A busca pelo uso de energia renovável, bem como a mitigação dos impactos antropogênicos, desempenha importante papel no desenvolvimento da sociedade contemporânea. O uso de energia de origem renovável é uma possível solução para os problemas relacionados aos impactos ambientais, em especial, às alterações climáticas. Uma importante fonte de energia renovável é a biomassa oriunda de resíduos orgânicos que, após a digestão anaeróbia, resulta em um gás rico em metano, conhecido como biogás. Sob o ponto de vista de qualidade ambiental, o aproveitamento energético dos resíduos produzidos a partir do tratamento das águas residuárias domésticas pode minimizar os impactos ambientais à medida que permite a diminuição da carga orgânica descartada na água e no solo. Adicionalmente, também é possível mitigar os efeitos negativos de emissões de metano na atmosfera quando o biogás é utilizado na produção de energia por meio das células a combustível (CaC) do tipo óxido sólido (SOFC). Neste sentido, o presente trabalho objetivou avaliar o ciclo de vida da ETE, da unidade geradora de biogás, sua purificação e uso em CaCs, identificando o potencial de mitigação dos gases do efeito estufa e de aproveitamento energético do biogás. Dentre os principais resultados obtidos, a etapa construtiva, é a principal contribuinte da demanda acumulada de energia, participando com 55% da CED, enquanto a etapa de tratamento do esgoto, fase líquida, destaca-se na produção de emissões atmosféricas, cerca de 23.500 Kg CO2 eq por dia. O potencial de emissões dos gases de efeito estufa podem ser evitados, durante todo o ciclo, em cerca de 3.000 kg CO2 eq por dia. A energia total que pode ser aproveitada com o biogás gerado na ETE e usado em CaC, do tipo SOFC, é de cerca de 14.000 kWh/dia, o que pode suprir em 100% a demanda de eletricidade da fase de tratamento. Os resultados apresentados lançam um desafio e geram oportunidades para pesquisadores e planejadores de sistemas energéticos desenvolverem estratégias ambientalmente saudáveis para a utilização dos recursos renováveis. / The search for renewable energy use and mitigation of anthropogenic impacts play an important role in the development of contemporary society. The use of energy from renewable sources is a possible solution to the problems related to environmental impacts, in particular, climate change. An important renewable energy source is biomass deriving from organic waste, after the anaerobic digestion, resulting in a gas rich in methane, known as biogas. From the point of view environmental quality, energy recovery of waste generated from the treatment of domestic wastewater can minimize environmental impacts as it allows the reduction of organic load dropped in water and soil. Additionally, it is also possible to mitigate the negative effects of methane emissions in the atmosphere when the biogas is used in the production of energy through solid oxide fuel cells (SOFC). In this sense, this study aimed at assessing the life cycle of a Wastewater Treatment Plant (WWTP), the biogas-generating unit, its purification and fuel cells use by identifying the potential mitigation of greenhouse gases and energy use of biogas. Among the main results obtained, the constructive phase is the main contributor to the cumulative energy demand, accounting for 55% of the CED, while in the step of sewage treating its particularly important the production of atmospheric emissions, about 23,500 kg CO2eq per day. The potential for emissions of greenhouse gases can be avoided throughout the cycle, at around 3,000 kg CO2eq per day. The total energy that can be produced with the biogas generated in WWTP and burned in the SOFC is approximately 14,000 kWh/day, which can provide 100% of the power demand of the treatment phase. The results presented launch challenges and generate opportunities for researchers and energy systems planners to develop strategies for environmentally healthy use of renewable resources.

