Research Information System
SUSTAINABLE CITIES AND SOCIETY, vol.63, 2020 (SCI-Expanded, Scopus)
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.