Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.

Increasing energy efficiency is critical towards mitigating greenhouse gas emissions from fossil-fuel combustion, reducing oil dependence, and achieving a sustainable global energy system (Greene, 2011:608). Most South African legislation and research scholars support the above statement; however, with a lack of tangible evidence, the statement is yet to be proved physically true in the South African manufacturing industry. A case study was conducted within three automotive component manufacturers located in Nelson Mandela Bay Metropolitan Municipality, with the objective of identifying energy efficiency projects; investigate the perception of company employees on energy efficiency and assessing whether there are benefits for the companies when implementing such projects. For the research methodology, the mixed mode method was used. Quantitative data was collected using energy assessments and a questionnaire was used for the collection of qualitative data. The quantitative and qualitative findings clearly demonstrate that company managers and operational staff need to have a clear understanding of the concept of 'energy efficiency'. Efficiency projects implemented include automated compressors, changing hot-water geyser settings, installation of power factor correction, and tariff structure changes. The quantitative recommendations were centered on switching off equipment when not required. As an alternative, the use of sensors, timers and other automated control devices should be investigated and implemented where feasible. Qualitatively recommendations advise that companies with employees who do not understand energy efficiency, training and awareness programmes need to be applied. Employees would then be able to put their energy saving knowledge into action. This study demonstrated that there is a need for further research to be undertaken, to improve efficiency for energy within the automotive manufacturing industry. / Environmental Management, Department of Environmental Science / M. Sc. (Environmental Management, Department of Environmental Science)

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-10-20Bitstream added on 2014-06-13T19:01:56Z : No. of bitstreams: 1 barros_rc_dr_botfca.pdf: 1218929 bytes, checksum: 6c805e9ebc41df7fb712b8ff8562dacf (MD5) / Com o fenômeno da globalização e da unificação dos mercados, novas oportunidades de negócio surgiram, exigindo dos produtores o incremento da qualidade dos serviços e o controle preciso das operações, reduzindo o custo operacional. Neste novo cenário, as propriedades rurais estão passando por grandes mudanças, transformando-se em verdadeiras empresas rurais. Cada vez mais este novo modelo econômico prima pela qualidade e pela sustentabilidade do agronegócio. Para tal, é necessário um sistema que auxilie o produtor rural a administrar o seu negócio. A maioria dos estudos estão focados em levantamento financeiro e esquecem que é essencial para a agricultura definir o Balanço Energético e determinar a sua eficiência. Vários trabalhos propostos comparam duas formas produtivas em uma determinada região, mas não existe um estudo em nível nacional. O presente trabalho propõe um modelo de sistema de informação que abrange a parte energética do agronegócio, bem como o envio destas informações para uma base centralizada, a fim de obter o modelo energético rural brasileiro. Com isto, será possível determinar o Balanço Energético e as formas mais eficientes de plantio no agronegócio. O sistema proposto é dividido em dois módulos. O primeiro é voltado aos pequenos produtores rurais, no intuito de ajuda-los na administração rural, disponibilizando relatórios gerencias para que o produtor conheça o desempenho energético do sistema agrícola implantado, podendo maximizar os resultados energéticos e melhorar a eficiência da produção. O segundo módulo é composto de um software de analise de dados, recebendo os dados enviados pelos produtores e construindo uma base nacional de informação a respeito dos resultados energéticos, podendo comparar a eficiência dos sistemas de plantio... / With the phenomenon of globalization and the unification of markets, new business opportunities have emerged, requiring producers to increase service quality and precise control of operations, reducing operating cost. In this new senary, farms are undergoing major changes, transforming themselves into true rural businesses. Increasingly, this new economic model strives for quality and sustainability of agribusiness. For such a system is needed to assist the farmer to manage your business. Most studies are focused on raising financial and forget that agriculture is essential to define the energy balance and determine its efficiency. Several studies comparing two proposed forms of production in a given region, but there is a nationwide study. This paper proposes a model of information system that covers the energy part of agribusiness, as well as sending this information to a centralized database in order to get the Brazilian rural energy model. With this, you can determine the energy balance and more efficient ways of planting in agribusiness. The proposed system is divided into two modules. The first is geared to small farmers in order to aid them in farm management, providing management reports for the producer to meet the energy performance of the agricultural system in place, the results can maximize energy and improve production efficiency. The second module consists of a software data analysis, receiving the data sent by the producers and building a national information regarding the results of energy and can compare the efficiency of cropping systems in certain regions or making a historical analysis, comparing performance over the years

