The diploma thesis is focused on verifying the success of prediction models mean monthly flows for strategy control storage function of water management system. The success of prediction models is tested during three different periods. To determine the predicted inflows into the reservoirs is applied to the generator artificial flow series LTMA using the Monte Carlo method and the classical model of neuron networks. During the solution is applying the principles of adaptive control. The adaptive control is compared with simple control for improved outflow.

The diploma sthesis is focused on verifying the influence of the length of the prediction period and the accuracy of the predicted mean monthly inflows of water sources in the management of hydropower function selected water management system. To determine the predicted inflows into the reservoir system is applied zonal prediction model. When the solution is used a simulation model that is hand-built in Microsoft Excel and optimization model, which is automatically built into the program SOMVS and is applying the principles of adaptive control.

There have been significant advances in communication technologies over the last decade, such as cellular networks, Wi-Fi, and optical communication. Not only does the technology impact peoples’ everyday lives, but it also helps cities prepare for power outages by collecting and exchanging data that facilitates real-time status monitoring of transmission and distribution lines. Smart grids, contrary to the traditional utility grids, allow bi-directional flow of electricity and information, such as grid status and customer requirements, among different parties in the grid. Thus, smart grids reduce the power losses and increase the efficiency of electricity generation and distribution, as they allow for the exchange of information between subsystems. However, smart grids is not resilient under extreme conditions, particularly when the utility grid is unavailable. With the increasing penetration of the renewable energy sources (RES) in smart grids, the uncertainty of the generated power from the distributed generators (DGs) has brought new challenges to smart grids in general and smart microgrids in particular. The rapid change of the weather conditions can directly affect the amount of the generated power from RES such as wind turbine and solar panels, and thus degrading the reliability and resiliency of the smart microgrids. Therefore, new strategies and technologies to improve power reliability,sustainability, and resiliency have emerged. To this end, in this thesis, we propose a novel framework to improve the smart microgrids reliability and resiliency under severe conditions. We study the transition to the grid-connected operational mode in smart microgrids,in the absence of the utility grid, as an example of emergency case that requires fast and accurate response. We perform a comparative study to accurately predict upcoming grid-connected events using machine learning techniques. We show that decision tree models achieve the best average prediction performance. The packets that carry the occurrence time of the next grid-connected transition are considered urgent packets. Hence, we per-form an extensive study of a smart data aggregation approach that considers the priority of the data. The received smart microgrids data is clustered based on the delay-sensitivity into three groups using k-means algorithm. Our delay-aware technique successfully reduces the queuing delay by 93% for the packets of delay-sensitive (urgent) messages and the Packet Loss Rate (PLR) by 7% when compared to the benchmark where no aggregation mechanism exists prior to the small-cell base stations. As a mitigation action of the utility grid unavailability, we use the electrical vehicles (EVs) batteries as mobile storage units to cover smart microgrids power needs until the utility grid recovery. We formulate a Mixed Integer Linear Programming (MILP) model to find the best set of electrical vehicles with the objective of minimum cost. The EVs participating in the emergency power supply process are selected based on the distance and throughput performance between the base station and the EVs

The costs of patient care reached a new height. Poor management of patient flows in hospitals lead to unnecessary waiting time, a low degree of capacity utilization and expensive needless treatments. In the beginning of this paper a shortly overview of health care optimization research is shown, which leads to the implementation of interdisciplinary clinical pathways to improve the patient flow. Based on this the structure of scheduling focused clinical pathways is described. After that, a mixed integer linear programming model is shown, which is able to schedule these pathways. In the end the model is verified by an instance of a clinical pathway.

Mental illness is a major problem in today's society and many individuals have experienced a mental health problem. Severe mental illness, such as schizophrenia, bipolar disorder, and major depression, is also relatively common, and mental illness can even lead to a person taking their own life. For individuals who have such destructive thoughts, quick access to care and proper evaluation and treatment is crucial. Therefore, the value of acquiring a special ambulance with a focus on psychiatric care has been identified. However, to utilize these special ambulances to their full potential, it is important that they are placed at optimal locations.         We propose an optimization model that aims to identify optimal locations for psychiatric emergency units in a specific geographical region. A collaboration with Region Skåne allowed us to use real data, and thus perform a scenario study to evaluate the optimization model. In our scenario study, we used our model to identify the optimal placements of one, two, and three psychiatric ambulances based on population and risk probability, respectively. The results from the scenario study show that the optimal location for a certain area can vary depending on which perspective is chosen. It is therefore important to have clear and well-thought-out goals for the placement of special ambulances.

