Transportation has a major impact on our society and environment, contributing 70% of U.S petroleum use, 28% of U.S. greenhouse gas (GHG) emissions, over 34,000 fatalities and 2.2 million injuries in 2013. Punitive approaches to used to tackle environmental issues in the transportation sector, such as congestion pricing have been well documented, although the use of incentives or rewards lags behind in comparison. In addition to the use of more fuel-efficient, alternate energy vehicles and various other energy reduction strategies; energy consumption can be lowered through incentivizing alternative modes of transportation. This paper focused on modifying travelers’ behavior by providing rewards to enable shifts to more energy-efficient modes, (e.g., from auto to either bus or bicycles). Optimization conditions are formulated for the problem to understand solution properties, and numerical tests are carried out to study the effects of system parameters (e.g., token budget and coefficient of tokens) on the optimal solutions (i.e., energy savings). The multinomial logit model is used to formulate the full problem, comprised of an objective function and constraint of a token budget ranging from $5,000-$10,000. Comparably, the full problem is computationally reduced by various parameterization strategies, in that the number of tokens assigned to all travelers’ is parameterized and proportional to the expected energy savings. An optimization solution algorithm is applied with a global and local solver to solve a lagrangian sub-problem and a duo of heuristic solution algorithms of the original problem. These were determined necessary to attain an optimal and feasible solution. Input data necessary for this analysis is obtained for the Town of Amherst, MA from the Pioneer Valley Planning Commission (PVPC). The results demonstrated strong evidence to conclude a positive correlation between the system’s energy savings and the aforementioned system parameters. The local and global solvers solution algorithm reduced the average energy consumption by 11.48% - 19.91% and12.79% – 21.09% consecutively for the identified token budget range from a base case scenario with no tokens assigned. The duo of lagrangian heuristic algorithms improved the full problems solution i.e., higher energy savings, when optimized over a local solver, while the parameterized problem formulations resulted in higher energy savings when compared to the full problems’ formulation solution over local solver, but higher energy savings compared over the global solver. The Computational run-time for the global and local solvers solution algorithm for the full problem formulation required 43 hours and 24 minutes consecutively, while the local solver for the lagrangian heuristics and parameterized problem solution algorithm took 13 minutes and 7 minutes consecutively. Future research on this paper will be comprised of a bi-level optimization problem formulation where a high level optimization aims at maximizing system-wide energy savings, while a low-level consumer surplus maximization problem is solved for each system user.

Philosophiae Doctor - PhD / Wireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These three models are complemented by a load-balancing model structured to balance energy consumption on the network of cluster heads that forms the backbone for any cluster-based hierarchical sensor network. We present two novel approaches for clustering the nodes of a hierarchical sensor network: a) a distance-aware clustering where nodes are clustered based on their distance and the residual energy and b) a service-aware clustering where the nodes of a sensor network are clustered according to their service offered to the network and their residual energy. These approaches are implemented into a family of routing protocols referred to as EOCIT (Energy Optimization using Clustering Techniques) which combines sensor node energy location and service awareness to achieve good network performance. Finally, building upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed as a novel multipath routing protocol that finds energy efficient routing paths for sensor readings dissemination from the cluster heads to the sink/base station of a hierarchical sensor network. Our simulation results reveal the relative efficiency of the newly proposed approaches compared to selected related routing protocols in terms of sensor network lifetime maximization.

This diploma thesis describes possibilities of thermal insulation of a detached house and choice optimization of insulating materials especially based on economic and ecologic factors. The thesis describes basic physical effects that are associated with heat transfer and that should be respected in insulation design. These effects influence the choice of suitable insulating materials based on their properties.

The diploma thesis with research of efficiency of renewable and low-potential energy sources of buildings. It is available on numerical simulations for sharing office and heating and cooling system buildings in DesignBuilder. There are various energy sources and ways of controlling heating and cooling. The results are evaluated in terms of time, after the expected compromises in the building, in terms of energy consumption and its price.

Cílem práce byl návrh a implementace řídicích algoritmů pro optimalizaci spotřeby energie elektrického vozidla. Hlavním úkolem byla optimalizace rozložení energie mezi hlavním zdrojem energie (bateriemi) a super-kapacitory v průběhu jízdního cyklu. Jízdní výkonový profil je odhadován a předpovězen na základě 3D geografických souřadnic a matematického modelu vozidla. V první části jsou uvedeny komponenty vozidla a jejich modely. Poté jsou představeny algoritmy na základě klouzavého průměru a dynamického programování. Byly provedeny simulace a analýzy pro demostraci přínosů algoritmů. V poslední části je popsána Java implementace algoritmů a také aplikace pro operační systém Android.

