal from the measurement. Then Fast Fourier Transform (FFT) of the current signal is calculated and the real and imaginary parts of the transform are evaluated separately. Statistical features such as maximum, minimum and standard deviation of the real and imaginary parts are extracted. After the feature extraction procedure, the boundaries of each load appliance in terms of extracted features are determined to build a rule table and the load appliance is identified using these rules. In this study, the identification of both individual appliances and different combinations of appliances are performed. The results show that this new approach provides successful identification performance with over 98 % identification rate. Furthermore, it is demonstrated that the separately evaluation of real and imaginary parts of the Fourier transform provides around 4.7 % improvement.



中文翻译:

一种用于住宅负载设备识别的新颖方法

对房屋的能耗进行分析对于房屋的能源管理非常重要。随着智能家居的扩展,房屋的能源管理变得越来越重要。要管理此扩展,应确定负载。在这项研究中,提出了一种新颖的负载设备识别方法。这种方法在提取特征时仅利用电流波形。在所提出的方法中,首先进行数据预处理以从测量中提取一个周期信号。然后,计算当前信号的快速傅立叶变换(FFT),并分别评估该变换的实部和虚部。提取统计特征,例如实部和虚部的最大,最小和标准偏差。在特征提取过程之后,确定每个负载设备在提取特征方面的边界以构建规则表,并使用这些规则识别负载设备。在这项研究中,对单个设备和设备的不同组合都进行了识别。结果表明,该新方法提供了成功的识别性能,识别率超过98%。此外,证明了对傅立叶变换的实部和虚部分别进行评估可提高约4.7%。结果表明,该新方法提供了成功的识别性能,识别率超过98%。此外,证明了对傅立叶变换的实部和虚部分别进行评估可提高约4.7%。结果表明,该新方法提供了成功的识别性能,识别率超过98%。此外,证明了对傅立叶变换的实部和虚部分别进行评估可提高约4.7%。

更新日期:2020-09-23
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