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The main purpose of disaggregation is to decompose a signal into a set of other signals that together constitute it. This approach could be applied to audio signals, health care, home automation, ubiquitous systems and energy systems. It may be unworkable to individually measure the energy consumption of loads in a system simultaneously and, through disaggregation, we can make an inference using a main meter. The main contribution of this work is to use PCA to extract representativeness of an energy consumption signal we want to disaggregate, identifying its most relevant characteristics. The field of study is relevant because it allows information to be obtained in a simpler and cheaper way about the individual consumption of loads that make up a system. This opens up perspectives for other approaches such as smart grids and IoT. We demonstrate that when compared to other techniques, the proposal produces more accurate disaggregation results.

Energy Disaggregation Using Principal Component Analysis Representation