The electricity cost is one of the largest components of operating costs on a belt conveyor system. This dissertation introduces a unique Conveyor Electricity Cost Efficiency Audit Methodology (CECEAM). In the CECEAM the conveyor system is evaluated from a high to detail level in order to identify opportunities to improve electricity costs. The CECEAM includes methodologies and tools developed to analyze not only the conveyor belt alone, but also the materials handling system as a whole. The outline of the dissertation is structured as follows: Chapter 1 includes the background and problem identification by means of a literature study. The main objective, as well as specific objectives, is defined in this chapter. In chapter 2, the CECEAM is introduced and an overview of the total methodology is discussed. The data acquisition part of the CECEAM; documentation, personnel, walk, technical audit as well as the conveyor database is discussed in chapter 3. Chapter 4 concentrates on the Conveyor Energy Conversion Model (CECM) and the verification thereof. The Integrated Conveyor Energy Model (ICEM) methodology is introduced (in chapter 5) and the economic evaluation concepts and energy management basics needed in the CECEAM are covered. Chapter 6 covers a CECEAM case study where the practical application of the CECEAM is illustrated with ICEM simulations, opportunity identification and recommendations. The conclusion and recommendations for further studies is proposed in chapter 7. / Dissertation (MSc (Electrical Engineering))--University of Pretoria, 2007. / Electrical, Electronic and Computer Engineering / unrestricted

Global economic trends suggest that there is a need to generate sustainable renewable energy to meet growing global energy demands. Solar energy harnessed by concentrated photovoltaic (CPV) systems has a potential for strong contributions to future energy supplies. However, as a relatively new technology, there is still a need for considerable research into the relationship between the technology and the solar resource. Research into CPV systems was carried out at the University of Ottawa’s Solar Cells and Nanostructured Device Laboratory (SUNLAB), focusing on the acquisition and assessment of meteorological and local solar resource datasets as inputs to more complex system (cell) models for energy yield assessment. An algorithm aimed at estimating the spectral profile of direct normal irradiance (DNI) was created. The algorithm was designed to use easily sourced low resolution meteorological datasets, temporal band pass filter measurement and an atmospheric radiative transfer model to determine a location specific solar spectrum. Its core design involved the use of an optical depth parameterization algorithm based on a published objective regression algorithm. Initial results showed a spectral agreement that corresponds to 0.56% photo-current difference in a modeled CPV cell when compared to measured spectrum. The common procedures and datasets used for long term CPV energy yield assessment was investigated. The aim was to quantitatively de-convolute various factors, especially meteorological factors responsible for error bias in CPV energy yield evaluation. Over the time period from June 2011 to August 2012, the analysis found that neglecting spectral variations resulted in a ~2% overestimation of energy yields. It was shown that clouds have the dominant impact on CPV energy yields, at the 60% level.

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.

