Browsing by Subject "Zeitreihenanalyse"

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Experimentelle Entwicklung einer modellbasierten prädiktiven Regelung für den flexiblen Betrieb von Biogasanlagen
(2023) Dittmer, Celina; Lemmer, Andreas
The transformation of the energy system requires controllable producers due to increasingly decentralised, fluctuating electricity generation from wind turbines and photovoltaics. Biogas plants can make a substantial contribution here by making plant operation more flexible and thus providing electricity as needed. Technical adjustments, such as the expansion of gas storage capacities and CHP output, can compensate for short-term fluctuations. However, in order to be able to shift the potential of electricity generation over longer periods of time, an adapted feed-in strategy is essential. The control of biogas production poses several challenges in practical implementation. First, the conversion of biomass into biogas is a complex process and must be considered individually for each biogas plant. Models developed so far use parameters for all characteristic process phases and influencing variables in order to be able to model anaerobic digestion. In contrast, biogas plants are often only rudimentarily equipped with measurement technology, so that corresponding parameters are not available. In this work, a model-predictive control of biogas plant operation was developed to enable demand-driven electricity generation. The aim was to develop models that are particularly well suited for practical use. Thus, for the first time, a successful application on almost all biogas plants could be possible without or with only minor adaptations to the existing measurement technology. All studies carried out in this thesis are based on a real-world laboratory, the "Unterer Lindenhof". This includes a practical biogas plant as well as an electrical consumption corresponding to that of a village with about 125 inhabitants. In a first step, forecasting models were evaluated to predict the electricity demand of the real-world laboratory over 48 hours in advance. Four models from the field of time series analysis were examined, one TBATS and three different ARIMA models. In an evaluation of 366 forecasts each, all four models performed sufficiently well to provide a set point for biogas plant operation, with average MAPE values of 13-16 %. Further investigations showed that forecasts can also be carried out over a period of up to 14 days without significant losses in forecast quality. In a further step, a model was developed to simulate biogas production. This is also based on time series analysis, or more precisely on a regression model. Thus, it differs significantly from previous developments in this field, which are mostly based on the complex ADM1. It turns out to be very advantageous that the developed simulation model uses as input parameters only historical data of the last four weeks of biogas production and the amount of solid substrates fed in, without considering their composition. The simulation of biogas production over 48 hours in advance is based on correlations resulting from these two data sets. An evaluation of the model over 366 simulations resulted in an average MAPE of 14-18 %. Data from both digesters of the biogas plant were used, which can be considered as independent systems, demonstrating the adaptability of the model. In a third step, the feeding schedule was developed for demand-based biogas production. For each 48 hours in advance, 1500 randomised feeding schedules were calculated. Some constraints were imposed, such as the maximum amount of substrate that is technically possible in the biogas plant. The biogas production expected from the feeding schedules could be calculated using the simulation model. By comparing the simulation with the desired biogas demand profile, the simulation with the least deviations could be determined and the appropriate feeding plan selected and implemented. The entire model predictive control system was used and thoroughly tested in a field trial at the real-world laboratory "Unterer Lindenhof". Over a period of 36 days, an average MAPE of less than 20 % was achieved in comparison between the real biogas production and the desired biogas demand. During the test period, the biogas demand was derived from the predicted electricity demand of the real-world laboratory. The investigations carried out show that the model-predictive control system developed enables demand-oriented electricity generation on full-scale and that, due to the models being very close to practice for the first time, adaptation to almost all biogas plants is possible.
Modelling nonlinearities in cointegration relationships
(2017) Schweikert, Karsten; Jung, Robert
This thesis is concerned with the statistical modelling of long-run equilibrium relationships between economic variables. It comprises of four main chapters - each representing a standalone research paper. The connecting thread is the use of nonlinear cointegration models. More precisely: Chapter 2, Asymmetric price transmission in the US and German fuel markets: A quantile autoregression approach, proposes a new econometric model for asymmetric price transmissions using quantile regressions. Chapter 3, Are gold and silver cointegrated? New evidence from quantile cointegration, investigates the potentially nonlinear long-run relationship between gold and silver prices. Chapter 4, Testing for cointegration with SETAR adjustment in the presence of structural breaks, develops a new cointegration test with SETAR adjustment allowing for the presence of structural breaks in the equilibrium equation. Chapter 5, A Markov regime-switching model of crude oil market integration, revisits the globalization-regionalization hypothesis for the world crude oil using a Markov-switching vector error correction model.
