7 resultados para Data-driven energy e ciency

em Universitätsbibliothek Kassel, Universität Kassel, Germany


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Die Inspektion von Schule und Unterricht hat eine lange Tradition. Auf der Grundlage von Dokumenten aus der hessischen Schulgeschichte wird gezeigt, dass anfänglich der einzelne Lehrer, sein sittliches Verhalten und insbesondere sein Umgang mit der unterrichtlichen Disziplin im Zentrum der Beobachtungen stand. Das moderne Inspektionswesen nutzt demgegenüber das sozialwissenschaftliche Instrumentarium um anstelle punktueller Erfolgskontrollen schulische und unterrichtliche Entwicklungsprozesse in ihrer Komplexität zu beobachten, zu beschreiben und zu bewerten.

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Conceptual Information Systems unfold the conceptual structure of data stored in relational databases. In the design phase of the system, conceptual hierarchies have to be created which describe different aspects of the data. In this paper, we describe two principal ways of designing such conceptual hierarchies, data driven design and theory driven design and discuss advantages and drawbacks. The central part of the paper shows how Attribute Exploration, a knowledge acquisition tool developped by B. Ganter can be applied for narrowing the gap between both approaches.

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In dieser Arbeit werden verschiedene Computermodelle, Rechenverfahren und Methoden zur Unterstützung bei der Integration großer Windleistungen in die elektrische Energieversorgung entwickelt. Das Rechenmodell zur Simulation der zeitgleich eingespeisten Windenergie erzeugt Summenganglinien von beliebig zusammengestellten Gruppen von Windenergieanlagen, basierend auf gemessenen Wind- und Leistungsdaten der nahen Vergangenheit. Dieses Modell liefert wichtige Basisdaten für die Analyse der Windenergieeinspeisung auch für zukünftige Szenarien. Für die Untersuchung der Auswirkungen von Windenergieeinspeisungen großräumiger Anlagenverbünde im Gigawattbereich werden verschiedene statistische Analysen und anschauliche Darstellungen erarbeitet. Das im Rahmen dieser Arbeit entwickelte Modell zur Berechnung der aktuell eingespeisten Windenergie aus online gemessenen Leistungsdaten repräsentativer Windparks liefert wertvolle Informationen für die Leistungs- und Frequenzregelung der Netzbetreiber. Die zugehörigen Verfahren zur Ermittlung der repräsentativen Standorte und zur Überprüfung der Repräsentativität bilden die Grundlage für eine genaue Abbildung der Windenergieeinspeisung für größere Versorgungsgebiete, basierend auf nur wenigen Leistungsmessungen an Windparks. Ein weiteres wertvolles Werkzeug für die optimale Einbindung der Windenergie in die elektrische Energieversorgung bilden die Prognosemodelle, die die kurz- bis mittelfristig zu erwartende Windenergieeinspeisung ermitteln. In dieser Arbeit werden, aufbauend auf vorangegangenen Forschungsarbeiten, zwei, auf Künstlich Neuronalen Netzen basierende Modelle vorgestellt, die den zeitlichen Verlauf der zu erwarten Windenergie für Netzregionen und Regelzonen mit Hilfe von gemessenen Leistungsdaten oder prognostizierten meteorologischen Parametern zur Verfügung stellen. Die softwaretechnische Zusammenfassung des Modells zur Berechnung der aktuell eingespeisten Windenergie und der Modelle für die Kurzzeit- und Folgetagsprognose bietet eine attraktive Komplettlösung für die Einbindung der Windenergie in die Leitwarten der Netzbetreiber. Die dabei entwickelten Schnittstellen und die modulare Struktur des Programms ermöglichen eine einfache und schnelle Implementierung in beliebige Systemumgebungen. Basierend auf der Leistungsfähigkeit der Online- und Prognosemodelle werden Betriebsführungsstrategien für zu Clustern im Gigawattbereich zusammengefasste Windparks behandelt, die eine nach ökologischen und betriebswirtschaftlichen Gesichtspunkten sowie nach Aspekten der Versorgungssicherheit optimale Einbindung der geplanten Offshore-Windparks ermöglichen sollen.

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Genetic programming is known to provide good solutions for many problems like the evolution of network protocols and distributed algorithms. In such cases it is most likely a hardwired module of a design framework that assists the engineer to optimize specific aspects of the system to be developed. It provides its results in a fixed format through an internal interface. In this paper we show how the utility of genetic programming can be increased remarkably by isolating it as a component and integrating it into the model-driven software development process. Our genetic programming framework produces XMI-encoded UML models that can easily be loaded into widely available modeling tools which in turn posses code generation as well as additional analysis and test capabilities. We use the evolution of a distributed election algorithm as an example to illustrate how genetic programming can be combined with model-driven development. This example clearly illustrates the advantages of our approach – the generation of source code in different programming languages.

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Using the Multi-Configuration Dirac-Fock (MCDF) method we calculate with 9 configuration state functions the correlation energy as well as the total energy of the lowest J = 0 ground state of all two-electron systems from H- to Thorium (Z = 90). A comparison with experimental data, which are available only in the low Z region, shows a very good agreement.