Energy analysis at different stages of a building’s life-cycle allows designers and engineers to make proper design decisions, which will enhance the efficiency and energy saving measures. However, energy analysis of a building using traditional methods at every stage of the project is time-consuming and more labor intensive. Thus, energy simulations of buildings are rarely introduced in all design stages of the project. This study focuses on data transfer process from BIM model (Revit) to energy simulation model (IES ‹VE›) using OpenBIM meta-data model - Industry Foundation Classes (IFC) as an exchangeable file format. This data sharing process simplifies the complexity in energy modeling and allows to investigate different design alternatives in each phase of the building’s life-cycle. To investigate the efficiency and completeness of this data transfer process, a demonstration of data sharing is carried. By evaluating the results from the demonstration, efficiency gaps are identified in the data transferred process. A detailed investigation on the cause of efficiency gaps in data sharing is carried out and incorporated in this paper.:Abstract 1. Introduction 2. Building Energy Simulation 2.1. Categorization of Energy Simulation Models 3. Data Sharing Requirements - IFC 4. Data Sharing Demonstration 4.1. BIM model 4.2. Data investigation with model viewer 4.3. Data quality verification in energy simulation model 4.3.1. Evaluation of Results 5. Conclusion References

Les Réseaux de capteurs sans fil (WSN) sont une technologie émergente avec des applications potentielles dans divers domaines de la vie quotidienne, tels que la surveillance structurelle et environnementale, la médecine, la surveillance militaire, les explorations robotisées, etc. Les nœuds de capteurs doivent fonctionner pendant une longue période avec des batteries capacité limitée, par conséquent le facteur plus important dans les WSN est la consommation d'énergie. Dans cette thèse, nous proposons des techniques d'optimisation algorithmiques dynamiques, et adaptative pour la réduction de l'énergie. Tout d'abord, un modèle énergétique précis est présenté. Ce modèle repose sur des mesures réelles de courant consommé pour différents scénarios qui peuvent se produire lors de la communication entre les nœud. Il en est conclu que la couche MAC joue un rôle essentiel dans la réduction de l'énergie consommée. Ensuite, un protocole MAC dynamique est présenté. Il adapte de manière dynamique l’intervalle de réveil des nœuds de capteurs à partir d’une estimation du trafic. L’algorithme adaptatif modélisé de façon heuristique pour comprendre le comportement de convergence des paramètres algorithmiques. Le protocole est appliqué sur des réseaux de capteurs corporels et il surclasse les autres protocoles MAC en termes de latence ainsi que de consommation d'énergie ce qui permet donc d'augmenter la durée de vie de trois à six fois. Enfin, une technique basée sur l’optimisation adaptative de la puissance d'émission radio est appliquée sur des canaux variant dans le temps. La puissance de sortie est réglée dynamiquement au meilleur niveau de puissance selon l’état du canal, ce qui diminue la consommation d’un facteur deux. / Wireless Sensor Networks (WSN) are a fast emerging technology with potential applications in various domains of daily-life, such as structural and environmental monitoring, medicine, military surveillance, robotic explorations etc. WSN devices are required to operate for a long time with limited battery capacity, therefore, the most important constraint in WSN is energy consumption. In this thesis, we propose algorithmic-level dynamic and adaptive optimization techniques for energy reduction in WSN. First, an accurate energy model is presented. This model relies on real-time power measurements of various scenarios that can occur during communication between sensor nodes. It is concluded that MAC layer plays a pivotal role for energy reduction. Then, a traffic-aware dynamic MAC protocol is presented which dynamically adapts the wake-up schedule of sensor nodes through traffic estimation. An adaptive algorithm is designed for this purpose that is heuristically modeled to understand the convergence behavior of algorithmic parameters. The proposed protocol is applied to body area networks and it outperforms other low-power MAC protocols in terms of latency as well as energy consumption and consequently increases the lifetime from three to six times. Finally, an SNR-based adaptive transmit power optimization technique is applied under time-varying channels. The output power is dynamically tuned to best power level under slow varying channel, which results in an average gain by two times.