Genomförandet av en sårbarhetsanalys syftar till att identifiera svaga delar av ett system för att på effektivaste sätt förebygga och åtgärda eventuella brister i systemet. Ett sätt att identifiera de svaga delarna är att simulera olika scenarier genom att använda en matematisk modell. I den här studien byggs en matematisk modell upp med hjälp av optimeringslära, en gren inom matematiken som används för att hitta ett optimalt värde till en funktion under vissa begränsande villkor. Optimeringslära lämpar sig väl for att studera flöden, så som till exempel Stockholms kollektivtrafik. Stockholms kollektivtrafik kan ses som ett flöde av resenärer som så snabbt som möjligt vill ta sig från en punkt till en annan under begränsade villkor i form av utrymme, tid och förbindelser. Stockholms kollektivtrafik förenklas till ett system bestående av 34 noder, sammanlänkade genom SL:s spårtrafik Och stombusslinjer. Normalt trafikflöde simuleras och används som referensfall För fyra olika scenarier med begränsningar i trafiken. De fyra scenarierna är: • Kapacitetsbegränsningar på sträckan mellan Slussen och T-Centralen • Kapacitetsbegränsningar på sträckan mellan Skanstull och Gullmarsplan • Kapacitetsbegränsningar på sträckan mellan Fridhemsplan och Alvik • Inga fungerande tvärförbindelser Resultatet från simuleringarna visar att Stockholms kollektivtrafik generellt har bra resiliens men att systemet är väldigt beroende av T-Centralen och Stockholm Central som en stor del av resenärerna passerar. Efter känslighetsanalys dras slutsatsen att den matematiska modellen genererar trovärdiga resultat och optimeringslära visar sig vara ett bra verktyg vid sårbarhetsanalys av flöden. / The purpose of performing a vulnerability analysis is to identify security deficiencies in a system and to reduce the risk of harmful events in an efficient manner. One way to identify vulnerabilities is to simulate different scenarios by using a mathematical model. In this study an optimization model is used, which means that an optimal value is found for a function under some certain limiting constraints. Optimization is a good choice when dealing with flow problems, such as the public transportation system in Stockholm. The public transportation system in Stockholm can be viewed as a flow of travelers that want to move as quickly as possible from one place to another while constrained by limited capacity, time and connections. The public transportation system is simplified into 34 nodes, connected through the major routes, in form of commuter trains, subways, trams and buses. Normal traffic flow is simulated and used as a reference for  our scenarios with certain limitations in the traffic flow. The four different scenarios are: • Capacity limitations between Slussen and T-Centralen • Capacity limitations between Skanstull and Gullmarsplan • Capacity limitations between Fridhemsplan and Alvik • Non working transverse route. The simulation results prove that the Stockholm public transportation system has good resilience. However, the system is very dependent on the specific nodes T-Centralen and Stockholm Central, through which an extensive number of travelers pass by each day. A sensitivity analysis is performed on the result to ensure that the mathematical model generates credible results, and optimization theory proves to be a good tool for investigating the vulnerability of flows. / <p>www.ima.kth.se</p>

