In response to the increasing penetration of distributed energy resources in the distribution network and the technical challenges this transition represents, this paper presents a novel approach for photovoltaic (PV) systems identification in the residential sector. Non-intrusive Load Monitoring (NILM) techniques have been focused mostly in identifying conventional loads on the customer side, thus more emphasis on distributed generation being integrated into the electrical grid is required to ensure system flexibility and most importantly stability of the electrical system. The proposed methodology combines basic statistics with the conventional machine learning Support Vector Machine, to identify PV load signatures from aggregated measurements in the residential sector using OpenPMU measurements. The main contributions of this paper are based on improving processing times of the conventional machine learning supervised algorithm and also providing important information for network operators based on simple techniques using electric current records from OpenPMU measurements.

International policies and targets to globally reduce carbon dioxide emissions have contributed to increasing penetration of distributed energy resources (DER) in lowvoltage distribution networks. The growth of technologies such as rooftop photovoltaic (PV) systems and electric vehicles (EV) has, to date, not been rigorously monitored and record keeping is deficient. Non-intrusive load monitoring (NILM) methods contribute to the effective integration of clean technologies within existing distribution networks. In this study, a novel NILM method is developed for the identification of DER electrical signatures from smart meter net-demand data. Electrical profiles of EV and PV systems are allocated within aggregated measurements including conventional electrical appliances. Data from several households in the United States are used to train and test classification and regression models. The usage of conventional machine learning techniques provides the proposed algorithm with fast processing times and low system complexity, key factors needed to differentiate highly variable DER power profiles from other loads. The results confirm the effectiveness of the proposed methodology to individually classify DER with performance metrics of 96% for EV and 99% for PV. This demonstrates the potential of the proposed method as an embedded function of smart meters to increase observability in distribution networks. K E Y W O R D Sdemand-side management, distributed energy resources, non-intrusive load monitoring, smart grids, supervised machine learning methodsThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Transport sector electrification represents an increase in the number of electric vehicles (EV), producing significant variations in the distribution network dynamics. As a result, bidirectional power flow, overload and load unbalances are caused at the low voltage level due to unexpected increased load peaks. Non-intrusive load monitoring (NILM) methods have been developed as a strategy for energy management systems, applied to the customer side producing energy savings. This research presents a NILM methodology based on a low complexity conventional supervised machine learning pipeline. Our approach uses Principal Component Analysis (PCA) and Random Forest (RF) to detect the presence of a charging electric vehicle on the electricity network. By processing low sampling rate active power data, this approach provides a simple but feasible method that can be applied to smart meters. This provides useful data analysis for distribution network operators (DNO) to effectively deal with variability caused by these low carbon loads in the distribution grid. Achieving an overall efficacy of 92.68%, the proposed method can be compared with other state of the art methods developed under higher complexity techniques.