The present work is a contribution towards the integration of building simulation tools in order to better represent the complexity of the real world. It attempts to overcome certain shortfalls of contemporary simulation applications with respect to indoor air flows. As a result, the evaluation of building energy consumption and indoor air quality is expected to be improved. Advanced fluid flow models (as employed within Building Thermal Simulation - BTS - and Computational Fluid Dynamics - CFD) with different degrees of detail were investigated and their modelling deficiencies identified. The CFD technique which defines the fluid flow on a micro scale was integrated into BTS in which fluid flow is described in a larger scale. The resulting combined approach strengthens the modelling potential of each methodology by overcoming their specific deficiencies. BTS's inability to predict air flow property gradients within a single space was surmounted and the difficult of estimating CFD boundary conditions are now supplied by BTS. The conflation approach is expected to be employed where gradients of indoor air flow properties can be considered crucial to the evaluation of thermal comfort and energy consumption. The BTS environment, ESP-r, was elected to perform the current work and a new CFD program, dfs, was specifically developed for the analysis of three-dimensional, turbulent, transient air flow. Finally, the two approaches were integrated. The integration work focuses on the CFD boundary conditions where the interactions of BTS and CFD take place; these occur at the inside zone surfaces and at the zone openings. Three conflation approaches were devised addressing different degrees of complexity and sophistication. The first one, involving the two types of zone boundaries, corresponds to a simple approach where the BTS and CFD systems exchange information without any direct interaction. The second approach consists of three other schemes to handle the thermal coupling at the internal zone surfaces. The third approach comprises coupling between the nodal network approach as employed by the BTS environment, and the continuity and momentum equations in the CFD technique. A validation methodology consisting of analytical validation, intermodel comparison and empirical validation is described and applied. The technique is shown to be adequate for modelling indoor air flows when compared to existing models. Three situations, covering the different types of air flows encountered within buildings are discussed to demonstrate the combined method's applicability when compared with the nodal network approach. Finally, general conclusions are presented and some possible future work is identified showing that the developed methodology is very promising.

Context : Big Data and Cloud Computing nowadays require large amounts of storage that are accessible by many servers. The Energy consumed by these servers as well as that consumed by hosts providing the storage has been growing rapidly over the recent years. There are various approaches to save energy both at the hardware and software level, respectively. In the context of software, this challenge requires identification of new development methodologies that can help reduce the energy footprint of the Distributed Storage System. Until recently, reducing the energy footprint of Distributed Storage Systems is a challenge because there is no new methodology implemented to reduce the energy footprint of the Distributed Storage Systems. To tackle this challenge, we evaluate the energy consumption of Distributed Storage Systems by using a Power Application Programming Interface (PowerAPI) that monitors, in real-time, the energy consumed at the granularity of a system process. Objectives : In this study we investigate the Energy Consumption of distributed storage system. We also attempt to understand the effect on energy consumption for various patters of video streams. Also we have observed different measurement approaches for energy performance. Methods : The method is to use a power measuring software library while a synthetic load generator generates the load i.e., video data streams. The Tool which generates the workload is Standard Performance Evaluation Corporation Solution File Server (SPECsfs 2014) and PowerAPI is the software power monitoring library to evaluate the energy consumption of distributed storage systems of GlusterFS and Compuverde. Results : The mean and median values of power samples in mill watts for Compuverde higher than Gluster. For Compuverde the mean and median values until the load increment of three streams was around a 400 milliwatt value. The values of mean and median for the Gluster system were gradually increasing. Conclusions : The results show Compuverde having a higher consumption of energy than Gluster as it has a higher number of running processes that implement additional features that do not exist in Gluster. Also we have concluded that the conpuverde performed better for higher values of Load i.e., video data streams. / <p>Topic : Evaluating Energy Consumption of Distributed Storage Systems</p><p>Advisor: Dr. Dragos Ilie, Senior Lecturer, BTH</p><p>External Advisor: Stefan Bernbo,CEO, Compuverde AB</p><p>Student: Samuel Sushanth Kolli</p><p>The report gives a clear description of Distributed Storage Sytems and their Energy consumption with Performance Evaluation.</p><p>The report also includes the complete description and working of SpecSFS 2014 and PowerAPI Tool.</p> / Performance Evaluation of Distributed Storage Systems

