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A non-intrusive load monitoring (NILM) process is intended to allow for the separation of individual appliances from an aggregated energy reading in order to estimate the operation of individual loads. In the past, electricity meters specified only active power readings, for billing purposes, thus limiting NILM capabilities. Recent progress in smart metering technology has introduced cost-effective, household-consumer-grade metering products, which can produce multiple features with high accuracy. In this paper, a new method is proposed for applying a BIRCH (balanced iterative reducing and clustering using hierarchies) algorithm as part of a multi-dimensional load disaggregation solution based on the extraction of multiple features from a smart meter. The method uses low-frequency meter reading and constructs a multi-dimensional feature space with adaption to smart meter parameters and is useful for type I as well as type II loads with the addition of timers. This new method is described as energy disaggregation in NILM by means of multi-dimensional BIRCH clustering (DNB). It is simple, fast, uses raw meter sampling, and does not require preliminary training or powerful hardware. The algorithm is tested using a private dataset and a public dataset. [ABSTRACT FROM AUTHOR]

In this paper, the modeling of storage elements and a unified theory liberated of the I–V characteristics of a specific technology is developed. First, the definition of an ideal storage element with limited energy capacity is presented, then the semiideal storage properties are discussed. The addition of power conservative two-port network denoted POPI (Pout = Pin) is added in order to define a flexible storage element in terms of characteristics. The paper demonstrates the theory by examples of storage elements integrated in power electronics systems. The ideas and methodology presented in this work can be applied to power electronics applications as well as power systems, smart grids, and microgrids, where storage is an important factor. [ABSTRACT FROM AUTHOR]

Non-intrusive load monitoring is an algorithm or process that disaggregates the total power in a facility to identify consumption of individual appliances. In this paper, a new algorithm is proposed to classify events of appliance states based on modification of the cross-entropy (CE) method. The main contribution is a formulation and solution of the problem with the CE method as a constrained optimization problem. This new technique is called the modified CE method. The proposed algorithm is simple, as it operates in real time, using low rate sampling of the active power. In addition, there is no need to train the system or to use complex hardware. The algorithm is tested using the REDD and AMPds datasets and the results are compared to several state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Energy storage may improve power management in microgrids that include renewable energy sources. The storage devices match energy generation to consumption, facilitating a smooth and robust energy balance within the microgrid. This paper addresses the optimal control of the microgrid's energy storage devices. Stored energy is controlled to balance power generation of renewable sources to optimize overall power consumption at the microgrid point of common coupling. Recent works emphasize constraints imposed by the storage device itself, such as limited capacity and internal losses. However, these works assume flat, highly simplified network models, which overlook the physical connectivity. This work proposes an optimal power flow solution that considers the entire system: the storage device limits, voltages limits, currents limits, and power limits. The power network may be arbitrarily complex, and the proposed solver obtains a globally optimal solution. [ABSTRACT FROM PUBLISHER]

Energy fraud detection bears significantly on urban ecology. Reduced losses and power consumption would affect carbon dioxide emissions and reduce thermal pollution. Fraud detection also provides another layer of urban socio-economic correlation heatmapping and improves city energy distribution. This paper describes a novel algorithm of energy fraud detection, utilizing energy and energy consumption specialized knowledge poured into AI front-end. The proposed algorithm improves fraud detection's accuracy and reduces the false positive rate, as well as reducing the preliminary required training dataset. The paper also introduces a holistic algorithm, specifying the major phenomena that disguises as energy fraud or affects it. Consequently, a mathematical foundation for energy fraud detection for the proposed algorithm is presented. The results show that a unique pattern is obtained during fraud, which is independent of a reference non-fraud pattern of the same customer. The theory is implemented on real data taken from smart metering systems and validated in real life scenarios. [ABSTRACT FROM AUTHOR]