Image segmentation is a critical step in building a computer vision algorithm that is able to distinguish between separate objects in an image scene. Image segmentation is based on two fundamentally intertwined components: pixel comparison and pixel grouping. In the pixel comparison step, pixels are determined to be similar or different from each other. In pixel grouping, those pixels which are similar are grouped together to form meaningful regions which can later be processed. This thesis makes original contributions to both of those areas. First, given a Markov Random Field framework, a Stochastic Nested Aggregation (SNA) framework for pixel and region grouping is presented and thoroughly analyzed using a Potts model. This framework is applicable in general to graph partitioning and discrete estimation problems where pairwise energy models are used. Nested aggregation reduces the computational complexity of stochastic algorithms such as Simulated Annealing to order O(N) while at the same time allowing local deterministic approaches such as Iterated Conditional Modes to escape most local minima in order to become a global deterministic optimization method. SNA is further enhanced by the introduction of a Graduated Models strategy which allows an optimization algorithm to converge to the model via several intermediary models. A well-known special case of Graduated Models is the Highest Confidence First algorithm which merges pixels or regions that give the highest global energy decrease. Finally, SNA allows us to use different models at different levels of coarseness. For coarser levels, a mean-based Potts model is introduced in order to compute region-to-region gradients based on the region mean and not edge gradients. Second, we develop a probabilistic framework based on hypothesis testing in order to achieve color constancy in image segmentation. We develop three new shading invariant semi-metrics based on the Dichromatic Reflection Model. An RGB image is transformed into an R'G'B' highlight invariant space to remove any highlight components, and only the component representing color hue is preserved to remove shading effects. This transformation is applied successfully to one of the proposed distance measures. The probabilistic semi-metrics show similar performance to vector angle on images without saturated highlight pixels; however, for saturated regions, as well as very low intensity pixels, the probabilistic distance measures outperform vector angle. Third, for interferometric Synthetic Aperture Radar image processing we apply the Potts model using SNA to the phase unwrapping problem. We devise a new distance measure for identifying phase discontinuities based on the minimum coherence of two adjacent pixels and their phase difference. As a comparison we use the probabilistic cost function of Carballo as a distance measure for our experiments.

Image segmentation is a critical step in building a computer vision algorithm that is able to distinguish between separate objects in an image scene. Image segmentation is based on two fundamentally intertwined components: pixel comparison and pixel grouping. In the pixel comparison step, pixels are determined to be similar or different from each other. In pixel grouping, those pixels which are similar are grouped together to form meaningful regions which can later be processed. This thesis makes original contributions to both of those areas. First, given a Markov Random Field framework, a Stochastic Nested Aggregation (SNA) framework for pixel and region grouping is presented and thoroughly analyzed using a Potts model. This framework is applicable in general to graph partitioning and discrete estimation problems where pairwise energy models are used. Nested aggregation reduces the computational complexity of stochastic algorithms such as Simulated Annealing to order O(N) while at the same time allowing local deterministic approaches such as Iterated Conditional Modes to escape most local minima in order to become a global deterministic optimization method. SNA is further enhanced by the introduction of a Graduated Models strategy which allows an optimization algorithm to converge to the model via several intermediary models. A well-known special case of Graduated Models is the Highest Confidence First algorithm which merges pixels or regions that give the highest global energy decrease. Finally, SNA allows us to use different models at different levels of coarseness. For coarser levels, a mean-based Potts model is introduced in order to compute region-to-region gradients based on the region mean and not edge gradients. Second, we develop a probabilistic framework based on hypothesis testing in order to achieve color constancy in image segmentation. We develop three new shading invariant semi-metrics based on the Dichromatic Reflection Model. An RGB image is transformed into an R'G'B' highlight invariant space to remove any highlight components, and only the component representing color hue is preserved to remove shading effects. This transformation is applied successfully to one of the proposed distance measures. The probabilistic semi-metrics show similar performance to vector angle on images without saturated highlight pixels; however, for saturated regions, as well as very low intensity pixels, the probabilistic distance measures outperform vector angle. Third, for interferometric Synthetic Aperture Radar image processing we apply the Potts model using SNA to the phase unwrapping problem. We devise a new distance measure for identifying phase discontinuities based on the minimum coherence of two adjacent pixels and their phase difference. As a comparison we use the probabilistic cost function of Carballo as a distance measure for our experiments.