Foi construído um modelo representativo do ciclo de vida da água em uma refinaria de petróleo, contemplando todos os usos a que esta se presta. Nesse contexto foram avaliados do ponto de vista ambiental, energético e econômico cenários em que etapas adicionais eram incorporadas ao tratamento de efluentes de forma a viabilizar o reúso de água e o fechamento do circuito na própria refinaria, reduzindo assim a necessidade de captação e, consequentemente, de tratamento da água bruta captada pela refinaria. O Cenário I corresponde ao cenário-base, sem implantação de nenhuma ação voltada ao reúso. No Cenário II, é incorporada a etapa adicional chamada Tratamento Fase 1, constituída por um processo de Clarificação seguido de uma Eletrodiálise Reversa (EDR) que permite o reúso de 255,7 m3/h dos 350 m3/h lançados inicialmente ao corpo hídrico no Cenário I. No Cenário III, é incorporada ao arranjo do Cenário II uma etapa de Cristalização Evaporativa para tratar o concentrado salino da EDR, recuperando, assim, mais 55,4 m3/h dos 350 m3/h lançados inicialmente, utilizando, para isso, vapor residual inicialmente não aproveitado pela refinaria. A análise ambiental foi desenvolvida por Avaliação do Ciclo de Vida (ACV) e constatou um desempenho muito semelhante dos três cenários. Apesar disso, a análise em perfil aberto, de impactos de midpoint, evidenciou ganhos ambientais significativos associados ao fechamento de circuito de água e, embora com vantagens muito discretas, o Cenário III apresentou um desempenho superior ao do Cenário II em todas as categorias e, na grande maioria delas, também superior ao desempenho do Cenário I. A análise de indicador único, em endpoint, destacou o impacto em Mudança Climática, relativo principalmente à queima de gás natural na caldeira para a geração de vapor, como o principal impacto ambiental associado ao ciclo de vida da água na refinaria, responsável por mais de 90% do valor correspondente ao resultado do indicador único. A análise energética foi desenvolvida utilizando-se o indicador de Demanda Cumulativa de Energia (CED) e resultou em um desempenho superior do Cenário I, ainda que com pequenas diferenças em relação aos Cenários II e III. O pior desempenho foi o do Cenário II. Comparando-se a contribuição relativa dos diferentes tipos de energia, destaca-se a energia de origem hidrelétrica, responsável por cerca de 80% do indicador único de CED em todos os três cenários. A análise econômica foi realizada por meio de indicadores tradicionalmente utilizados para a análise de viabilidade de projetos Taxa Interna de Retorno (TIR) e Valor Presente Líquido (VPL) , considerando, como referência, as regras de cobrança pelo uso da água vigentes na bacia do rio Paraíba do Sul. Com os preços cobrados atualmente pelo uso da água desta bacia, a implantação de ambos os cenários de reúso (II e III) não se viabiliza economicamente. Para que isso ocorra, o valor cobrado pelo uso da água teria que ser da ordem de 50 a 80 vezes maior do que o que é cobrado atualmente. Dentre os cenários de reúso, o Cenário II apresentou desempenho econômico superior ao do Cenário III. / It was built a representative model of the water life cycle within a petroleum refinery, considering all the uses in which it is applied. In this context, under environmental, energetic and economic perspective, different scenarios were analyzed, where further treatment stages were added to the wastewater treatment process so that recycled water could be provided back to the refining process, reducing, therefore, the need for freshwater intake and pretreatment by the refinery. Scenario I is the base scenario, without implementation of any water reuse aimed action. In Scenario II, it is incorporated the additional stage called Phase 1 Treatment, which consists of a Clarification process followed by an Electrodialysis Reversal (EDR).This enables the recycling of 255.7 m3/h from the 350 m3/h previously discharged to the water body in Scenario I. In Scenario III, it is incorporated to the Scenario II setting an Evaporative Crystallization process for treating the concentrated brine resulting from the EDR process. This enables the recovery of more 55.4 m3/h from the 350 m3/h initially released, using, for that, the energy from residual steam previously not used by the refinery. The environmental analysis was developed through Life Cycle Assessment (LCA) and found very similar performances for all three scenarios. Despite that, the open profile analysis, of midpoint impacts, showed significant environmental gains from the closure of the water circuit and, though with very small advantages, Scenario III showed a better performance than Scenario II in all impact categories and, in most of them, also better than Scenario I performance. The single score analysis, considering endpoint impact categories, highlighted Climate Change, specially related to the natural gas burning in the boiler for steam generation, as the main impact category associated to the water life cycle within the refinery, being responsible for more than 90% of all the value of the single score indicator. The energetic analysis was developed using the Cumulative Energy Demand (CED) indicator and resulted in a better performance of Scenario I, even if with just small differences from Scenarios II and III. The worst performance was from Scenario II. Comparing the relative contribution of the different types of energy, the hydroelectricity was the most important one, being responsible for around 80% of the CED single score in all three scenarios. The economic analysis was developed through traditional indicators used for assessing projects viability Internal Return Rate (IRR) and Net Present Value (NPV), considering, as reference, the rules of charging for water use valid nowadays at the Paraíba do Sul river basin. With the prices charged nowadays for the water use from this basin, the implementation of both reuse scenarios is not economic viable. In order to make it viable, the charged value would have to be around 50 to 80 times higher than it is today. Among the reuse scenarios, Scenario II had a better economic performance than Scenario III.

