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Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve.


An efficient, appliance-level approach for energy disaggregation, exploiting the benefits of Generative Adversarial Networks, is presented. The concept of adversarial training supports the creation of fine tuned disaggregators, which produce more detailed load estimations for a specific appliance, compared to state of the art deep learning models. The Generator and Discriminator of the model are appropriately adapted to fit the particularities of NILM problem, whereas a Seeder component is added to provide encoded compact input vectors to the Generator.


Smart grids are faced with the challenge of meeting the ever increasing load demands of electric vehicles (EVs). To provide acceptable charging services, operators need to be equipped with an efficient charging stations (CSs) planning strategy. Unfortunately, existing planning solutions are quite limited. They normally rely on standard IEEE bus systems or power grids that are specific to certain cities. In this paper, using stochastic geometry, we formulate the CSs planning on a stochastic geometry-based power grid model, that we previously showed to mimic real-world power grids.


This paper proposes a novel approach to infer missing electricity meter data of a building using a seasonal autoregressive integrated moving average model with exogenous variables (SARIMAX). A cross-correlation function is utilized to identify buildings that have electricity usage patterns similar to the building with missing meter data.