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The principal objective of this paper is to develop a methodology for the formulation of a master plan for renewable energy based electricity generation in The Gambia, Africa. Such a master plan aims to develop and promote renewable sources of energy as an alternative to conventional forms of energy for generating electricity in the country. A tailor-made methodology for the preparation of a 20-year renewable energy master plan focussed on electricity generation is proposed in order to be followed and verified throughout the present dissertation, as it is applied for The Gambia. The main input data for the proposed master plan are (i) energy demand analysis and forecast over 20 years and (ii) resource assessment for different renewable energy alternatives including their related power supply options. The energy demand forecast is based on a mix between Top-Down and Bottom-Up methodologies. The results are important data for future requirements of (primary) energy sources. The electricity forecast is separated in projections at sent-out level and at end-user level. On the supply side, Solar, Wind and Biomass, as sources of energy, are investigated in terms of technical potential and economic benefits for The Gambia. Other criteria i.e. environmental and social are not considered in the evaluation. Diverse supply options are proposed and technically designed based on the assessed renewable energy potential. This process includes the evaluation of the different available conversion technologies and finalizes with the dimensioning of power supply solutions, taking into consideration technologies which are applicable and appropriate under the special conditions of The Gambia. The balance of these two input data (demand and supply) gives a quantitative indication of the substitution potential of renewable energy generation alternatives in primarily fossil-fuel-based electricity generation systems, as well as fuel savings due to the deployment of renewable resources. Afterwards, the identified renewable energy supply options are ranked according to the outcomes of an economic analysis. Based on this ranking, and other considerations, a 20-year investment plan, broken down into five-year investment periods, is prepared and consists of individual renewable energy projects for electricity generation. These projects included basically on-grid renewable energy applications. Finally, a priority project from the master plan portfolio is selected for further deeper analysis. Since solar PV is the most relevant proposed technology, a PV power plant integrated to the fossil-fuel powered main electrical system in The Gambia is considered as priority project. This project is analysed by economic competitiveness under the current conditions in addition to sensitivity analysis with regard to oil and new-technology market conditions in the future.

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The demand for biomass for bioenergy has increased rapidly in industrialized countries in the recent years. Biogenic energy carriers are known to reduce CO2 emissions. However, the resource-inefficient production of biomass often caused negative impacts on the environment, e.g. biodiversity losses, nitrate leaching, and erosion. The detrimental effects evolved mainly from annual crops. Therefore, the aim of modern bioenergy cropping systems is to combine yield stability and environmental benefits by the establishment of mixed-cropping systems. A particular emphasis is on perennial crops which are perceived as environmentally superior to annual crops. Agroforestry systems represent such mixed perennial cropping systems and consist of a mix of trees and arable crops or grassland within the same area of land. Agroforestry practices vary across the globe and alley cropping is a type of agroforestry system which is well adapted to the temperate zone, with a high degree of mechanization. Trees are planted in rows and crops are planted in the alleyways, which facilitates their management by machinery. This study was conducted to examine a young alley cropping system of willows and two grassland mixtures for bioenergy provision under temperate climate conditions. The first part of the thesis identified possible competition effects between willows and the two grassland mixtures. Since light seemed to be the factor most affecting the yield performance of the understory in temperate agroforestry systems, a biennial in situ artificial shade experiment was established over a separate clover-grass stand to quantify the effects of shade. Data to possible below- and aboveground interactions among willows and the two grassland mixtures and their effects on productivity, sward composition, and quality were monitored along a tree-grassland interface within the alleys. In the second part, productivity of the alley cropping system was examined on a triennial time frame and compared to separate grassland and willow stands as controls. Three different conversion technologies (combustion of hay, integrated generation of solid fuel and biogas from biomass, whole crop digestion) were applied to grassland biomass as feedstock and analyzed for its energetic potential. The energetic potential of willow wood chips was calculated by applying combustion as conversion technique. Net energy balances of separate grassland stands, agroforestry and pure willow stands evaluated their energy efficiency. Results of the biennial artificial shade experiment showed that severe shade (80 % light reduction) halved grassland productivity on average compared to a non-shaded control. White clover as heliophilous plant responded sensitively to limited radiation and its dry matter contribution in the sward decreased with increasing shade, whereas non-leguminous forbs (mainly segetal species) benefited. Changes in nutritive quality could not be confirmed by this experiment. Through the study on interactions within the alleys of the young agroforestry system it was possible to outline changes of incident light, soil temperature and sward composition of clover-grass along the tree-grassland interface. Nearly no effects of trees on precipitation, soil moisture and understory productivity occurred along the interface during the biennial experiment. Considering the results of the productivity and the net energy yield alley cropping system had lower than pure grassland stands, irrespective of the grassland seed mixture or fertilization, but was higher than that for pure willow stands. The comparison of three different energetic conversion techniques for the grassland biomass showed highest net energy yields for hay combustion, whereas the integrated generation of solid fuel and biogas from biomass (IFBB) and whole crop digestion performed similarly. However, due to the low fuel quality of hay, its direct combustion cannot be recommended as a viable conversion technique, whereas IFBB fuels were of a similar quality to wood chip from willow.