Certain features of dynamic models of energy demand based on the economic theory of production are examined. Attention is mainly confined to first and third generation dynamic models. First generation models are partial adjustment models where energy Is treated largely in isolation from other inputs. Third generation models are based explicitly on dynamic economic optimization, incorporating the notion of costs of adjustment for quasi-fixed inputs. The analysis focusses on three main issues. The first is the characteristics of first generation models and how these models can be extended to the industrial sector. The second is the nature and empirical significance of alternative definitions of third generation models according to whether adjustment costs are treated as a function of net or gross investment. The third issue is whether first and third generation models are generically related and, if so, how. The work on first generation models is mainly confined to the Balestra-Nerlove framework. This specification generally has been applied to individual fuels in the residential/commercial sector but is also applied to aggregate residentidal energy demand and to industrial fuel demand. In terms of third generation models, recognition that adjustment costs may reflect not only net but also gross investment leads to a more complex model, with significant differences in the theoretical specification between net and gross investment formulations. These differences remain significant empirically. Application of the net and gross investment model specifications to Canadian data show that estimation of the simpler net investment version may well entail erroneous estimates of certain parameters. The energy functions of the first and third generation models are found to be generically related. The simplicity of a first generation model can under certain conditions be consistent with the richer, more complex framework of third generation models. However, empirical testing did not support the notion of treating a first generation energy function as tantamount to a reduced form specification of a third generation model.

Approved for public release; distribution is unlimited / The purpose of this thesis is to provide an overview of Germany's energy supply options until 2020, the political implications and the respective consequences for Germany's foreign energy policy. The oil and gas supply situation for Germany will become more complex in the upcoming decade. Since oil imports from the UK and Syria are expected to cease after 2005, 18% of the current oil supply will have to be substituted within this decade. Russia may not be available to provide the amount necessary. The gas situation is somewhat less urgent, as a supply shift will have to take place only after 2010, when the Norwegian and Dutch gas reserves cease to satisfy the export demand. The only regions that will be able to provide oil and gas on a global level to meet the growing world demand will be the Middle East, Russia and other Caspian Sea neighbors. Germany's welfare is directly dependent on its economical success. As a highly industrialized country, Germany should take a tremendous interest not only in the future development of the international energy market, but also in attempting to influence the development immediately following that of its domestic needs. / Lieutenant Commander, German Navy

