Non-Intrusive A/C Load Disaggregation Using Deep Learning

With the increase in global temperature, the rise in electricity demand has been accelerated. Air conditioning accounts for most of the residential power demand. In order to improve energy efficiency and manage the A/C power demand, smart meter data can be exploited to extract intelligent information about A/C power usage. In this study, we will demonstrate deep learning-based non-intrusive load monitoring algorithms (NILM) to disaggregate the A/C power consumption from the total power consumption measured by smart meter. Deep learning can provide end-to-end solutions to deal with large-scale data and is widely used to address real world challenging problems. We will use Pecan Street smart meter data to show the performances of the proposed A/C operating cycle classification and A/C power consumption estimation. Our preliminary results will manifest the effectiveness/potential of deep learning-based algorithms and suggest further investigation for performance improvement/model generalization.