Event Detection for Load Disaggregation in Smart Metering A. N. Milioudis † , G. T. Andreou † , V. N. Katsanou † , K. I. Sgouras † and D. P. Labridis † † Department of Electrical and Computer Engineering Aristotle University of Thessaloniki, Greece Email: amilioud@auth.gr, gandreou@auth.gr, vkatsano@auth.gr, ksgouras@ee.auth.gr, labridis@auth.gr Abstract—The reduction of consumption is an objective of the Smart Grid paradigm. The pursuit of efficient solutions requires the knowledge that can be derived from each installation’s energy consumption measurements through Smart Metering. This work presents an event detection methodology, aimed to help in the disaggregation of the total measured energy consumption in an installation to a number of partial curves corresponding to individual appliances. The work has been conducted within the scope of the EU funded FP7 project ”CASSANDRA - A multivariate platform for assessing the impact of strategic decisions in electrical power systems”. Index Terms—Event detection, smart metering, load disaggre- gation, non intrusive load monitoring (NILM). I. I NTRODUCTION Smart Home applications are based on the concept of online monitoringand control of the Low Voltage (LV) loads. In that sense, they require the knowledge of the operational status of each LV appliance within an installation. This information can be easily utilized in the context of demand side management programs towards energy savings and efficiency by the im- plementation of personalized incentives for consumed energy reduction or peak shaving [1]–[4]. The necessity for the knowledge of the operational status of the appliances has been traditionally addressed either by installing sensors on every appliance, or by using an inter- mediate monitoring system in order to record the appliance’s operation [5]. However, this intrusive load monitoring method is considered inconvenient, due to its high cost for large scale implementations. A more simple methodology, namely the Non-Intrusive Load Monitoring (NILM), has been proposed at the early 90s [6], with the advantage of requiring only one single power meter installed at the main feeding panel, in order to monitor and identify the status of the plugged appliances. Although this approach leads to a lower implementation cost, its challenge so far has been the task of load identification from aggregated voltage and current signals. NILM algorithms rely on the utilization of the electrical and functional characteristics of the loads towards the formulation of distinct and robust data fingerprints, i.e. Load Signatures (LS). The higher the uniqueness of these load signatures, the easier the identifica- tion procedure. The latter led to a lot of research during the last decade [7]–[10], since the sufficiency of the LS constitutes the key role for successful load recognition. Moreover, several approaches regarding the implementation of the NILM concept have been proposed. These approaches utilize several load features, [11]–[14], such as the active and reactive power, the harmonic distortion, the transient behavior, and even the voltage distortion in order to structure an appro- priate data formation that describes the load’s behavior in a unique and representative way. Nevertheless, the proposed approaches in the literature are designed to take into account measuring sampling rates of at least several kHz. This produces a technological gap with respect to practices used today by electrical utilities, where measurements are typically taken per 15 minutes at best. Aiming to find a realistic common ground, the CASSANDRA platform [15] utilizes per minute measurements of active and reactive power in an installation. In this context, a new procedure had to be developed from scratch. Therefore, the goal of the CASSANDRA platform disaggregation methodol- ogy is to recognize the individual appliances within a given aggregated consumption curve and determine the duration of their operation, so as to produce an effective personalized model of each installation regarding its energy needs. The available measurements per installation consist of per minute consumption of active and reactive power. Conse- quently, the data per installation can be regarded as two distinct data vectors P and Q, respectively. Considering that the measurement data correspond to a N-minute time period, the aforementioned vectors are comprised of elements, where elements P i and Q i are the respective measured active and reactive power of the i th minute. The first step in a disaggregation methodology to be applied to these vectors, is to decompose the aggregated consumption curve to a number of partial curves corresponding to the consumption of unknown individual appliances. This proce- dure comprises an event detection algorithm, and it is the outcome of the methodology proposed here. Subsequently, an identification process has to take place, in order to assign the consumption of each partial curve to a particular type of appliance. II. METHODOLOGY The event detection methodology takes as input the ag- gregated consumption curve (one for the active and one for the reactive power) of an installation, and decomposes it in particular consumption event curves. An aggregated active power consumption curve can be considered to consist of two specific states depending on 1 2013 4th IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe), October 6-9, Copenhagen