Suchergebnisse

Inhaltsangabe: Die Verminderung von Treibhausgasemissionen und die Sicherung der Energieversorgung sind Schlüsselfragen unserer Zeit, die auch in der hochindustrialisierten Wirtschaft Deutschlands zunehmend in den Fokus rücken. Allerdings sind zukünftige Entwicklungen von Unsicherheiten geprägt, die durch eine Vielzahl von Faktoren und Trends beeinflusst werden. Vor dem Hintergrund der immensen Herausforderungen von Klimaschutz und Energieeffizienz untersucht die vorliegende Studie, den Einfluss von Smart Metering und Smart Grid auf die deutsche Energiewirtschaft im Jahre 2025 mit Hilfe der Szenarioanalyse. Mit der Unterstützung 18 ausgewählter Experten sowie der Auswertung von 253 Artikeln und Studien konnten in der vorliegenden Untersuchung vier alternative Szenarien für das Jahr 2025 entwickelt werden. Neben der Darstellung von Interdependenzen für die Entwicklungen bis zum Jahr 2025 und dem Zieljahr sowie der Identifikation von Treibern und Hemmnissen, sollen beteiligte Akteure die Möglichkeit erhalten, die Herausforderungen der Zukunft verstehen zu können und durch die erarbeiteten Handlungsempfehlungen aktiv mitzugestalten.

[EN] Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis. ; This study has received funding by the IMPADAPT project /CGL2013-48424-C2-1-R from the Spanish ministry MINECO with European FEDER funds and from the European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 619172 (SmartH2O: an ICT Platform to leverage on Social Computing for the efficient management of Water Consumption). ; Pastor-Jabaloyes, L.; ...

In the first chapter, I compare the forecasting accuracy of different high-frequency based volatility models. The empirical analysis shows that the HEAVY and the Realized GARCH generally outperform the rest of the models. The inclusion of overnight returns considerably improves volatility forecasts for stocks across all models. Furthermore, the analysis shows that models based on realized volatility benefit much less from allowing leverage effects than do models based on daily returns. In the second chapter, the cause for this observation is investigated more deeply. I explain it by documenting that realized volatility tends to be higher on down-days than on up-days and that a similar asymmetry cannot be found in squared daily returns. I show that leverage effects are present already at high return-frequencies and that these are capable of generating asymmetries in realized variance but not in squared returns. In the third chapter, a conservative test based on the adaptive lasso is applied to investigate the optimal lag structure for modeling realized volatility dynamics. The empirical analysis shows that the optimal significant lag structure is time-varying and subject to drastic regime shifts. The accuracy of the HAR model can be explained by the observation that in many cases the relevant information for prediction is included in the first 22 lags. In the fourth chapter, a wild multiplicative bootstrap is introduced for M- and GMM estimators of time series. In Monte Carlo simulations, the wild bootstrap always outperforms inference which is based on standard asymptotic theory. Moreover, in most cases the accuracy of the wild bootstrap is also higher and more stable than that of the block bootstrap whose accuracy depends heavily on the choice of the block size.