Ph.D. thesis is focused on the design of the method for simulation of energy demands of machine tool in operation at the stage of its development. Proposed method is developer on the basis of literature search in science and in industry. The method itself is composed of five related steps, that must be realised in the proper order in order to create a relevant energy profile of a machine tool. The output from the method are simulated data providing a course of comsumed energy and required power which are time dependent. Output data are obtained on the basis of the drive system simulation of machine tool through G-code, that is interpreted for simulation by the compiler into the matrix. It contains data necessary for controlling of machine tool, such as the required end points of the tool and required feed rates, to which is assigned a time value. G-code is partially time parametrized. It is then followed by full time parametrization through a of mathematical model of drive mechanisms and due the synergy of software for drive mechanisms control (Matlab Simulink) and software for physical simulation (MSC Adams) is processed into output data. As an input parameter figures also coefficient used as multiplier of the normal force of driving mechanisms, which is a function of feed rate. This loss function is obtained experimantally. In the context of Ph.D. thesis were conducted two experiments, used to verify the developed method. For each experiment is proceeded according to estabilished method and it is included a comparsion of simulation and measured data for various operating modes. The proposed method, described in the Ph.D. thesis, allows designers to summarize the energy demand of the proposed machine before its production. When correctly interpreted, the results of the method can serve as a basis for improving the energy profile and thereby increasing the energy efficiency of the machine tool.

Ph.D. thesis is focused on the design of the method for simulation of energy demands of machine tool in operation at the stage of its development. Proposed method is developer on the basis of literature search in science and in industry. The method itself is composed of five related steps, that must be realised in the proper order in order to create a relevant energy profile of a machine tool. The output from the method are simulated data providing a course of comsumed energy and required power which are time dependent. Output data are obtained on the basis of the drive system simulation of machine tool through G-code, that is interpreted for simulation by the compiler into the matrix. It contains data necessary for controlling of machine tool, such as the required end points of the tool and required feed rates, to which is assigned a time value. G-code is partially time parametrized. It is then followed by full time parametrization through a of mathematical model of drive mechanisms and due the synergy of software for drive mechanisms control (Matlab Simulink) and software for physical simulation (MSC Adams) is processed into output data. As an input parameter figures also coefficient used as multiplier of the normal force of driving mechanisms, which is a function of feed rate. This loss function is obtained experimantally. In the context of Ph.D. thesis were conducted two experiments, used to verify the developed method. For each experiment is proceeded according to estabilished method and it is included a comparsion of simulation and measured data for various operating modes. The proposed method, described in the Ph.D. thesis, allows designers to summarize the energy demand of the proposed machine before its production. When correctly interpreted, the results of the method can serve as a basis for improving the energy profile and thereby increasing the energy efficiency of the machine tool.

This master thesis deals with the question how you could use “demand side management” to influence the consumer on the energy market. Because of the increasing part of the renewable energy the stability in the energy production will drop. With new technology the building of energy storages will be one way to deal with the problem. Another way is to influence the consumer. Firstly the thesis will give a brief overview about the current development of renewable energy. This includes the production time and location of every energy sources. After that the next chapter is about the consummation side of the energy market with the prices. A detailed examination will show the biggest groups of demander and their location and time of consumption. The third chapter will describes the German electricity market including the main problems with demand side management on this market: the low price elasticity. The last chapter will take in the actual demand side management. Methods and applications will show a possible way to overcome the main problem but can’t bring a universal solution. The reasons are the low amount of data referring to renewable energy and applied demand side management and the development of a new market with a less of fossil fuel. The new market would change all actual concepts of pricing and vice versa the consumption.

With regard to autonomous driving the demands on comfort are increasing. This makes it attractive to use active suspension systems. The system developed at TU Darmstadt is able to increase driving comfort up to 28 % while maintaining driving safety compared to a passive suspension system. This paper investigates the influence of available energy and power of the active system. The investigation is based on a simulation of a quarter car model and an uneven country road. This paper shows that the more energy the active system has at its disposition, the greater is the range between a comfortable and a sporty chassis. Furthermore the driving comfort can be increased by 28 % with constant driving safety. The average power required for this is 15 W and the maximum power is 300 W.

A sudden extreme change in the weather can result in significant impact onthe life system in the present-day scenario. A well-planned prediction for damage during extreme weather conditions can have minimal impact on the grid components and efficient response and recovery models. With technology advancements and innovation in smart grid technologies we can now have accesses to uninterrupted power supply with smart utilization of energy and reduce CO2 emissions. Artificial Intelligence plays a vital role insolving present day power issues. Large amounts of data and rapid usage of computational power has accelerated to use machine learning models topredict and forecast the energy demand. Hence this study aims to determine how machine learning will improve the microgrid operation at Tezpur University. The main application areas studied in this thesis are identified as demand and load forecasting, simulating Photovoltaic (PV)production in a Microgrid and power outages. This thesis is aimed to develop and compare different ML algorithms to test validate and predict the PV production, energy demand and power outages.