The research part of the diploma thesis is focused on an issue of energy consumption in sludge treatment at wastewater treatment plants divided by the number of population equivalent. The aim of the practical part is to set indicators and propose evaluative criteria concerning energy performance of urban wastewater treatment plants. The verification of proposed criteria and energetic analysis result from data of the wastewater treatment plant in Novy Jicin.

The main aim of this thesis is design air-conditioning system and optimization of indoor climate by solar shading. In theoretical part I focused on dividing and description off different types of solar shading. In the next part I designed suitable air-conditioning system for the open space office. The last part is first concentrating on the simulation of the indoor climate in software BSim2014. And then on evaulation of the appropriate solar shading system, whick I can use in the building.

Die verschiedenen Maßnahmen zur Senkung des Kraftstoffverbrauchs von dieselbetriebenen Schienenfahrzeugen sind Gegenstand der Forschung und werden in den kommenden Jahren, bedingt durch weltweit anwachsendes Transportvolumen, begrenzte Ressourcen und steigendes Umweltbewusstsein, weiter an Bedeutung zunehmen. Die Nutzung der Abgasenthalpie des Dieselmotors stellt dabei eine Möglichkeit zur Reduktion des Kraftstoffverbrauchs dar. Im Vergleich zu anderen Methoden ist der absolute Einfluss auf den Verbrauch zwar geringer, eine Kombination ist jedoch möglich und führt zu einer Verbesserung der Energieeffizienz. In dieser Arbeit wird die Möglichkeit der Abgasenthalpienutzung mittels thermoelektrischer Generatoren (TEG) betrachtet. Diese hat den Vorteil einer hohen Leistungsdichte, eines geringen Wartungsaufwands sowie einer einfachen Integration in die Topologie der exemplarisch untersuchten Lokomotive. Bei der Umsetzung sind wesentliche Randbedingungen wie die herrschenden Abgastemperaturen und -massenströme das verfügbare Einbauvolumen und der maximal zulässige Abgasgegendruck, zu beachten. Weiterhin stellt sich die thermomechanische Festigkeit der thermoelektrischen Module (TEM) als Herausforderung dar. Dies macht eine Optimierung auf Systemebene unabdingbar, welche mit numerischen Methoden effizient und wirtschaftlich durchgeführt werden kann. Aufgrund der Systemkomplexität und unterschiedlichen Skalierungsebenen der Systemkomponenten wird dazu ein Multi-Layer-Multi-Scale Optimierungsansatz vorgestellt, welcher eine realitätsnahe Konzeption und Bewertung des Systems ermöglicht. Wesentliche Fragestellungen der thermomechanischen Festigkeit des Moduls, der technischen Realisierbarkeit bis hin zur Gestaltung einer systemleistungsorientierten Betriebsstrategie werden im Laufe des implementierten Prozesses mit Hinblick auf die maßgebende maximale Systemleistung adressiert und beantwortet.

Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO₂-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO₂ mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO₂ concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.

Demand-Side Management concepts have the potential to positively impact the financial as well as the environmental aspects of energy-intensive industries. More specifically, they allow reducing the energy cost for the industrial plants by dealing with energy-availability fluctuations. In this context, efficient frameworks for scheduling with energy awareness have been studied and showed potential to reduce the overall energy bill for energy-intensive industries, for instance stainless steel and paper plants. Those frameworks usually combine scheduling and energy optimization into one monolithic system. This work investigates the possibility of integrating the two systems by specific exchange of signals, while keeping the scheduling model separated from the energy-cost optimization model. Such integration means that the pre-existent schedulers and energy optimizers could be easily modified and reused without re-implementing the whole new system. Two industrial problems with different scheduling approaches are studied. The first problem is about pulp and paper production which uses the Resource Task Network (RTN) scheduling approach. The second one is about stainless steel production which is based on a bi-level heuristic implementation of an improved energy-aware scheduler. This work presents the decomposition methods that are available in literature and their application to the two industrial problems. Besides an improvement in the RTN approach for handling storages, this thesis describes a prototype implementation of the energy-aware RTN scheduler for paper and pulp production. Furthermore, this work investigates the performance of the application of different decomposition methods on different problem instances. The numerical case studies show that even though the decomposition decreases the solution quality compared to the monolithic system, it still gives good solutions within an acceptable duration with the advantage of having two separate pre-existent systems which are simply exchanging signals.