Since the end of the Second World war, United Kingdom energy policy has been highly influenced by beliefs about the adequacy of indigeneous resources, security of supply and macro-economic objectives. Over the same period we observe a growing sophistication in the official projections of energy requirements both in terms of content and technique. An analysis of the methods and models used in the calculation of recent official projections of UK energy demand identifies a number of problems. These mainly concern the size of the price elasticity of energy demand employed in the sectoral models and the modelling of interfuel substitution particularly in the Domestic Sector market for space and water heating. A review of econometric studies of UK demand indicates that the price elasticities of energy demand employed in the Department of Energy models of sectoral energy demand are generally lower than the estimates reported elsewhere. In contrast, official estimates of the own price and cross price elasticities for individual fuels in the Other Industry Sector are broadly consistent with other econometric studies. However the body of work carried out in this area is found to have a number of shortcomings: (i) in general, the problem of autocorrelation has been dealt with inadequately and inappropriately, (ii) it has not been possible to distinguish long run and short run effects satisfactorily, (iii) it has not been possible to distinguish the independent influences of income and price satisfactorily, (iv) models of interfuel substitution have generally performed poorly in the Domestic Sector and (v) the impact of biased disembodied technical change has not been considered in most studies of interfuel substitution. A new study of energy demand in the Industrial and Domestic Sectors of the UK economy attempts to address these problems. The results are mixed. Well established techniques are used to detect and adjust the results for first order autocorrelation. Biases of technical change are estimated and in particular the Domestic Sector interfuel substitution model performs well. However the problem of modelling the dynamic structure of energy demand remains unresolved. In the Industrial Sector, cross section and time series data is pooled in order to distinguish the independent influences of output and price. While the resulting price and output elasticities conform to the values that we would expect a priori, the explanatory power of the model is low. In the Domestic Sector it is possible to develop a model of energy demand with a good predictive performance, however simulation experiments with the model show that the high frequency variation of the dependent variable is mostly explained by the exogeneous temperature variable which itself is difficult to predict. In contrast it is found that relatively simple relationships between overall primary energy demand and key economic and demographic variables can have surprisingly good forecasting properties. This probably arises because of the inertia of systems at this level of aggregation. A severe test is constructed for a number of single equation forecasting models. The parameters of the models are estimated on UK time series data from 1954 to 1973 then the results are used to generate forecasts of the dependent variables over the highly unstable period between 1974 and 1980. Several of the models provide very accurate predictions. The main purpose of the thesis is to assess recent official forecasts of UK energy demand. The single equation model which performs best in the forecasting test is used as a basis for generating projections to the year 2000. The results are compared with the latest official projections. It is found that the official forecasts imply a lower price elasticity than used in the single equation model, a similar relationship between the structural composition of GDP and aggregate energy demand while the implicit income elasticity is higher than our estimate under high fossil fuel price assumptions but lower when fossil fuel prices are assumed to be lower. The simple model yields very similar forecasts to the official projections for 2000 with the exception of one case. With regard to projections of final energy consumption in the year 2000, there are some areas of contention between the bulk of econometric evidence and the implications of the official forecasts but they are small. Most aspects of the fuel mix forecasts are broadly supported by the econometric evidence given the relative fuel price assumptions. It is concluded that the latest official projections represent a fairly realistic appraisal of the likely outcome of an interplay of market forces in the energy market.

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The Hot Spot effect is an issue that reduces the network lifetime considerably. The network on the field forms a tree structure in which the sink represents the root and the furthest nodes in the perimeter represent the leaves. Each node collects information from the environment and transmits data packets to a "reachable" node towards the sink in a multi-hop fashion. The closest nodes to the sink not only transmit their own packets but also the packets that they receive from "lower" nodes and therefore exhaust their energy reserves and die faster than the rest of the network sensors. We propose a technique to allow the data sink to identify nodes severely suffering from the Hot Spot effect and to move beyond these nodes. We will explore the best trajectory that the data sink should follow. Performance results are presented to support our claim of superiority for our scheme.

Student Number : 0420494F - MSc research report - School of Geography, Archaeology and Environmental Studies - Faculty of Science / Government reforms aim to provide free basic and affordable electricity in poor urban areas. However, the growing rate of urban poverty, limited development of energy infrastructure, the cost of modern services and fuel use culture further contribute towards the consumption of multiple fuel use. The diverse ways in which urban households utilise the available energy can make fuel transition very difficult to achieve. This research aims to investigate household energy consumption patterns in Diepkloof in order to understand the factors limiting a fuel transition from coal and wood to electricity. The analysis in this report uses data collected from the household survey in Diepkloof as well as key stakeholders in this field including the Soweto Electricity Crisis Committee and Eskom. The reason for using these two stakeholders was because SECC is a social movement activist group advocating free electricity while Eskom is the electricity service provider. Household data examines the range of fuels, factors affecting fuel choice and the attitude of people towards alternative energy sources. The two stakeholders help to critically assess the effectiveness of energy policy document supporting the supply of electricity to poor communities, including Diepkloof. This research concluded that there is no clear evidence that an energy transition is taking place in Diepkloof. Moreover, the results indicate that the energy policy has good intentions but there are still some difficulties with affordability, and culture or personal choice. As a consequence of the underlying problem, most poor households still rely on multiple fuels use for major thermal purpose such as cooking and heating.

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