In this research, carbon emissions and carbon savings in the smart grid are modelled and quantified. Carbon emissions are defined as the product of the activity (energy) and the corresponding carbon factor. The carbon savings are estimated as the difference between the conventional and improved energy usage multiplied by the corresponding carbon factor. An adaptive seasonal model based on the hyperbolic tangent function (HTF) is developed to define seasonal and daily trends of electricity demand and the resultant carbon emissions. A stochastic model describing profiles of energy usage and carbon emissions for groups of consumers is developed. The flexibility of the HTF for modelling cycles of energy consumption is demonstrated and discussed with several case studies. The analytical description to determine electricity grid carbon intensity in the UK is derived, using the available fuel mix data from the Elexon portal. The uncertain realisation of energy data is forecasted and assimilated using the ensemble Kalman filter (EnKF). The numerical optimisation of carbon emissions and savings in the smart grid is further performed using the ensemble-based Closed-loop Production Optimisation Scheme (EnOpt). The EnOpt involves the optimisation of fuel costs and carbon emissions (maximisation of carbon savings) in the smart grid subject to the operational control constraints. The software codes for the based on the application of EnKF and EnOpt are developed, and the optimisation of energy, cost and emissions is performed. The numerical simulation shows the ability of EnKF in forecasting and assimilating the energy data, and the robustness of the EnOpt in optimising costs and carbon savings. The proposed approach addresses the complexity and diversity of the power grid and may be implemented at the level of the transmission operator in collaboration with the operational wholesale electricity market and distribution network operators. The final stage of work includes the quantification of carbon emissions and savings in demand response (DR) programmes. DR programmes such as Short Term Operating Reserve (STOR), Triad, Fast Reserve, Frequency Control by Demand Management (FCDM) and smart meter roll-out are included, with various types of smart interventions. The DR programmes are modelled with appropriate configurations and assumptions in power plants used in the energy industry. This enables the comparison of emissions between the business-as-usual (BAU) and the smart solutions applied, thus deriving the carbon savings. Several case studies involving the modelling and analysing DR programmes are successfully performed. Thus, the thesis represents novel analytical and numerical techniques applied in the fast-growing UK market of smart energy solutions.

Ensemblestück für Oboe, Klarinette, Fagott, Marimba, 2 Violinen, Viola, Violoncello und Kontrabass. Das in freier Tonalität gehaltene Werk orientiert sich an der Sonatensatzform. Aufführungsdauer: 8\''20\".