Energibehov och energibesparing är något som är en viktig fråga får många hushåll just nu.Med skenande elpriser är det många som vill, och behöver, reducera sin förbrukning. En godidé för att minska energibehoven i gamla hus är att tilläggsisolera. Genom att tilläggsisoleragår det att minska energibehovet och även de uppvärmningskostnader som uppkommer.Detta arbete går ut på att studera ett småhus, byggt på 1970-talet, där den nuvarandeenergiförbrukningen jämförs med den förbrukning som blir efter att vinden hartilläggsisolerats. Efter att den nya förbrukningen har tagits fram görs även en beräkning på hurmycket solceller gynnar energiförbrukningen.Det genomförs en litteraturstudie för att få övergripande fakta om energianvändning, solcelleroch tilläggsisolering. Insamling av fakta och relevanta värden för det studerade huset erhållsgenom ett möte med de boende. Beräkningarna behandlar bland annat transmissionsförlustergenom bjälklag, energiförbrukning och producerad solel, och utförs med hjälp av två olikametoder.Huset som studeras är placerat i Degerfors, och har en boarea på 102,5 m2. Det värde somanvänds för energiförbrukningen är ett medelvärde som har räknats fram från åren 2021 och2022. Det beräknade värdet ligger på cirka 11 786 kWh per år.Resultatet av beräkningarna visar att energiförbrukningen minskar mellan 13 och 14 procentmed endast tilläggsisolering, och mellan 52 och 69 procent med tilläggsisolering och solceller.Slutsatsen är att det absolut är en god idé att tilläggsisolera om de boende vill sänkaenergiförbrukningen. Solceller gynnar energiförbrukningen, men det är endast påsommarhalvåret som de visar en tydlig skillnad. / Energy demand and energy saving is something that is an important question for manyhouseholds right now. With growing electricity prices, many people want, and need, to reducetheir consumption. A good idea to reduce energy needs in old houses is to add additionalinsulation. By additional insulation, it is possible to reduce the energy demand and also theheating costs that arise.This work consists of studying a villa, built in the 1970s, where the current energyconsumption is compared with the consumption that will be after the attic has beenadditionally insulated. After the new consumption has been estimated, a calculation is alsomade of how much solar cells benefit the energy consumption.The method used is first a literature study to obtain overall facts about energy use, solar cellsand additional insulation. Collection of facts and relevant values for the studied house isobtained through a meeting with the residents. The calculations deal with, among other things,transmission losses through joists, energy consumption and produced solar electricity, and arecarried out using two different methods.The house under study is located in Degerfors, and has a living area of 102.5 m2. The valueused for energy consumption is an average value that has been calculated from the years 2021and 2022. The calculated value is approximately 11,786 kWh per year.The results of the calculations show that energy consumption is reduced between 13 and 14percent with only additional insulation, and between 52 and 69 percent with additionalinsulation and solar cells.In conclusion, it is absolutely a good idea to additionally insulate if the residents want toreduce energy consumption. Solar cells benefit energy consumption, but it is only in thesummer half that they show a clear difference.

Energy demand forecasting has been more vital in recent years with countries setting goals to become climate neutral by 2050. Indeed, energy demand forecasting allows the understanding of drivers of the energy demand in all sectors of the economy. It also allows the planning of transformation of the future energy system. This study focuses on forecasting energy demand in Europe using a multi-country bottom-up modelling approach. The work explores ways of collecting large quantity of data to feed an energy model and method of completion for missing data series. It also aims at studying attributes that make a model user friendly and easy to use for the modelling of several countries. A model and a database are developed to answer these questions. A case application is conducted on the specific topic of the phase out of internal combustion engines in the EU to validate model dynamics and practical use. It is found that an energy demand forecasting model is easier and more time efficient to use with an included historical database. The case study shows that multi-country modelling can be relevant for policy assessment. Finally, improvements and future developments are proposed for the present work. / Prognoser för energiefterfrågan har blivit allt viktigare under de senaste åren i och med att länder har satt upp mål om att bli klimatneutrala senast 2050. Prognoser för energiefterfrågan gör det möjligt att förstå drivkrafterna bakom energiefterfrågan inom alla ekonomiska sektorer. Det gör det också möjligt att planera omvandlingen av det framtida energisystemet. Denna studie fokuserar på prognoser för energiefterfrågan i Europa med hjälp av en bottom-up-modelleringsmetod för flera länder. I arbetet undersöks olika sätt att samla in stora mängder data för att mata en energimodell och metoder för att komplettera saknade dataserier. Det syftar också till att studera attribut som gör en modell användar vänlig och lätt att använda för modellering av flera länder. En modell och en databas utvecklas för att besvara dessa frågor. För att validera modellens dynamik och praktiska användning genomförs en fallstudie om utfasningen av förbränningsmotorer i EU. Det visar sig att en modell för prognostisering av energiefterfrågan är enklare och mer tidseffektiv att använda med en inkluderad historisk databas. Fallstudien visar att modeller för flera länder kan vara relevanta för policybedömning. Slutligen föreslås förbättringar och framtida utveckling för det aktuella arbetet.

