Predicting the energy output of wind farms based on weather data: important variables and their correlation

Pre-print available at: http://arxiv.org/abs/1109.1922Wind energy plays an increasing role in the supply of energy world wide. The energy output of a wind farm is highly dependent on the weather conditions present at its site. If the output can be predicted more accurately, energy suppliers can coordinate the collaborative production of different energy sources more efficiently to avoid costly overproduction. In this paper, we take a computer science perspective on energy prediction based on weather data and analyze the important parameters as well as their correlation on the energy output. To deal with the interaction of the different parameters, we use symbolic regression based on the genetic programming tool DataModeler. Our studies are carried out on publicly available weather and energy data for a wind farm in Australia. We report on the correlation of the different variables for the energy output. The model obtained for energy prediction gives a very reliable prediction of the energy output for newly supplied weather data. © 2012 Elsevier Ltd.Ekaterina Vladislavleva, Tobias Friedrich, Frank Neumann, Markus Wagne

Experimental analysis of computer system dependability

This paper reviews an area which has evolved over the past 15 years: experimental analysis of computer system dependability. Methodologies and advances are discussed for three basic approaches used in the area: simulated fault injection, physical fault injection, and measurement-based analysis. The three approaches are suited, respectively, to dependability evaluation in the three phases of a system's life: design phase, prototype phase, and operational phase. Before the discussion of these phases, several statistical techniques used in the area are introduced. For each phase, a classification of research methods or study topics is outlined, followed by discussion of these methods or topics as well as representative studies. The statistical techniques introduced include the estimation of parameters and confidence intervals, probability distribution characterization, and several multivariate analysis methods. Importance sampling, a statistical technique used to accelerate Monte Carlo simulation, is also introduced. The discussion of simulated fault injection covers electrical-level, logic-level, and function-level fault injection methods as well as representative simulation environments such as FOCUS and DEPEND. The discussion of physical fault injection covers hardware, software, and radiation fault injection methods as well as several software and hybrid tools including FIAT, FERARI, HYBRID, and FINE. The discussion of measurement-based analysis covers measurement and data processing techniques, basic error characterization, dependency analysis, Markov reward modeling, software-dependability, and fault diagnosis. The discussion involves several important issues studies in the area, including fault models, fast simulation techniques, workload/failure dependency, correlated failures, and software fault tolerance

Multifidelity Uncertainty Quantification of a Commercial Supersonic Transport

The objective of this work was to develop a multifidelity uncertainty quantification approach for efficient analysis of a commercial supersonic transport. An approach based on non-intrusive polynomial chaos was formulated in which a low-fidelity model could be corrected by any number of high-fidelity models. The formulation and methodology also allows for the addition of uncertainty sources not present in the lower fidelity models. To demonstrate the applicability of the multifidelity polynomial chaos approach, two model problems were explored. The first was supersonic airfoil with three levels of modeling fidelity, each capturing an additional level of physics. The second problem was a commercial supersonic transport. This model had three levels of fidelity that included two different modeling approaches and the addition of physics between the fidelity levels. Both problems illustrate the applicability and significant computational savings of the multifidelity polynomial chaos method

First discovery augmented reality for learning solar systems

The development of Augmented Reality (AR) systems in educational settings should be given more attention and recognition on its contribution to the evolution of education. Although this shift of pedagogical method may disrupt the traditional curriculum model, it also offers great opportunity to complement and improve the modern age education model. This paper presents an AR-based mobile application for exploring Space and Science for primary school students called the First Discovery (FD). This application supplements a traditional book that contains 10 target images for solar system and its planets, which can be scanned by the AR camera in FD application. Evaluation was carried out among primary school children, elementary educators as well as parents, which showed a highly favorable response. It is hoped that the proposed FD application is able to improve the ability of children in retaining knowledge after the AR science learning experience, to enhance information accessibility of the science learning content for children as well as to develop creative learning and the ability of children in exploring and problem solvin

Series of experiments for empirical validation of solar gain modelling in building energy simulation codes - experimental setup, test cell characterization, specifications and uncertainty analysis

Empirical validation of building energy simulation codes is an important component in understanding the capacity and limitations of the software. Within the framework of Task 34/Annex 43 of the International Energy Agency (IEA), a series of experiments was performed in an outdoor test cell. The objective of these experiments was to provide a high-quality data set for code developers and modelers to validate their solar gain models for windows with and without shading devices. A description of the necessary specifications for modeling these experiments is provided in this paper, which includes information about the test site location, experimental setup, geometrical and thermophysical cell properties including estimated uncertainties. Computed overall thermal cell properties were confirmed by conducting a steady-state experiment without solar gains. A transient experiment, also without solar gains, and corresponding simulations from four different building energy simulation codes showed that the provided specifications result in accurate thermal cell modeling. A good foundation for the following experiments with solar gains was therefore accomplished