Le suivi de la mise en place du magma à faible profondeur et de sa migration vers la surface est crucial pour prévoir les éruptions volcaniques.Avec les progrès récents de l'imagerie SAR et le nombre croissant de réseaux GNSS continus sur les volcans, il est maintenant possible de fournir une évolution continue et spatialement étendue des déplacements de surface pendant les périodes inter-éruptives. Pour les volcans basaltiques, ces mesures combinées à des modèles dynamiques simples peuvent être exploitées pour caractériser et contraindre la mise en pression d'un ou de plusieurs réservoirs magmatiques, ce qui fournit une meilleure information prédictive sur l'emplacement du magma à faible profondeur. L'assimilation de données—un processus séquentiel qui combine au mieux les modèles et les observations, en utilisant parfois une information a priori basée sur les statistiques des erreurs, pour prédire l'état d'un système dynamique—a récemment gagné en popularité dans divers domaines des géosciences. Dans cette thèse, je présente la toute première application de l'assimilation de données en volcanologie en allant des tests synthétiques à l’utilisation de données géodésiques réelles.La première partie de ce travail se concentre sur le développement de stratégies afin d'évaluer le potentiel de l’assimilation de données. En particulier, le Filtre de Kalman d'Ensemble a été utilisé avec un modèle dynamique simple à deux chambres et de données géodésiques synthétiques pour aborder les points suivants : 1) suivi de l'évolution de la pression magmatique en profondeur et des déplacements de surface et estimation des paramètres statiques incertains du modèle, 2) assimilation des données GNSS et InSAR, 3) mise en évidence des avantages ou des inconvénients de l'EnKF par rapport à une technique d'inversion bayésienne. Les résultats montrent que l’EnKF fonctionne de manière satisfaisante et que l'assimilation de données semble prometteuse pour la surveillance en temps réel des volcans.La deuxième partie de la thèse est dédiée à l'application de la stratégie mise au point précédemment à l’exploitation des données GNSS inter-éruptives enregistrées de 2004 à 2011 au volcan Grímsvötn en Islande, afin de tester notre capacité à prédire la rupture d'une chambre magmatique en temps réel. Nous avons introduit ici le concept de ``niveau critique'' basé sur l’estimation de la probabilité d'une éruption à chaque pas de temps. Cette probabilité est définie à partir de la proportion d'ensembles de modèles qui dépassent un seuil critique, initialement assigné selon une distribution donnée. Nos résultats montrent que lorsque 25 +/- 1 % des ensembles du modèle ont dépassé la surpression critique une éruption est imminente. De plus, dans ce chapitre, nous élargissons également les tests synthétiques précédents en améliorant la stratégie EnKF d'assimilation des données géodésiques pour l'adapter à l’utilisation de données réelles en nombre limité. Les outils de diagnostiques couramment utilisés en assimilation de données sont mis en oeuvre et présentés.Enfin, je démontre qu'en plus de son intérêt pour prédire les éruptions volcaniques, l'assimilation séquentielle de données géodésiques basée sur l'utilisation de l'EnKF présente un potentiel unique pour apporter une information sur l'alimentation profonde du système volcanique. En utilisant le modèle dynamique à deux réservoirs pour le système de plomberie de Grímsvötn et en supposant une géométrie fixe et des propriétés magmatiques invariantes, nous mettons en évidence que l'apport basal en magma sous Grímsvötn diminue de 85 % au cours des 10 mois précédant le début de l'événement de rifting de Bárdarbunga. La perte d'au moins 0.016 km3 dans l'approvisionnement en magma de Grímsvötn est interprétée comme une conséquence de l'accumulation de magma sous Bárdarbunga et de l'alimentation consécutive de l'éruption Holuhraun à 41 km de distance. / Tracking magma emplacement at shallow depth as well as its migration towards the Earth's surface is crucial to forecast volcanic eruptions.With the recent advances in Interferometric Synthetic Aperture Radar (InSAR) imaging and the increasing number of continuous Global Navigation Satellite System (GNSS) networks recorded on volcanoes, it is now possible to provide continuous and spatially extensive evolution of surface displacements during inter-eruptive periods. For basaltic volcanoes, these measurements combined with simple dynamical models can be exploited to characterise and to constrain magma pressure building within one or several magma reservoirs, allowing better predictive information on the emplacement of magma at shallow depths. Data assimilation—a sequential time-forward process that best combines models and observations, sometimes a priori information based on error statistics, to predict the state of a dynamical system—has recently gained popularity in various fields of geoscience (e.g. ocean-weather forecasting, geomagnetism and natural resources exploration). In this dissertation, I present the very first application of data assimilation in volcanology from synthetic tests to analyzing real geodetic data.The first part of this work focuses on the development of strategies in order to test the applicability and to assess the potential of data assimilation, in particular, the Ensemble Kalman Filter (EnKF) using a simple two-chamber dynamical model (Reverso2014) and artificial geodetic data. Synthetic tests are performed in order to address the following: 1) track the magma pressure evolution at depth and reconstruct the synthetic ground surface displacements as well as estimate non-evolving uncertain model parameters, 2) properly assimilate GNSS and InSAR data, 3) highlight the strengths and weaknesses of EnKF in comparison with a Bayesian-based inversion technique (e.g. Markov Chain Monte Carlo). Results show that EnKF works well with the synthetic cases and there is a great potential in utilising data assimilation for real-time monitoring of volcanic unrest.The second part is focused on applying the strategy that we developed through synthetic tests in order to forecast the rupture of a magma chamber in real time. We basically explored the 2004-2011 inter-eruptive dataset at Grímsvötn volcano in Iceland. Here, we introduced the concept of “eruption zones” based on the evaluation of the probability of eruption at each time step estimated as the percentage of model ensembles that exceeded their failure overpressure values initially assigned following a given distribution. Our results show that when 25 +/- 1% of the model ensembles exceeded the failure overpressure, an actual eruption is imminent. Furthermore, in this chapter, we also extend the previous synthetic tests by further enhancing the EnKF strategy of assimilating geodetic data in order to adapt to real world problems such as, the limited amount of geodetic data available to monitor ice-covered active volcanoes. Common diagnostic tools in data assimilation are presented.Finally, I demonstrate that in addition to the interest of predicting volcanic eruptions, sequential assimilation of geodetic data on the basis of EnKF shows a unique potential to give insights into volcanic system roots. Using the two-reservoir dynamical model for Grímsvötn 's plumbing system and assuming a fixed geometry and constant magma properties, we retrieve the temporal evolution of the basal magma inflow beneath Grímsvötn that drops up to 85% during the 10 months preceding the initiation of the Bárdarbunga rifting event. The loss of at least 0.016 km3 in the magma supply of Grímsvötn is interpreted as a consequence of magma accumulation beneath Bárdarbunga and subsequent feeding of the Holuhraun eruption 41 km away.

