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The necessity of developing alternatives to fossil combustibles as energy sources is increasingly leading towards non-conventional, renewable and distributed generation systems. To manage the transition from the present centralized grid to the future active systems consisting of multiple bi-directional energy clients it is necessary to extend the measurement systems to LV grids. The overall objective of this work is to install real-time measuring devices at a few grid locations, and use distribution state estimation techniques to implement active network management techniques on an experimental 100-kVA micro-grid

The environmental impacts of medium to large scale buildings receive substantial attention in research, industry, and media. This paper studies the energy savings potential of a commercial soccer stadium during day-to-day operation. Buildings of this kind are characterized by special purpose system installations like grass heating systems and by event-driven usage patterns. This work presents a methodology to holistically analyze the stadium's characteristics and integrate its existing instrumentation into a Cyber-Physical System, enabling to deploy different control strategies flexibly. In total, seven different strategies for controlling the studied stadium's grass heating system are developed and tested in operation. Experiments in winter season 2014/2015 validated the strategies' impacts within the real operational setup of the Commerzbank Arena, Frankfurt, Germany. With 95% confidence, these experiments saved up to 66% of median daily weather-normalized energy consumption. Extrapolated to an average heating season, this corresponds to savings of 775 MWh and 148 t of CO 2 emissions. In winter 2015/2016 an additional predictive nighttime heating experiment targeted lower temperatures, which increased the savings to up to 85%, equivalent to 1 GWh (197 t CO 2 ) in an average winter. Beyond achieving significant energy savings, the different control strategies also met the target temperature levels to the satisfaction of the stadium's operational staff. While the case study constitutes a significant part, the discussions dedicated to the transferability of this work to other stadiums and other building types show that the concepts and the approach are of general nature. Furthermore, this work demonstrates the first successful application of Deep Belief Networks to regress and predict the thermal evolution of building systems.

Abstract-Micro-electronics component and circuit design requires long computation time; to reduce this time, the use of simplification techniques has been introduced. In order to obtain a first validation of the method, a first test case is presented; the simplification techniques have been applied to the analytical expression of Y parameters of an inductor equivalent circuit. The resulting expressions have been used in the fitting process in order to reproduce the behaviour of a simulated inductor. Five different optimization algorithms, both deterministic (POWELL and DIRECT) and stochastic (CRS, CRS ENHANCED and OPTIA) have been tested for the fitting. The result of the introduction of the simplification techniques has been the reduction of the running time during the fitting. From an optimization point of view, the best results have been obtained by the stochastic algorithms CRS, and OPTIA.