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Solar energy is one option to serve the rising global energy demand with low environmental impact.1 Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light.2 However, the impact of cloudiness on photovoltaic power yields (PV) and cloud induced deviations from average yields might vary depending on the technology, location and time scale under consideration.
Solar energy is one option to serve the rising global energy demand with low environmental Impact [1]. Building an energy system with a considerable share of solar power requires long-term investment and a careful investigation of potential sites. Therefore, understanding the impacts from varying regionally and locally determined meteorological conditions on solar energy production will influence energy yield projections. Clouds are moving on a short term timescale and have a high influence on the available solar radiation, as they absorb, reflect and scatter parts of the incoming light [2]. However, modeling photovoltaic (PV) power yields with a spectral resolution and local cloud information gives new insights on the atmospheric impact on solar energy.
A novel approach to produce 2D designs by adapting the HyperNEAT algorithm to evolve non-uniform rational basis splines (NURBS) is presented. This representation is proposed as an alternative to previous pixel-based approaches primarily motivated by aesthetic interests, and not designed for optimization tasks. This spline representation outperforms previous pixel-based approaches on target matching tasks, performing well even in matching irregular target shapes. In addition to improved evolvability in the face of a well defined fitness metric, a NURBS representation has the added virtues of being continuous rather than discrete, as well as being intuitive and easily modified by graphic and industrial designers.
Work in progress: Starter-project for first semester students to survey their engineering studies
(2015)
The simulation of fluid flows is of importance to many fields of application, especially in industry and infrastructure. The modelling equations applied describe a coupled system of non-linear, hyperbolic partial differential equations given by one-dimensional shallow water equations that enable the consistent implementation of free surface flows in open channels as well as pressurised flows in closed pipes. The numerical realisation of these equations is complicated and challenging to date due to their characteristic properties that are able to cause discontinuous solutions.
Cost efficient energy monitoring in existing large buildings demands for autonomous indoor sensors with low power consumption, high performance in multipath fading channels and economic implementation. Good performance in multipath fading channels can be achieved with noncoherent chaotic modulation schemes such as chaos on-off keying (COOK) or differential chaos shift keying (DCSK). While COOK stands out in the area of power consumption, DCSK excels when it comes to its performance in noisy and multipath fading channels. This paper evaluates a combination of both schemes for autonomous indoor sensors. The simulation results show 50% less power consumption than DCSK and more than 3dB SNR gain in Rayleigh fading channels at BER=10-3 as compared to COOK, making it a promising candidate for low power transmission in autonomous wireless indoor sensors. We further present an enhanced version of this scheme showing another 1 dB SNR improvement, but at 25% less power consumption than DCSK.