Syftet med studien är att undersöka hur ensemblelärare med kompetens i kompinstrument som keyboard, bas eller elgitarr som saknar utbildning och kunskap i instrumentet sång, arbetar med sångare och text i ensembleundervisningen. Bakgrundslitteraturen redovisar forskning om hur genusaspekter påverkar identiteter, inflytande, sång och texten i ensembleundervisningen. Därefter presenteras forskning på lärares val av material, bedömning och metodik i ensembleundervisningen. Slutligen presenteras tidigare forskning inom sångtexter och låtskrivande i ensembleundervisning. Studien utgår från ett sociokulturellt perspektiv med fokus på lärares och elevers samspel i ensembleundervisningen. Studien utgår från semistrukturerade intervjuer med fem ensemblelärare som arbetar med ensemble på gymnasiet med inriktning på rock- och popensemble. Data från intervjuerna genomgår en tematisk analys. Studiens resultat redovisar ensemblelärarnas förutsättningar som påverkar undervisningen och deras metodik. Vidare presenteras ensemblelärarnas metoder och uppfattningar om arbetet med låttexten i undervisningen. Slutligen redovisas ensemblelärarnas syn på sångtexters funktion och betydelse för kompinstrumentalister. I diskussionen presenteras reflektioner om varför sångarna tenderar till att få en exkluderad ensembleundervisning gentemot kompinstrumentalisterna i ensemblen. Slutligen redovisas hur styrdokumentens otydliga riktlinjer för sångtext kan påverka ensembleundervisningen. / The aim of this study is to investigate how ensemble teachers with instrumental backgrounds work with singers and lyrics in ensemble class. The background literature presents research on how gender affects identities, influence, singing and the lyrics in ensemble class. Following this, research on teachers’ choice of material, assessment, and methodology for ensemble class is presented. Finally, previous research on lyrics and songwriting in ensemble teaching is presented. The study is based on a socio-cultural perspective with a focus on teachers’ and students’ interaction in ensemble class. The study is based on semi-structured interviews with five ensemble teachers who work in aesthetic programs in upper secondary school with expertise in rock and pop ensemble. Data from the interviews are processed in a thematic analysis. The results shed light on ensemble teachers’ conditions that affect their teaching and methodology. Furthermore, the ensemble teachers’ methods and perceptions about working with lyrics in the ensembles are presented. Finally, the ensemble teachers’ views on the function and significance of lyrics for the instrumentalists are presented. The discussion presents reflections on why the singers are misplaced in the ensemble class, as well as reflections on the curriculums' unclear guidelines for the lyrics impact in ensemble class.

One of the most important and challenging questions in the area of clustering is how to choose the best-fitting algorithm and parameterization to obtain an optimal clustering for the considered data. The clustering aggregation concept tries to bypass this problem by generating a set of separate, heterogeneous partitionings of the same data set, from which an aggregate clustering is derived. As of now, almost every existing aggregation approach combines given crisp clusterings on the basis of pair-wise similarities. In this paper, we regard an input set of soft clusterings and show that it contains additional information that is efficiently useable for the aggregation. Our approach introduces an expansion of mentioned pair-wise similarities, allowing control and adjustment of the aggregation process and its result. Our experiments show that our flexible approach offers adaptive results, improved identification of structures and high useability.

The capacity of renewable energy sources constantly increases world-wide and challenges the maintenance of the electric balance between power demand and supply. To allow for a better integration of solar energy supply into the power grids, a lot of research was dedicated to the development of precise forecasting approaches. However, there is still no straightforward and easy-to-use recommendation for a standardized forecasting strategy. In this paper, a classification of solar forecasting solutions proposed in the literature is provided for both weather- and energy forecast models. Subsequently, we describe our idea of a standardized forecasting process and the typical parameters possibly influencing the selection of a specific model. We discuss model combination as an optimization option and evaluate this approach comparing different statistical algorithms against flexible hybrid models in a case study.

Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniques that the data set used for parameter estimation comes from a stationary joint distribution may not hold in this context because manufacturing processes are subject to physical changes like wear and aging, denoted as process drift. This can cause the estimated model to deviate significantly from the current state of the modeled system. In this paper, we discuss the problem of estimating regression models from drifting processes and we present ensemble regression, an approach that maintains a set of regression models—estimated from different ranges of the data set—according to their predictive performance. We extensively evaluate our approach on synthetic and real-world data.

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<p dir="ltr">The volume of big data has surged, notably in unstructured textual data, comprising emails, social media, and more. Currently, unstructured data represents over 80% of global data, the growth is propelled by digitalization. Unstructured text data analysis is crucial for various applications like social media sentiment analysis, customer feedback interpretation, and medical records classification. The complexity is due to the variability in language use, context sensitivity, and the nuanced meanings that are expressed in natural language. Traditional machine learning approaches, while effective in handling structured data, frequently fall short when applied to unstructured text data due to the complexities. Extracting value from this data requires advanced analytics and machine learning. Recognizing the challenges, we developed innovative ensemble approaches that combine the strengths of multiple conventional machine learning classifiers through a probabilistic approach. Response to the challenges , we developed two novel models: the Consensus-Based Integration Model (CBIM) and the Unified Predictive Averaging Model (UPAM).The CBIM and UPAM ensemble models were applied to Twitter (40,000 data samples) and the National Electronic Injury Surveillance System (NEISS) datasets (323,344 data samples) addressing various challenges in unstructured text analysis. The NEISS dataset achieved an unprecedented accuracy of 99.50%, demonstrating the effectiveness of ensemble models in extracting relevant features and making accurate predictions. The Twitter dataset, utilized for sentiment analysis, demonstrated a significant boost in accuracy over conventional approaches, achieving a maximum of 65.83%. The results highlighted the limitations of conventional machine learning approaches when dealing with complex, unstructured text data and the potential of ensemble models. The models exhibited high accuracy across various datasets and tasks, showcasing their versatility and effectiveness in obtaining valuable insights from unstructured text data. The results obtained extend the boundaries of text analysis and improve the field of natural language processing.</p>

Den här studien ämnade undersöka utommusikaliska dimensioner av gymnasieskolans ensembleundervisning, med fokus på pop- och rockensemble. Ambitionen var att identifiera möjliga roller och identiteter hos elever för att synliggöra eventuella maktrelationer. Detta för att bidra till ökad kunskap i strävan efter en jämlik och inkluderande musikundervisning. Datainsamlingen skedde genom enskilda semistrukturerade intervjuer och fokusgrupper med gymnasieelever vid estetiska programmet. Dessa transkriberades till text och analyserades tematiskt utifrån diskursteorins begreppsapparat.  Resultatet visar ojämlika maktrelationer där framför allt gruppbildningar utifrån könsidentitet och instrument avgör vilka som kan inta attraktiva positioner och roller. Dessa maktrelationer upprätthålls genom att utesluta subjekt och grupper som kan förstås som motsatser. Vidare synliggör resultatet att ensembleformatets utgångspunkt i genrepraktiken pop- och rock kan ses som en anledning till att normer och stereotyper upprätthålls. Slutsatserna visar ett behov av en breddad undervisningsrepertoar, en bredare representation samt en ökad social medvetenhet bland lärare och elever. / This study intended to examine the extra-musical dimensions of upper secondary school ensemble teaching, pop and rock in particular. The ambition was to identify the student’s possible roles and identities in order to make any power relations visible in order to contribute to increased knowledge in the pursuit of an equal and inclusive music education. Data collection was made through individual semi-structured interviews and focus groups with students off the upper secondary high school arts program. These were transcribed into text and analyzed thematically based on the concepts of discourse theory. The results show unequal power relations where, above all, group formations based on gender and instruments are the decisive factor in who can take attractive positions and roles. Positions of power are maintained by excluding other groups that can be understood as opposites. Furthermore, the results show that the ensemble’s starting point in the practice of pop -and rock genre can be seen as a reason for norms and stereotypes to be maintained. The conclusions show a need for a broader teaching repertoire, a broader representation and an increased social awareness among teachers and students.