Sediment, carbon and nitrogen capture in mountainous irrigated rice systems
(2016) Slaets, Johanna I. F.; Cadisch, Georg
Anthropogenic influences have caused landscapes to change worldwide in the last decades, and changes have been particularly intense in montane Southeast Asia. Traditional swiddening cropping systems with low environmental impacts have been largely replaced by forms of permanent upland cultivation, often with maize. The associated soil fertility loss at the plot scale is well documented. In valley bottoms of these areas, paddies have been cultivated for centuries, and are considered some of the most sustainable production systems in the world – in part maintained by the influx of fertile sediments through irrigation. Altered cropping patterns on the slopes therefore also have potential repercussions on rice production, and hence on food security, but the consequences of shifted sediment and nutrient redistribution at the landscape scale are not well understood. In order to assess these effects, methodologies were developed in this thesis that enable low-cost, continuous monitoring of sediment and nutrient transport in irrigated watersheds (Chapter 2), as well as quantification of the uncertainty on constituent loads (Chapter 3). These methods are applied in a case study to determine sediment, organic carbon and nitrogen trap efficiency of paddy rice fields in a mountainous catchment in Vietnam (Chapters 4 and 5). The upland area had an average erosion rate of 7.5 Mg ha-1 a-1. Sediment inputs to the paddy area consisted of 64 Mg ha-1 a-1, of which irrigation water provided 75% and the remainder came from erosion during rainfall events. Erosion contributed one third of the sand inputs, while sediments from irrigation water were predominantly silty, demonstrating the protective effect of the reservoir which buffered the coarse, unfertile material. Almost half of the total sediment inputs were trapped in the rice area. As all of the sand inputs remained in the rice fields, the upland-lowland linkages could entail a long-term change in topsoil fertility and eventually a rice yield loss. Quantification of nutrient re-allocation in Chapter 5 showed that irrigation was even more important as a driver of sediment-associated organic carbon and nitrogen inputs into the rice fields, contributing 90% of carbon and virtually all nitrogen. Direct contributions from erosion to the nutrient status of the paddies were negligible, again underscoring the protective function of the surface reservoir in buffering irrigated areas from unfertile sediment inputs. 88% of the sediment-associated organic carbon and 93% of the nitrogen were captured by the rice fields. Irrigation water additionally brought in dissolved nitrogen, resulting in a total nitrogen input of 1.11 Mg ha 1 a-1. Of this amount, 24% was determined to be in the plant-available forms of ammonium and nitrate, a contribution equivalent to 66% of the recommended nitrogen application via chemical fertilizer. The dependence of paddy soil fertility on agricultural practices in the uplands illustrates the vulnerability of irrigated rice to unsustainable land use in the surrounding landscape. Unfortunately, alternatives for upland land use that are not detrimental to soil quality are hard to come by, due to the economic reality of high maize prices on the world market. Conservation measures and agroforestry systems offer potential, but without some form of payment for environmental services, adoption rates remain low. Finding sustainable solutions is especially urgent as climate change is likely to increase the number of extreme rainfall events and hence intensify the redistribution processes already taking place. In this light, the role of trapping elements in landscapes such as paddy fields and surface reservoirs becomes more important as well. As these features are widely spread throughout tropical landscapes, their role in global sediment and nutrient cycles must be taken into account. The methodologies developed in this thesis, for sediment and nutrient transport monitoring and for uncertainty assessment, can aid in closing the data gap that currently hinders a reliable assessment of the consequences of anthropogenic and climate change, both on food security and on environmental impacts, locally, regionally and globally.
SPECTRAN, a set of Matlab programs for spectral analysis
(2012) Marczak, Martyna; Gómez, Víctor
Spectral analysis is one of the most important areas of time series econometrics. The use of spectral measures is widespread in different science fields such as economics, physics, engineering, geology. The SPECTRAN toolbox has been developed to facilitate the application of spectral concepts to univariate as well as to multivariate series. It offers a variety of frequency-domain techniques and supports the statistical inference. It also provides convenient tools for the examination of the results, e.g.functions for writing the output to a file or functions specially designed for plotting the estimated spectral measures. The key feature of SPECTRAN is the user-friendliness embodied in, e.g., the central function spectran which performs the whole analysis with default settings, but also gives the user the possibility to adjust them. This document sets out the most relevant spectral concepts and their implementation in SPECTRAN. Finally, three examples shall illustrate the application of different toolbox function to macroeconomic data.