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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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Personal weather stations (PWS) are gaining popularity since they provide open meteorological data with high spatial resolution. However, a number of factors may affect the quality of the measurements of these stations. PWS provide irradiance measurements using silicon-photodiode sensors, which are a low-cost and lower-maintenance option compared to thermopile-type pyranometers. Siliconphotodiode sensors present, however, several limitations related to the spectral bandwidth, the device's temperature and other affecting factors, which increase the error in the measurements. This article evaluates the accuracy of irradiance measurements from PWS under all-sky conditions. We propose a calibration method to reduce the uncertainty in the measurements based on the solar zenith angle, the temperature of the device and the clear-sky index. The proposed calibration model is evaluated in 30 personal weather stations from different producers located in 5 climate zones worldwide over several years. An average reduction of the relative mean bias error from 18.4% to 2.8% is achieved based on 5-minute instantaneous irradiance samples for clear-sky instances and it is within a ±5% tolerance for all-sky conditions, where other metrics are also improved 1-4%. This study helps understanding the nature of uncertainty of irradiance measurements in this increasingly used data source and provides a practical calibration method to increase accuracy of PWS data.

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