Conventional machine learning (ML) models and algorithms are constantly advancing at a fast pace. Most of this development are due to the implementation of hybrid- and ensemble techniques that are powerful tools to complement and empower the efficiency of the algorithms. At the same time, the development and demand for renewable energy sources are rapidly increasing driven by political and environmental issues in which failure to act fast enough, could lead to an existential crisis. With the phasing of non-renewable to renewable energy sources, new challenges arise due to its intermittent and variable nature. Accurate forecasting techniques plays a crucial role in addressing these challenges. In this thesis, I present a hybrid ensemble machine learning model based upon stacking, utilizing a Gradient Boosted Tree as a meta-learner to predict the energy demand for the energy area SE3 in Sweden. The Hybrid model is based on three composite models: XGBoost, CatBoost and Random Forest (RF); utilizing only features extracted from the timeseries data. For training and testing the proposed Hybrid model, hourly demand load data was gathered from Svenska Kraftnät, measuring energy consumption for the energy area SE3 from year 2016-2021. The forecasting results of the models are measured using a regression score (R-squared, which measures Explained Variance) and Accuracy (measured in terms of Mean Absolute Percentage Error). The result shows that in an experimental setting, the Hybrid model reaches a R-squared score of 0.9785 and an accuracy of 97.85%. When utilized for day-ahead prediction on unseen data outside of the scope of the training dataset, the Hybrid model reaches a R-squared score of 0.9764 and an Accuracy of 93.43%. This thesis concludes that the proposed methodology can be utilized to accurately predict the variance in the energy demand and can serve as a framework to decision makers in order to accurately predict the energy demand in Sweden. / Konventionella maskininlärningsmodeller (ML) och algoritmer utvecklas ständigt i snabb takt. Det mesta av denna utveckling beror på implementeringen av hybrid- och ensembletekniker som är kraftfulla verktyg för att komplettera och stärka effektiviteten hos algoritmer. Samtidigt ökar utvecklingen och efterfrågan på förnybara energikällor snabbt, drivet av politiska och miljömässiga motiv, där underlåtenhet att agera tillräckligt snabbt kan leda till en existentiell kris. Med utfasningen av icke-förnybara till förnybara energikällor uppstår nya utmaningar på grund av dess intermittenta och varierande karaktär. Noggranna prognostekniker spelar en avgörande roll för att hantera dessa utmaningar. I det här examensarbetet presenterar jag en hybrid ensemble maskininlärningsmodell baserad på stacking, med användning av ett Gradient Boosted Decision Tree (GBDT) som en meta-learner för att förutsäga energibehovet för energiområdet SE3 i Sverige. Hybridmodellen är baserad på tre kompositmodeller: XGBoost, CatBoost och Random Forest (RF) och använder endast features extraherade från tidsseriedata. För att utbilda och testa den föreslagna hybridmodellen samlades timbelastningsdata från Svenska Kraftnät, som mäter energiförbrukningen för energiområdet SE3 från år 2016-2021. Modellernas prognosresultat mäts med hjälp av ett regressionsmått (R-kvadrat, som mäter Explained Variance) och Accuracy (mätt i termer av Mean Absolute Percentage Error). Resultatet visar att i en experimentell miljö når hybridmodellen en R-kvadratvärde på 0,9785 och en Accuracy på 97,85%. När hybridmodellen används för att förutsäga energiförbrukningen dagen framåt på data utanför omfattningen av träningsdata, når hybridmodellen ett R-kvadratpoäng på 0,9764 och en Accuracy på 93,43%. Denna avhandling drar slutsatsen att den föreslagna metoden kan användas för att korrekt förutsäga variansen i energibehovet och kan fungera som ett ramverk för beslutsfattare för att korrekt prognostisera energibehovet i Sverige.