Die vorliegende Arbeit beschäftigt sich mit der Prozessführung der Fallgewichtsverdichtung. Konkret wird der Einfluss der eingetragenen kinetischen Energie und des Impulses auf die Kinematik des Fallgewichts und die Reaktion des Bodens untersucht. Die aktuell etablierte Dimensionierung der Fallgewichtsverdichtung erfolgt einzig auf Basis der potentiellen Energie des Fallgewichts. Mindestens eine weitere unabhängige Steuergröße fehlt, um den Prozess zu optimieren. Die Arbeit analysiert hierzu den Einfluss des Impulses mit 1-g-Modellversuchen. Die Beschleunigung des Fallgewichts zeigt zwei charakteristische Bereiche: (1) eine starke Überhöhung, kurz nach dem Einschlag und (2) ein Plateau, welches länger anhält und dann abrupt endet. Die Überhöhung der Beschleunigung ist direkt proportional zur quadratischen Einschlaggeschwindigkeit und ergibt sich aus der Impulsfortpflanzung im Boden. Dies mobilisiert eine zusätzliche Masse im Boden, die gemeinsam mit dem Fallgewicht mit gleicher Geschwindigkeit in den Boden eindringt. Diese Phase wird dominiert durch den Unelastischen Stoß zwischen Fallgewicht und Boden. In der zweiten Phase erfolgt ein reibungsbehafteter Lastabtrag, der mit der Änderung der Kratertiefe korreliert. Mit dem Wissen um diese Mechanismen wird ein Optimierungsparameter vorgeschlagen, Mit dem gezeigt wird, dass durch Reduktion der Einschlaggeschwindigkeit bei gleichbleibendem Impuls die Fallgewichtsverdichtung effizienter ausgeführt werden kann. Ferner wird demonstriert, dass die Kratertiefenentwicklung mit fortschreitender Ausführung progressiv oder degressiv verlaufen kann. Zusammenfassend zeigt die Arbeit, dass der Einsatz schwerer Fallmassen, die aus geringer Höhe fallen effizienter ist, als der Einsatz leichter Fallmassen, welche aus großer Höhe fallen. Der Grund dafür liegt in der nachteiligen Mobilisierung der zusätzlichen Bodenmasse, welche proportional zur quadratischen Einschlaggeschwindigkeit zunimmt und das Eindringen des Fallgewichts durch seine zusätzliche axiale Trägheit hemmt.

<p dir="ltr">The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor-rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implementation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built environment.</p>

As part of efforts to curb the protracted electricity problem in Nigeria, the government enacted the National Renewable Energy and Energy Efficiency Policy (NREEEP) in 2014. Through this policy, the country plans to increase its electricity generation from renewables to 20% by 2030. This thesis investigates the economic feasibility of this lofty goal, and as well determine the best hybrid configuration for off-grid rural/remote power generation across the six geopolitical zones of Nigeria The economic feasibility results, using Long-range Energy Alternative Planning (LEAP) tool, show that the 20% renewables goal in the Nigerian power generation mix by 2030 is economically feasible but will require vast investment, appropriate supportive mechanisms, both fiscal and non-fiscal (especially for solar PV) and unalloyed commitment on the part of the government. Moreover, the techno-economic results with Hybrid Optimization Model for Electric Renewable (HOMER) reveal Small hydro/Solar PV/Diesel generator/Battery design as the most cost-effective combination for power supply in remote/rural areas of Nigeria. Findings also highlight the better performance of this system in terms of fuel consumption and GHGs emission reduction. Lastly, the study identifies factors influencing RE development, and offers strategic and policy suggestions to advance RE deployment in Nigeria.

Submitted by Maria Aparecida (cidazen@gmail.com) on 2017-08-24T14:39:48Z No. of bitstreams: 1 José Artur.pdf: 1071380 bytes, checksum: e1992e06fe45627db90b9f36e8a88d84 (MD5) / Made available in DSpace on 2017-08-24T14:39:48Z (GMT). No. of bitstreams: 1 José Artur.pdf: 1071380 bytes, checksum: e1992e06fe45627db90b9f36e8a88d84 (MD5) Previous issue date: 2012-09-21 / This master dissertation presents a optimization mathematical programming model derived from the classical problem of transport, which aims to scale, with global optimization, the fleet of a system of road passenger transport, describing possible routes between each source/target to meet the constraints of cost (profitability) and quality of service. It covers classic methods of solution of linear programming models considered streaming networks and proposes improvements to the canonical model of the transport problem from the perspective of transit planning, and analyze the use of dynamic programming, evolutionary methods and heuristics for solving the problem of minimization of the model. / Neste trabalho é apresentado um modelo de otimização derivado do problema clássico de transporte, que tem a finalidade de dar suporte ao planejamento de transporte de passageiros , com otimização global, dimensionando a frota de veículos de transporte rodoviário, qualificando as rotas possíveis entre cada origem/destino para satisfazer as restrições de custo (rentabilidade) e qualidade de serviço. Abrange métodos clássicos de solução de modelos de programação linear considerados de fluxo contínuo de redes e propõe melhorias no modelo canônico do problema de transporte a partir da perspectiva do planejamento operacional, além de analisar o uso de métodos de programação dinâmica, métodos evolutivos e heurísticos para a solução do problema de minimização.