Incorporating coincidental water data into non-intrusive load monitoring

Non-intrusive load monitoring (NILM) is among successful approaches aiding residential energy management. However, the presence of multi-mode appliances and appliances with close power values and lack of a proper volume of training dataset have remained influential in worsening the computational complexity and diminishing the accuracy of classification-based NILM algorithms. To tackle these challenges, we propose a novel classification process, which considers the correlation of water and electricity consumption of some appliances as a novel signature in the network to improve the accuracy of disaggregation process in overlapping modes and tackle the lack of proper volume of training dataset. In the first phase of the proposed method, the K-nearest neighbors method, as a fast classification technique, is employed to extract power signals of appliances with exclusive non-close power values. Then, two different deep learning-based methods are proposed to disaggregate the consumption of appliances with close consumption values considering the correlation of electricity and water consumption of some appliances. Throughout these methods the water consumption of these appliances are also disaggregated. The main objectives of the proposed methods are increasing accuracy in the close modes of power consumption, and informing consumers about the water consumption pattern of some appliances. To illustrate the proposed processes and validate its effectiveness, the Almanac Minutely Power Dataset as a real dataset with a sampling rate of 1-minute is considered. The numerical results show marked improvement with respect to the existing classification-based NILM techniques. Moreover, it shows the applicability of proposed methods in dealing with low-frequency readings carried out by existing smart meters.